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| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Marius CiepluchandClaude Opus 5
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2589c3d47a |
docs(contributor-scan): what c/memory covers, and what SMT covers instead
libpng's library code never calls strcpy, strcat, sprintf or gets -- all twelve files that do are under contrib/. It overflows through array and pointer arithmetic and memcpy sizing, which is a different question from the one this family asks: "can this expression exceed this allocation" rather than "does tainted data reach this call". That is a constraint problem. It is what SMT is for, and what this project deliberately did not adopt -- the architecture decision was abstract interpretation as arbiter, taken because a model layer can be built from code where execution cannot be assumed. That decision was right for the flow families and it does not reach this one. The maintainer has attacked libpng exactly this way, using SMT to lay out memory and then guiding a fuzzer to the inputs that reach it, which is evidence about the shape of the problem rather than a plan to reimplement it: the honest routes to C memory safety are constraint solving or execution, and the ladder already has a rung for the second. So 2.15 now separates the cheap route (pin a C project whose CVE runs through a string function, giving the family its first true positive) from the expensive one (model the arithmetic, a different engine), instead of implying a sink-table fix would cover both. And 2.16 asks the question this spec's predecessor would have asked: c/memory has scored zero true positives and three separate classes of false one -- a substring match, a bound it could not see, an allocation sized to fit. Every one was a real engine defect and every one is fixed, but the family has never been right. Keep it scoped and labelled, or retire it; either way with a number behind it, and never leaving a silent C scan to read as a clean bill of health. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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c1191b3a6b |
feat(model): an access-control finding names a line, and what the PDG does not give us
contributor-scan 2.7. Taint and config verdicts have always carried a `path:line:function` location; dominance never did, so every access-control finding reached a contributor as `api_views/users.py:?:update_password` -- the right file by accident, because the region was asked about that file, and no line at all. The query now reports the operation's file and the verdict carries the location, with an engine that cannot report one getting no location rather than an invented one. Two questions answered by measurement while doing it. **Does this factor in the PDG?** The data half, centrally: `reachableByFlows` is the entire taint arbiter and `joern-parse` applies the `dataflowOss` overlay. The control half, not at all -- and it is not available to us. Measured: 0 of 13,184 calls in the libpng CPG and 0 of 1,152 in the vampi CPG have a controlling control structure, so `controlledBy` returns nothing in either. Everything guard-shaped therefore runs on CFG dominance or on syntactic conditions, which is exactly why 2.12's first attempt failed: libpng's bound reaches its sink through an `error` flag, and that is a control-dependence fact and nothing else. Recorded as 2.14, including the possibility that it cannot be enabled -- which would bound what guard reasoning this engine can ever do. **Would checking out a vulnerable libpng find a known CVE?** No, and not because of the version. Every one of the twelve files using `strcpy`/`strcat`/`sprintf`/`gets` is under `contrib/`; the library itself uses none of them. libpng overflows through array and pointer arithmetic and `memcpy` sizing, and five of its buffer-overflow fixes touch no modelled sink at all. Pinning an older commit would produce the same silence for the same reason. That leaves two routes of very different size, recorded as 2.15: pin a C project whose CVE genuinely runs through a string function, which is cheap and would give the memory family its first true positive; or model how C actually overflows, which is size arithmetic rather than sink matching and should not be started by accident. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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9221ba8e40 |
feat(model): a bound on the data lowers the rung, it does not clear it
contributor-scan 2.12. c/injection and c/memory carried zero sanitizers, so no C flow could ever be discharged: every `argv -> strcpy` entailed however carefully it was bounded. Which sinks can be discharged this way is read from the sink fact's own CWE rather than declared: CWE-121 is a stack buffer overflow, so gets/sprintf/strcat/strcpy qualify. It is the only overflow CWE in the fact tables and it is C-only, so no Python or JavaScript sink is touched and the pair corpus is unaffected -- gates byte-identical. Not a sanitizer list, because `strlen` appears in the guarded and the unguarded case alike; the discharge is a guard, not a call. **The first implementation required the bound to DOMINATE the sink, and it found nothing.** libpng guards `strcpy(outname+len, ".png")` with `(len = strlen(inname)) > 250`, but that check sits on an else-if chain whose sibling branch skips it entirely, and the protection reaches the sink through an `error` flag: `++error` in the guarded branch, `if (!error)` around the copy. Joern's dominator set for that sink has 89 calls and correctly excludes the bound; `controlledBy` returns none either. Verified against the CPG before the rule was changed rather than after it failed. So the bound is searched in the method, and the consequence is corroborated rather than silence. That is the honest rung: a bound on this variable means the graph has NOT shown the copy is unguarded, and it has not shown it is guarded either -- off-by-one being how bounds fail. The judge is asked. Proving the flag-mediated case needs path-sensitive reasoning this arbiter does not do. libpng entailed 3 -> 1 The survivor is also a false positive: `strcpy(output + len, ".icc")` in iccfrompng.c, where `output = malloc(len + 5)` makes it fit exactly. That is a different discharge shape -- allocated to size, not bounds-checked -- and it is recorded as 2.13 rather than chased. Both shapes were found on one repository, and extending a rule until that repository is clean is the overfitting Req 8 exists to prevent; 2.13 requires a second C checkout with a genuine CWE-121 so the change can be measured for what it silences as well as what it clears. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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6dc25d77e4 |
feat(model): let the project say what it ships, and rank that first
contributor-scan 2.10. Every one of libpng's twenty-five entry-point regions was a `main()` -- twenty-three under contrib/, plus example.c and pngtest.c -- and those regions carried the fifty interprocedural taint requests that dominated a 300s query phase. A library has no entry points, so entry-point-driven analysis had nothing to anchor to except its own example programs. The task said to derive this rather than hardcode `contrib/`, and a project already declares it. Makefile.am names installed targets in `lib_LTLIBRARIES` and `bin_PROGRAMS`, each target's `<target>_SOURCES` names its files, and `check_PROGRAMS`/`noinst_PROGRAMS` are automake's own word for "not installed" -- exactly the distinction wanted. Taking every `_SOURCES` would have called libpng's whole test suite shipped code, since contrib/libtests declares its sources the same way the library does. interprocedural regions 25 -> 1 taint query 300.1s -> 125.0s total query 311.9s -> 138.5s regions 121 -> 121, unchanged by design The surviving interprocedural region is contrib/tools/pngfix.c:main, which libpng genuinely installs. Two rules keep it honest. **Silence is not exclusion**: a project with no build file this can read gets None back and nothing changes, so vampi's numbers are untouched. **Not shipped is not unscanned**: those regions are still arbitrated function-locally, because a contributor editing a test must still get an answer -- what they lose is the expensive treatment and first claim on the budget. Entailed findings went 0 -> 3, and all three are false positives at contrib/gregbook/wpng.c:374, `strcpy(outname+len, ".png")`. It is safe: outname is char[256] and line 325 guards with `if ((len = strlen(inname)) > 250)`. They were not introduced so much as uncovered -- a different callDepth meant a different sink set, and the 26 fgets false positives were masking them. The structural cause is recorded as 2.12: c/injection and c/memory carry ZERO sanitizers, so no C flow can ever be discharged and every `argv -> strcpy` entails however carefully it is bounded. The discharge here is not a call but a guard that dominates the sink, which is precisely what dominance.sc already does for access control and is wired to that one family. Also adds Group 7, five tasks for stopping the same false positive twice. Two of today's three false-positive classes were one bug in three matchers -- `resolve` in `resolveUrl`, `user` in `users`, `gets` in `fgets` -- each fixed locally and each next one surviving because nobody carried the fix across. A model may group counter-examples by cause and draft an edit; only a split-respecting measurement may adopt one, because the predecessor's improve lever admitted shapes taught by the holdout pairs they then recovered and turned a -0.059 Youden into +0.059. Gates byte-identical. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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67497c4d68 |
fix(cpg): a sink is matched as a word, not as a substring
`fgets` contains `gets`. The sink matcher's last clause was an unbounded
`methodFullName.contains(n)`, so every bounded read in libpng matched the
sink for the unbounded one. All 26 entailed findings on that repository
were the same false positive:
*argv -> fgets(pnmline, 256, wpng_info.infile) (line 335, 8 steps)
which is the safe API being reported as the dangerous one -- and `fgets`
is in the fact tables as a SOURCE, never a sink. Verified against the CPG:
methodFullName is exactly "fgets", and .contains("gets") is true.
Third instance of one bug class in this codebase, after the sanitizer that
matched `resolve` inside `resolveUrl` and the discharger that matched
`user` inside `users`. The sink matcher had never been given the same
treatment. All four of its clauses now run through the shared bounded
matcher, and that matcher is the punctuation-tolerant version written for
task 2.6 -- so `input(`, which an unconditional lookahead had made
unmatchable in `input(x)`, works for the first time as well.
libpng entailed 26 -> 0
Zero is the right answer for a PNG library whose only known CVE is out of
family. Gates byte-identical, so no pair outcome depended on the substring
match.
What remains on that scan is recorded, not claimed: 113 `c-unsafe-*` rows
assert an API is present rather than misused, and 50 overlay-coverage rows
are annotations rather than findings. 82 of the 113 are under `contrib/`.
Task 2.11 covers whether that band is worth putting in front of a
contributor at all; 2.10 already covers not scanning example code.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
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68b4e551f3 |
feat(model): the envelope checkout, and no to Joern server mode
contributor-scan 2.2 for libpng and the 2.3 decision it exists to inform. 89,082 lines of C across 93 files, under a 2GB heap cap: CPG build 16.1s (25s cold) Joern queries 250.0s arbitration 122.0s (105 calls, $0.0122) whole scan 446s peak RSS 911MB regions 121, all arbitrated, 32 unasked CVE-2025-66293 out_of_scope, as the recipe declares Memory is not the constraint a fleet runner would hit: 911MB leaves room for the rest of the job on a 2-vCPU/7GB runner. The memory pressure seen while building this was concurrent JVMs on one desktop, not repository size. **Server mode: no.** The query phase splits taint 243.3s / config 10.7s over two invocations, and fixed overhead measured on the same CPG is ~13s per invocation. So ~26s of the 250s is JVM startup and ~224s is CPGQL evaluation: a warm server removes 6% of the scan and none of the part that costs. The driver now records query seconds per kind, because a single total cannot tell startup from evaluation and the two answer this oppositely. The small repository pointed the other way. vampi's 34.3s over three kinds is roughly three startups and almost no evaluation, which made a warm JVM look worth having; the ratio inverts with size. That inversion is why the task was sequenced after a real repository rather than decided on a 520-line file. On whether one whole-repository CPG is the only shape for a horizontal fleet: no, and it is also not where the pressure is. The build parallelises and costs 16s; the query does not shard and costs 250s, so splitting a repository across runners divides the cheap phase -- and after 2.4 a shard boundary hides exactly the cross-module flows that matter. Scoping to a contributor's diff cuts the expensive phase instead. 911MB for 89k lines extrapolates to roughly 5GB at a million lines, which would exceed a standard runner, so the ceiling is real and further out than this checkout can locate. Two defects recorded rather than fixed, both invisible at vampi scale: 2.9, all 26 entailed findings are the same site reported 26 times, because regions that share a sink each emit their own; and 2.10, 22 of the 30 highest-ranked regions are libpng's `contrib/` example programs rather than the library. Also ignores Joern's `workspace/`, which a maintainer querying a CPG by hand from the repository root will create. Gates byte-identical. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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d15985f695 |
fix(model): a guard discharges the obligation it is a guard of
contributor-scan 2.6. Access control entailed 7 of 9 VAmPI handlers,
including three that do authorize and three the OpenAPI spec declares
public. Five separate defects, each invisible to a corpus of one-function
excerpts and each found by asking the query what it actually matched.
**A trailing segment is not a match.** `query.get` was matched by its last
segment, so every `.get(` in the repository became a protected database
operation -- `request.headers.get('Authorization')` and
`request_data.get('username')` included. Four of the seven false positives
came from this, and so did the apparent detection of the known BOLA: it was
entailed because fetching the Authorization header had no dominating guard,
not because of the defect. A qualified token now requires its qualifier.
**Configuration is not authorization.** dominance_specs took every
obligation fact. `security_setting` operations are discharged by a VALUE,
which config_specs already claims them for; pooled in here they made
`app.run(...)` an operation requiring an authorization guard and put
thirteen tokens in BOTH lists -- an obligation that is its own discharge,
the same shape as the taint sink that used to sanitize itself.
**Input validation is not authorization.** `jsonschema.validate` was
counted as a guard, so handlers with no authorization check at all read as
guarded. No operation in the table requires validated_input.
**A guard discharges the obligation it is a guard of.** The largest one.
`orm-read` requires an identity_constraint or an ownership_check;
`token_validator` is a path_guard. Flattened into one list, authenticating
the caller discharged an object-level obligation -- a handler that
authenticates and then looks the record up by a PATH parameter read as
fully guarded. The facts state that relation and the spec was discarding
it; it is now carried and the engine asks per operation.
**A word boundary needs a word.** Found while fixing the above. Guard
matching wrapped every token in `(?<![A-Za-z0-9_])...(?![A-Za-z0-9_])`.
The identity token `['sub']` begins with `[`, and the character before it
in `resp['sub']` is `p` -- so the assertion could never hold. Every
identity token starting with punctuation was unmatchable, and the identity
constraint that separates a correct lookup from an IDOR was invisible.
Also: dominance rows were repository-wide, so the model's unguarded
`delete_user` was attributed to the API handler of the same name. Rows are
scoped to the region's file, which is what the rung's own justification
says -- the evidence is a file contradicting itself.
Measured against VAmPI's OpenAPI `security` blocks, which declare per
operation whether auth is required, and agree with the code:
vampi precision 28.6% (2/7) -> 50.0% (2/4)
known found 3 of 3, all model_entailed
And on the labelled access-control pairs, which nobody tuned this against:
all 9 pairs pair_correct 3 -> 3, leak 44.4% -> 11.1%
7 non-VAmPI pair_correct 2 -> 2, leak 42.9% -> 0.0%
Every detection lost was on a pair whose FIXED side was flagged too, which
scores nothing. No correct detection was traded for the precision.
get_by_title was flagged before it was in the recipe: its code carries the
comment "# Broken Object Level Authorization" and the README lists it, so a
real finding was being scored as a false positive. It is now the recipe's
third known vulnerability.
Both remaining false positives are endpoints the spec declares public.
Recorded as 2.8, with the reason the mapper's access_level cannot simply be
trusted: for decorator frameworks "public" means "no decorator found",
which is the bug itself.
Gates byte-identical.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
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0fcd5feade |
feat(model): a region for the code that is in no function
contributor-scan 2.5. VAmPI's `host='0.0.0.0'` is now reported exactly once, at app.py:17:<module>. Before task 2.2 it was reported eight times in eight files that do not contain it; after 2.2 it was reported nowhere. Two causes, one shape: nothing could ask about module-level code. **The main-guard marker is not a function.** `__main__` is what the mapper emits for `if __name__ == '__main__':`. Sent to the engine as a function name it matched nothing, because a CPG calls a file's top-level code `<module>` -- verified against the VAmPI CPG rather than assumed. The region model translates the marker, which is the one place that turns what the mapper meant into what the scanner asks. **A function-scoped file had no region for its module body.** Settings, route registration and globals live in no function, and every region's query filters by function name. A file-level region beside the handlers would report each handler's findings a second time, which is why the earlier rule dropped it as "a second full-file pass". A module-scope region PARTITIONS the file instead of overlapping it. That only became cheap after batching: before it, a region was a JVM. Two things this needed to be correct rather than merely present: - A module method spans its whole file, so the taint query's line-range containment made `<module>` the parent of every function in it. Under module scope, membership is direct -- otherwise the module region reports every handler's sinks again, which is the duplication the old rule was avoiding. - A module body is not a handler. The marker-derived region inherited the handler families and entailed a missing authorization check on the main guard: a finding with no possible remediation. Caught by the first repository run that had the region, and now covered by a test. bind address 8 wrong files -> 0 -> 1, at app.py:17:<module> regions 22 -> 25 entailed 8 -> 9 query time 37.4s -> 41.3s peak RSS 827MB -> 823MB cost $0.0067 -> $0.0068 Three more regions for four seconds and no more model calls: the budget is a total and was already spent, so regions_unasked rose 17 -> 20 -- those regions got the free pass and are reported as unasked, not as clean. `<module>` is Joern's Python name for a module body; JavaScript uses `:program`, so this is verified for Python only. Gates byte-identical. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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ca7b5ba370 |
feat(model): a region that spans the flow, not just a function
contributor-scan 2.4, closing the no-go task 2.2 recorded. VAmPI's SQL injection is a request parameter reaching `get_by_username` in api_views/users.py, passed to `User.get_user` in models/user_model.py, interpolated into a query string and executed there. Source and sink are in different functions and different modules. Regions were per-function, so neither region saw both ends and the engine was silent on the most ordinary shape a real injection bug has. `reachableByFlows` was never the limitation -- it follows calls already. The limitation was the QUESTION. Sinks were filtered to the labeled function's own body, so a crossing flow had no sink to end at. Three changes, each with a reason it is sound rather than convenient: **An entry point is the trust boundary.** parameter_sources was off for repository scans because a repository's parameters are not all attacker input. An entry point's are, by definition -- that is what makes it an entry point. The driver turns it on only for regions the mapper found and named, and leaves it off for walker fallbacks and for named functions that are not entry points. **Sinks may sit below the handler.** taint.sc admits sinks in methods reachable from the labeled method through the call graph, bounded at ENTRY_POINT_CALL_DEPTH = 3: handler -> service -> data access, which is where these bugs live. Unbounded would make the question about most of the repository rather than about this entry point, and make it unaffordable. **The finding names the sink, not the asker.** A crossing flow ends in another file and another function, so Verdict carries the evidence's own `path:line:function` and the site uses it. Reporting `models/user_model.py:73:get_by_username` would name a place that does not exist: line 73 is inside get_user. Also: labeled methods are now resolved by name AND file. `get_user` exists in more than one module of a real repository, which a corpus of one-file excerpts could never surface. Measured on the same checkout and budget as 2.2: VAmPI-SQLI missed -> model_entailed at models/user_model.py:73:get_user known found 0 of 2 before 2.2's fixes, 1 of 2 in 2.2, 2 of 2 now entailed 7 -> 8 query time 40.9s -> 37.4s peak RSS 869MB -> 827MB cost $0.0067, unchanged Asking the bigger question cost nothing measurable here: file scoping and call-graph following pull in opposite directions and roughly cancel at this size. That is one 520-line repository, and VAmPI is now the repository this change was developed against -- the exact overfitting shape Req 8 exists to catch. The depth bound and the entry-point rule mean nothing general until a checkout nobody tuned against says so. Gates byte-identical. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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177c7f3719 |
feat(model): first repository measurement, and the four things it found
contributor-scan 2.2. VAmPI at a pinned commit, scanned whole rather than as excerpts, with benchmarks/repos/measure.py recording build time, query time, arbitration time, peak memory, cost, unjudged regions, and whether each known vulnerability was found and at which rung. The corpus could not have found any of this. Every defect below needs a repository to become visible, and three of them were invisible behind a passing test suite. **The batch path was unreachable.** query_batch was implemented, tested and committed while CpgResult exposed only `run`, so the driver's capability check found nothing and fell back to one JVM per region-family: the first attempt at this measurement ran eighteen minutes on 520 lines, issuing `--param function=` one at a time. Every test called query_batch directly, so none of them noticed. CpgResult now carries run_batch and a test asserts the capability is reachable through the object the driver is handed. The earlier 275s->106s improvement attributed to batching came from region deduplication alone. **The budget stopped free work.** It bounds model calls, but was enforced by breaking the region loop -- spending a paid limit to stop unpaid CPG arbitration. 6 of 22 regions were examined and VAmPI's BOLA, which the graph entails for nothing, was never reached. It now withholds the judge only. 22 of 22 regions arbitrated, 17 counted as regions_unasked (silent because unaffordable is not silent because decided), and the BOLA is entailed by the model layer. **A region with no function asked about the whole repository.** Sending function="" matched every hit in the tree and the driver attributed that answer to whoever asked: one `host='0.0.0.0'` in app.py became eight identical entailed findings in eight files that do not contain it. Queries now carry the region's file. Entailed findings 14 -> 7, query time 70.9s -> 40.9s. The single-request form stays unscoped so the committed pair measurements remain reproducible. **The JVM had no heap bound.** Joern claimed a quarter of physical RAM and runs were killed under memory pressure at different points, so the numbers described the host rather than the engine. -J-Xmx2048m by default, OPENULTRASAST_CPG_HEAP_MB to raise it. Also: arbitrated findings carried `function:line` where the report layer read `path:line`, so every entailed finding reached a contributor with a function name where its file belonged; and the witness line was parsed by splitting on a comma, which produced sites like `config.py:17): permissive literal '0.0.0.0':?`. Reading: GO on one CPG per repository -- 6.2s to build, 177s end to end, 869MB, $0.0067, nothing infeasible. NO-GO on region-scoped taint as the only shape: VAmPI's SQL injection runs from a handler parameter into a function in another module, and a per-function region sees neither end. That is the ordinary shape of a real injection bug, so it is recorded as task 2.4 along with three smaller defects this run exposed. Gates byte-identical. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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8c387306be |
feat(repos): pinned known-vulnerable checkouts, offline by default
The pair corpus scores excerpts: a vulnerable function and its fixed twin, handed to the engine with the answer already narrowed down. Nothing in it exercises a repository -- region ranking, the budget, one CPG over thousands of files, or whether a verdict that holds on fifteen lines still holds when the source, the sink and the guard live in three modules. benchmarks/repos/<name>.toml names a real checkout: url, a full 40-char commit SHA, licence, and every known vulnerability with its file, function and line. `ousast repos` lists them; `--fetch` materialises one; CI does neither, and conftest clears the switch and redirects the cache so a developer with warm checkouts cannot read them by accident. Three rules the loader enforces rather than documents: - A branch or tag is rejected. Either would make the same recipe mean different code next month while the baseline it produced kept the old numbers. - `in_scope` must be stated per vulnerability, never defaulted. It decides whether "not found" is a miss or a non-question, which is the most important thing about the result. - A recipe that declares `detection` must name something in scope to detect. `ousast repos` does not trust a recipe either: against a materialised checkout it checks HEAD against the pin, that the licence file is there, and that each declared function appears above its declared line. That earned its place immediately -- the first libpng recipe named LICENSE.md, which is the filename at HEAD today and not at the pinned commit, where it is still LICENSE. Two checkouts, verified against the real repositories: - vampi (MIT, 520 lines) is the DETECTION checkout. Excerpts of it are already in the pair corpus, including the update_password miss diagnosed earlier, so a whole-repository run says directly whether a verdict survives the move off the excerpt. Its routes register through an OpenAPI operationId, so it exercises registration-based entry-point mapping rather than the decorator path. - libpng (libpng-2.0, 91k lines of C) is the ENVELOPE checkout, pinned at the parent of the commit that added the missing clamp. Its CVE is marked out of scope: an out-of-bounds read is not a family this engine arbitrates, and recording "not found" against it as a miss is the easiest way to make a measurement lie. Nothing is vendored; the recipe records the licence so an artifact can say what it ran against. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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c35426e186 |
perf(model): one Joern invocation per query kind, not one per region
JVM startup dominated a scan: a call per region per family put a ten-line file at 275s over three regions, and would have put a thousand regions at roughly fifty hours. The queries themselves are milliseconds once the CPG is loaded, so the cost was almost entirely process starts. scan_repository() is now collect -> batch -> arbitrate. The three .sc scripts accept a `requests` JSON map and answer all of them in one run, keeping the single-request form so the committed measurements stay reproducible byte-for-byte. Arbiters are untouched: each is handed a CpgResult whose `run` returns its own prefetched rows. The batcher builds parameters by calling request_params() extracted from taint.py, dominance.py and config_value.py rather than reconstructing them. A second copy that drifts from the arbiter's is the exact shape of the five family-assumption bugs the predecessor spec spent a session finding; there is now one definition per family and the batcher borrows it. Regions are also deduplicated by (path, function), collapsing the nameless entry-point rows that were producing `model:injection:?:10`. Measured on the same ten-line file: 275s -> 106s, three duplicate findings -> one `model:injection:ping:10`, rung unchanged at model_entailed. Gates byte-identical. Server mode is recorded as task 2.3, deliberately sequenced after the repository measurement: whether the remaining ~100s floor is worth a process model, a weakened subprocess seam and a Scala-evaluating HTTP endpoint depends on a build-time-to-query-time ratio only 2.2 produces. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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e67f3ee2f0 |
feat(contributor-scan): 1.3 the model layer reaches a user
`model-grounded-detection` finished 16/16 with the model layer having zero
production callers. This closes that. A real scan of a Flask file now reports:
by_rung: {"model_entailed": 3, "model_corroborated": 0, "suspicion": 0}
model:injection:ping:10 <- os.system("ping -c 1 " + request.args["host"])
The stage is the fifth MAP proposer, mirroring _check_obligations, and is
additive: pattern, overlay and obligation findings all keep working, and without
a CPG engine it records cpg_unavailable and changes nothing else. Two scan
integrity tests pin the exact degradation set and both broke -- correctly. Their
`requested == baseline` assertion, that findings are byte-identical, still
passed, so the only change is that the scan now SAYS the arbiter was absent
instead of being silently without one. The guards are updated to assert the
model degradation specifically rather than being loosened.
ModelLayerConfig, not ModelConfig: that name is already the [models] block, and
the type checker caught the collision immediately. Two meanings of one name is
how a scan silently reads the wrong settings.
Every payload carries the same shape, skipped ones included. A payload that is
sometimes a full record and sometimes a single "skipped" key is how a report
ends up quietly omitting a section.
unjudged_sample names the lowest-ranked regions the budget did not reach.
Counting them is the honesty requirement; naming them is what lets a maintainer
write a large-repository exclusion list from evidence rather than guesswork.
The run also produced the finding behind new task 1.4: 275 seconds for a
ten-line file. cost_usd is 0.0, so those 21 invocations were not model calls --
they were Joern query launches, one per region per family, each a JVM start.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
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8663c1a5b3 |
feat(contributor-scan): 1.1 regions from entry points, 1.2 the repository driver
Pair scoring could rely on a labelled function; a contributor cannot supply one -- which code is risky is the question they are asking. Regions now come from entry points, with a file-level fallback that says it is one, and each carries only the families its shape and language admit. Ranking uses what EntryPointRecord already decided (access_level, trust_boundary) rather than a second opinion computed from the same evidence. A family is offered only where its arbiter could answer: access_control needs a handler, so a library file with no entry point is not asked about obligations it has no siblings to be inconsistent with. The driver makes the two costs that were free at pair scale explicit. ONE CPG per repository, reused across regions -- a build is 5-30s on a forty-line excerpt and per-region builds would make this unusable. And a budget that is a TOTAL for the run rather than the enumerator's per-region cap of eight, spent highest-rank-first, with whatever it does not reach counted into regions_unjudged. A tool that silently analyses a tenth of a repository and reports a clean result is worse than one that says what it skipped. Two flaws the tests caught, both mine and both about ownership: the budget counted calls by reading a client's own usage log, so it silently never tripped for a client that keeps none -- the driver now counts its own spend through a wrapper; and the driver trusted its caller to have sorted, when the order the budget is spent in belongs to whoever holds the budget. The third was worse and only surfaced because a test failed for the right reason: the driver passed candidates=() and never enumerated, so a repository scan would have reported only what the arbiters establish unaided and never populated the suspicion band -- which supplied 15 of 26 findings in the pair measurement. It enumerates now. Sibling audit (Req 8.5) done before writing: family routing lives in exactly one production place, pipeline._arbitrate, dispatching on spec type. regions_for returns family names and the driver resolves specs, so that stays true. 750 passing, mypy clean over 96 files, gates byte-identical, zero orphans. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
64a27c4e30 |
docs(model-grounded-detection): record what 16/16 does and does not mean
Asked whether the prior spec is finished. Audited rather than counted, and the answer is: all 16 tasks are complete as written, and not all requirements are satisfied in the product. Four are built but unreachable, so the requirement is not met where it matters: model/report.py implements the per-slice and overfitting-gap discipline (Req 11.1) and has no production caller; resolve_execution_backend returns NullExecutionBackend on BOTH branches so the execution_confirmed rung (Req 10.1) can never be reached; harvest_recipe (Req 9.4) is a seam with nothing plugged into it; and the pipeline that measures 90.0% pair-correct is called only by a measurement harness, never by ousast scan. That is a spec defect rather than an execution one. No task ever said "make this reachable", and Req 11.2 -- the gates stay byte-identical -- is satisfied PRECISELY BECAUSE nothing is wired. I read that as reassurance for a whole session when it was evidence. The spec is not reopened; it remains the complete record of building and measuring the arbiter. contributor-scan takes all four: its Req 1 closes 8.x and 11.1 in the product, Req 7.1 forces the memory decision 10.1 defers, and Req 9 covers the harvest 9.4 left as a seam. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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4e4681f162 |
docs(contributor-scan): design and tasks -- wire it, then measure on a real repo
Requirements approved, design and tasks generated. 47 criteria, all traced; 13 tasks in 6 groups. The design is deliberately conservative. The model layer joins as ONE STAGE INSIDE MAP, beside the obligations checker it most resembles -- which already takes entry points, produces findings and merges them. Findings are added, never substituted; the pattern and overlay paths keep working; a machine without Joern scans exactly as it does today. Nothing here is allowed to make the tool worse for someone who does not have the engine. Two decisions carry the most weight. One CPG per scan rather than per region: a build is 5-30s on an excerpt and is the dominant cost at repository scale, so per-region builds would make this unusable. And one total budget rather than the enumerator's per-region cap of eight, which is meaningless across thousands of regions; the budget is spent highest-rank-first and whatever it does not reach is counted and reported, never silently truncated. Task 2.2 is a go/no-go and comes before shipping: the first scan of a pinned known-vulnerable checkout is a measurement nobody has taken, and if one CPG per repository proves infeasible the region and budget design changes shape. Better to learn that before groups 3-6 are built on it. Group 5 (corpus) is last on purpose. The binding constraint today is reachability, not corpus size -- 198 of 238 OWASP pairs went unvendored because volume without readability makes every number worse. The task preamble carries the predecessor's two hard-won habits: audit sibling paths in the same commit as any routing or matching change (five bugs shared that shape, each caught by a measurement rather than a check), and run the whole CI list rather than the parts one remembers (CI was red for a session because `ruff check` passing was taken as evidence for `ruff format --check`). Not approved for implementation. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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fe45081b43 |
fix(ci): run ruff format, and record run 5 at 90% pair-correct
CI was red on `ruff format --check`: 23 files. I ran `ruff check` after every
change this session and never once ran `ruff format --check`, though CI runs
both. Lint passing is not formatting passing, and I treated one as evidence for
the other for the whole session.
Formatting applied. The full CI sequence now reproduces locally: format, lint,
mypy over 94 files, 735 tests, three gates byte-identical, compileall, and the
semantic job's 16 marked tests.
Also records run 5 of the pipeline measurement, which the vampi fix lifted as
predicted:
hunter (K=5) pipeline (K=1)
pair-correct 76.7% 90.0%
leak 12.3% 6.7%
detection -- 96.7%
cost ~$0.69 $0.0068
27 of 30 pairs. Five runs, each step from a measurement contradicting an
assumption: 73.3 (no pipeline path) -> 73.3 (routing fixed, IDENTICAL, which
exposed the discarded verdict) -> 80.0 (residual question matched to its
arbiter) -> 86.7 (aiohttp settings) -> 90.0 (dominance question no longer
contradicting its own evidence).
One miss left: dsvpwa-dsvpwa-main, whose setting is an argparse --ssl default
rather than a security-setting call. Out of reach before the settings work and
still is; contorting the arbiter around one pair would be worse than the miss.
The caveat is recorded beside the number: 30 pairs of one slice, and the slice
iterated against all session. The +16% overfitting gap against vfc-js is why
contributor-scan Req 8.1/8.2 exist.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
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14ca0a96ce |
docs(contributor-scan): requirements, including the corpus asks I first missed
10 requirements, 46 criteria. A NEW spec rather than an amendment to model-grounded-detection, which is complete and closed. Req 1-8 cover the gap that prompted it: the model layer has zero production callers, so ousast scan must use it (1), regions must come from entry points rather than a labelled function (2), ranking and budget must work at repository scale where the enumerator's per-region cap of eight is meaningless (3), the performance envelope must be measured on a real checkout (4), output must be actionable (5), packaging must be built and verified rather than merely defined (6), the two deferred decisions must close (7), and the honesty disciplines carry forward (8). Req 9 and 10 were added after the maintainer asked whether their requests had been captured. They had not been -- I folded the corpus asks implicitly into "run against a known-vulnerable repository", which is not the same thing. Now explicit: a PHP/WordPress slice (php2cpg works once an interpreter is present, which the image provides, and plugin CVEs are overwhelmingly the families the model arbitrates well); real-world C library CVEs -- libpng, image libraries, OpenSSH, libpam, kernel modules -- gated on the memory decision, because until it closes they would join 173 pairs already idle; more vibe-code pairs, since that slice is where the stated audience works and it is only 35 pairs; and regression tuning over a pinned large-repository baseline, which is the thing pair scoring structurally cannot see. Req 9.4 carries today's lesson: verify on a sample that the model can READ a slice before vendoring it. The OWASP harvest found 238 pairs and vendored 40 for exactly that reason. Not approved. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
41266ce472 |
docs(contributor-scan): brief -- the model layer is complete and unshipped
Asked what the spec state was and whether anything was forgotten. It was, and it is the largest thing outstanding: model-grounded-detection is 16/16 tasks complete and its arbiter measures 86.7% pair-correct at 6.7% leak, but the model layer has ZERO production callers. `ousast scan` still runs preprocess -> map -> overlay -> obligations -> regress -> report and touches none of it. Every number quoted today came from a measurement harness in a scratch directory. That is a scope gap in the predecessor spec rather than a slip in executing it: there was never a task saying "make this reachable", and its Req 11.2 (gates stay byte-identical) is satisfied PRECISELY BECAUSE nothing is wired. The spec is complete and the feature is unshipped, both at once. A new spec rather than an amendment, because the predecessor is coherent and closed -- it built an arbiter and measured it -- while this is a different question: making it something a developer runs on a repository they are about to contribute to. What it covers, in the order it bites: scan integration; whole-repo scanning (entry-point region selection, ranking across thousands of candidates, a budget policy -- the enumerator caps at 8 per region, meaningless at repo scale -- and a performance envelope never measured); running against a known-vulnerable checkout, since the 176 real-world CVE pairs are single-function excerpts that exercise none of it; the deferred memory decision, with 173 of those pairs idle behind a stub tier; and retiring evidence_level. It also records what was built outside any spec: the pipeline itself, the ops work (one image, compose, shell wrappers, pyinfra -- with the Docker image never actually built), and the corpus additions. And it carries the known limits forward rather than losing them: the flat permissive_values list that cannot tell debug=True from httponly=True, interprocedural resolution as the most repeated miss cause, and the five bugs that shared one shape -- a taint-specific assumption applied to every family, each caught by a measurement rather than a check. Not approved. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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adbb824eac |
feat(ops): a sourceable wrapper so docker is nobody's problem
source ops/shell/ousast.sh # bash / zsh
. ops\shell\ousast.ps1 # powershell
ousast scan .
ousast scan src/api
The container becomes an implementation detail. The engine is a JVM and ~2 GB
of Joern, and a contributor evaluating this tool should not have to install
either to try it -- what they get is a command.
Path rewriting is what makes it feel local: arguments under the working
directory are mapped to their mounted equivalents, so `src/api` means what the
user expects. Anything outside the mount is passed through unchanged WITH A
WARNING rather than silently rewritten into a path the container cannot see --
a wrong path that looks like it worked is worse than one that complains.
A native install wins when it is present. Someone who installed Joern
deliberately should not be quietly routed through a container, so the wrapper
uses a local joern-parse and venv when both exist; OUSAST_PREFER_NATIVE=0
forces the container. Verified end to end from an unrelated directory.
The image builds itself on first use and says why it is pausing. A first run
that prints a wall of build output and then works is worse than one that
explains itself.
Defaults stay conservative: the working directory mounts READ-ONLY, findings go
to ./.ousast as the only writable mount, and the network is off unless
`ousast-with-judge` asks for it.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
|
46232fe3e6 |
feat(ops): ship the tool and its engine in one image
A contributor should be able to run this with nothing installed but Docker: docker compose build TARGET=/path/to/repo docker compose run --rm ousast scan /target One image rather than two services, and the reason is the seam. The model layer reaches Joern by subprocess; putting both in the same image means Joern is simply on PATH, so there is no container-to-container call, no docker socket to mount, no host/container path translation, and no second code path in cpg/backend.py to get wrong. Sibling services would need the tool to exec into the engine's container, which means handing a linter the docker socket -- a large amount of privilege for something that reads source code. The image is big (a JRE plus ~2 GB of Joern); that is the honest cost of bundling an engine. I had started building the two-service version with a docker-wrapper mode in the backend and dropped it, tests included: once the engine is on PATH the wrapper is complexity with no purpose. Defaults chosen so an unfamiliar analyser is safe to try: the target mounts READ-ONLY, /work is a tmpfs so CPG scratch never outlives the run or touches the host, network_mode is `none` unless the LLM judge is wanted, and API keys pass through the environment rather than into an image layer. mem_limit is set because two Joern JVMs at once is what triggered an out-of-memory kill on a 7.7 GB machine during this feature's measurements. pyinfra deploy kept for persistent host installs, with the same version pin and the same checksum verification -- done as an explicit `sha512sum -c` step because pyinfra's files.download offers sha384/sha256/sha1/md5 and Joern publishes sha512. That mismatch was caught by validating against the installed pyinfra rather than trusting the API from memory. Both include php-cli: php2cpg drives PHP-Parser and shells out to a real interpreter, and without it a PHP tree fails with an opaque "Process exited with code 1" -- measured here, which is what makes a WordPress corpus feasible. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
3fe7b9ded7 |
fix(model-grounded-detection): the dominance question contradicted its own evidence
vampi-users-update-password scored both_silent while the arbiter returned model_corroborated on the vulnerable side and nothing on the fixed one -- so the finding existed and the pipeline threw it away. The residual question was written for the ENTAILED case, but entailed returns before the judge is ever consulted, so the question is only ever asked for CORROBORATED. It asserted "in a file where sibling handlers are guarded" and asked about "the guard its siblings have", printed directly above a witness reading "and no guarded sibling" -- because corroborated means precisely that no sibling is guarded. The judge was handed a prompt contradicting its own evidence line and reasonably answered no. The question now describes what corroborated actually is: an obligated operation no guard governs, with no sibling evidence either way, so the model cannot tell from consistency alone whether the guard is missing or the module is deliberately public. That is the real open question and the one worth asking. Verified end to end against a live endpoint: vuln model_corroborated, fixed silent, the pair splits. The taint and config questions were checked for the same defect and are correct -- each describes the corroborated case it is actually asked about. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
c74e101d10 |
measure(model-grounded-detection): 86.7% pair-correct at half the hunter's leak
hunter (K=5) pipeline (K=1)
pair-correct 76.7% 86.7%
leak 12.3% 6.7%
detection -- 93.3%
cost ~$0.69 $0.0067
Ten points of recall over the hunter, half the leak, a fifth of the model calls,
~100x cheaper. 26 of 30 pairs correct.
Four runs, and each step came from a measurement contradicting an assumption --
twice one I had stated:
1 73.3% the absence and config families had no pipeline path at all
2 73.3% routing fixed and the result IDENTICAL, which was the clue: the
arbiter was reached and its verdict thrown away, because every
corroboration was put to the judge as a taint question
3 80.0% residual question phrased for the arbiter that raised it
4 86.7% aiohttp Application(debug=True) and RedisStorage(httponly=False)
added to the settings table
The division of labour keeps moving the right way: model_entailed 9 -> 11 while
suspicion held at 15, so both findings gained in run 4 came from the MODEL, not
the LLM, and cost fell from $0.0126 to $0.0067 as work moved onto the free
deterministic path. That is the payoff curve for fact-table coverage, measured
rather than argued: closing a gap moves findings out of the LLM's column into
the model's, improving precision and cost at once.
Recorded corrections and limits, because the numbers are only worth what the
caveats are. The three silent config pairs were called corpus defects on a
similarity threshold borrowed from the OWASP minimal-fix analysis; it does not
apply to a slice that deliberately pairs a vulnerable function with a different
safe one, where 36% and 34% pairs both score pair_correct. Two were a settings
gap and now split. And permissive_values is a flat literal list with no keyword
association, holding both True and False -- debug=True is dangerous where
httponly=True is safe, and the model cannot tell them apart. It did not bite
here, and it is a real limit of the fact schema.
Four remaining misses, each with a named cause. Four runs cost about $0.04.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
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950a4c7ac4 |
fix(corpus): the three silent config pairs were a settings gap, not corpus defects
I was wrong, and the correction matters more than the fix. I reported these three pairs as corpus defects on a similarity measure -- 23%, 45%, 53% -- and said no settings table could make them split. That threshold came from the OWASP minimal-fix analysis and does not apply to this slice. The vibe-py corpus pairs a vulnerable function with a DIFFERENT, SAFE function from the same repository; low similarity is the design, not a defect. djangoat-views-user-view sits at 36% and dsvpwa-handlers-do-bdr at 34%, and both score pair_correct. Reading the actual code instead of the metric shows what the settings table was missing: aiohttp's Application(debug=True) and RedisStorage(httponly=False). Adding them, two of the three now split -- vuln model_entailed, fixed silent: dvpwa-app-init Application(debug = True, middlewares = [...]) dvpwa-middlewares RedisStorage(app['redis'], httponly = False) The third, dsvpwa-dsvpwa-main, stays silent for the reason predicted before the change: its setting is an argparse default (--ssl defaulting to 0), which is a CLI default rather than a security-setting call and is not reachable by this arbiter. One weakness this exposes and does not fix: permissive_values is a flat list of literals with no keyword association, so it holds both True and False. debug=True is dangerous while httponly=True is safe, and the model cannot currently tell them apart. It happens not to bite on these pairs; it is a real limit of the fact schema and worth naming. 734 passing, gates byte-identical. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
db3db6056d |
feat(model-grounded-detection): widen the settings table, wire the weak-algorithm arm
Two things, and a finding that says neither fixes the pairs that prompted them.
The settings table was Flask/FastAPI-shaped (CORS, set_cookie, app.run) while
the corpus contains aiohttp and Django applications, so three config_secrets
pairs contained no modelled security setting at all. Added aiohttp's
session_setup and cookie storages and Django's SECURE_* / DEBUG settings: 8
calls to 24.
And config_secrets spans three abstractions while only one was wired -- a
permissive setting VALUE. Weak algorithm CHOICE (CWE-326/327) is a different
thing: the danger is the algorithm NAMED, not a flag left open. Its data has
been sitting on the sink facts as `weak_literals` since before this feature and
nothing read it. Both abstractions read the same literal, so the arm is small:
hashlib.new("md5") is now entailed and hashlib.new("sha256") is not.
The finding: the three silent config pairs are CORPUS DEFECTS, not model gaps.
Measured similarity 23%, 45% and 53% -- dsvpwa-dsvpwa-main pairs `def main()`
against `def run(self, handler)`, a different function entirely. No settings
table can make those split, and it is worth saying so rather than letting the
work imply otherwise. The table gap was real and worth closing on its own
merits; it just is not why those pairs are silent.
734 passing, mypy/ruff clean, gates byte-identical.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
|
4e7fe65f17 |
measure(model-grounded-detection): the pipeline beats the hunter on both axes
hunter (K=5) pipeline (K=1)
pair-correct 76.7% 80.0%
leak 12.3% 6.7%
cost ~$0.69 $0.0076
determinism none entailed band deterministic
Higher recall, half the leak, a fifth of the model calls, ~90x cheaper.
Three runs, and the middle one earned its keep by changing nothing. Run 2 fixed
family routing and returned numbers IDENTICAL to run 1 -- which was the clue
that the dominance arbiter was being reached and its verdict thrown away. Every
corroboration was put to the judge as a taint question ("a sanitizer appears on
the path -- does it suffice?"), meaningless for a missing guard, so the judge
said no and the finding was dropped. Run 3 phrases the residual question for
the arbiter that raised it and converts 2 of 3 access_control pairs from silent
to pair-correct, arriving at model_corroborated -- a rung that existed but had
never reached a finding.
This settles a question open since the start of the session, and reverses an
earlier answer of mine. "Detection has regressed" was measured with the
proposer disconnected: an arbiter compared against a detector, before four
wiring bugs were found.
Division of labour: 15 suspicion, 9 model_entailed, 2 model_corroborated. The
model arbitrates 11 of 26 findings, and every conversion from suspicion to a
model rung removes a leak and a model call at once -- the payoff curve for sink
coverage and interprocedural resolution, now measured rather than assumed.
Still silent: 3 config_secrets (the settings table is Flask/FastAPI-shaped
while the pairs are aiohttp) and 1 access_control still to diagnose.
Three full runs cost about $0.03 in total.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
|
52f2ab5b4d |
fix(model-grounded-detection): ask the residual question the arbiter actually raised
The corrected re-measure returned numbers IDENTICAL to the buggy one, which was the clue. Routing was fixed, the dominance arbiter was reached, and the finding was discarded anyway -- because every corroboration was put to the judge as a TAINT question: "a sanitizer appears on the path: yes, does it suffice?". For a missing authorization guard that question is meaningless. The judge answers no to something that does not apply and the claim is dropped. All three vibe-py access_control pairs scored both_silent this way even though the dominance arbiter had returned model_corroborated for each of them, which I had verified by hand earlier in the session. Fourth instance in this feature of one shape: taint-specific behaviour applied to every family. Sanitizer matching, config routing, family dispatch, now the residual question. Each was exposed by a measurement rather than by a check. The question is now phrased for the arbiter that raised it -- a guard asymmetry for dominance, a permissive setting for constant abstraction, sanitizer sufficiency only for taint. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
6dbb0e6422 |
measure(model-grounded-detection): the wired pipeline halves the leak at 1/55th the cost
The first like-for-like comparison with the LLM hunter, on the same 30 vibe-py
pairs and the same metric.
hunter (K=5) pipeline (K=1)
pair-correct 76.7% 73.3%
leak 12.3% 6.7%
cost ~$0.69 $0.0126
determinism none entailed band deterministic
Half the leak, 55x cheaper, a fifth of the model calls, one pair short of the
hunter's recall (22 against 23 of 30). And understated: all six both_silent
pairs are access_control and config_secrets, which had no pipeline path to
their arbiters when this ran. A corrected run supersedes those rows.
The rung split says what is doing the work: 15 suspicion to 9 model_entailed on
the vulnerable side. The LLM supplies most of the recall; the model supplies
the precision, halving the leak by dropping claims it could contradict. That is
the division of labour the architecture was designed around and the first
evidence it pays.
It also revises an earlier conclusion of mine. "Detection has regressed" was
measured with the proposer disconnected -- an arbiter compared against a
detector. With the enumerator wired in, the comparison reverses on precision
and cost while holding recall.
And it makes the strategy concrete: the model arbitrates about a third of
findings today, and every suspicion converted to model_entailed is leak removed
for free and one fewer model call. Sink coverage and interprocedural resolution
now have a measured payoff rather than a hoped-for one.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
|
7a76fcb028 |
fix(model-grounded-detection): the pipeline arbitrated only flow families
scan_region took a TaintSpec and called taint_verdict, so access_control and config_secrets -- whose dominance and constant-abstraction arbiters have worked since group 4 -- had no pipeline path at all. On the first end-to-end vibe-py run every one of their pairs scored `both_silent`, which reads as the model failing when in fact no code reached the arbiter. This is the same class as the ceiling harness routing config_secrets through taint: family routing fixed in one place and not carried to the other. Third time this shape has appeared in this feature, which is worth saying plainly. Dispatch is on the spec TYPE rather than a family name, so a family without an arbiter cannot be silently routed to the wrong one -- it simply has no spec and is skipped, visibly. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
cdedb372dd |
feat(model-grounded-detection): wire the detector -- the enumerator proposes, the model disposes
Everything before this built an ARBITER: something that judges a flow taint
reachability had already found unaided. Nothing proposed to it. That is why its
recall was never comparable to a detector's, and why comparing 41.3% entailment
against the LLM hunter's 76.7% pair-correct was comparing two different kinds
of thing. model/judge.py and enumerate_candidates were called by no production
code at all; every number so far came from a measurement harness.
The wiring turns on a distinction judge() alone conflated:
the model CONTRADICTS it has coverage at this site and says no -- a
sanitizer on the path, or a declared-safe sink shape.
The claim is dropped (Req 8.2).
the model CANNOT DECIDE no modelled sink or source reaches the site at all.
The model is SILENT, not negative, so the claim is
reported at `suspicion` (Req 8.4).
Treating silence as refutation is the tempting mistake and would be wrong here:
23 of the 39 misses in the entailment ceiling were sinks simply absent from the
fact tables, so "no flow" usually means the model cannot see it rather than
that nothing is there. The rung carries that uncertainty on the finding's face
instead of a confidence score nobody can act on.
An entailed site still costs zero model calls. The candidate budget is bounded
so a dense region cannot become an unbounded spend.
Smoke-tested end to end against a live endpoint: 2 pairs, detection 100%,
$0.0024. The full vibe-py measurement -- the first like-for-like comparison
with the hunter's 76.7% pair-correct and 12.3% leak -- is running.
727 passing, mypy/ruff clean, zero orphaned modules.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
|
1af3db37f6 |
feat(corpus): vendor 40 OWASP xss pairs as the owasp slice
output_encoding had exactly ONE pair in the whole corpus. It now has 41. 238 pairs were harvested from OWASP BenchmarkJava (2740 generated servlets, GPL-2.0, with a ground-truth CSV) by matching each really-vulnerable test to the safe test of the same category it most resembles -- adjacent tests come from one template, so they differ in the sink or the sanitiser rather than being unrelated programs. Median similarity 0.91, against the 64% of the older `sast` OWASP entries that the group-4 stratification flagged as different programs. Only 40 are vendored. One representative of each of the seven categories was run through the model BEFORE vendoring, and only xss distinguished: its fix adds ESAPI encoding, which the Java sanitizer facts recognise, so the vulnerable side entails and the fixed side drops to corroborated. cmdi, sqli, ldapi and xpathi put the sink in a helper class outside the servlet and resolved no flow at all; pathtraver found the flow but its "safe" twin carries no distinguishing call, so text similarity had matched a twin that is not semantically the fix -- a subtler form of the very defect the harvest was designed to avoid. Those 198 pairs are deliberately left out: adding pairs the model cannot read would inflate the corpus while making every number less interpretable. Evidence for the vendored slice is 3 of 3 distinguished, which is a small sample and is stated as such in the catalog header rather than implied to be more. The slice is marked SYNTHETIC in its header: it measures breadth across a weakness class and must never stand in for real-world performance. The corpus already shows a +16% overfitting gap, and a large generated slice blended into a headline would widen it invisibly. Two things recorded rather than hidden: BenchmarkJava's LICENSE is GPL-2.0 while the older sast entries declare GPL-3.0 for OWASP, which looks like an error and is not copied; and OWASP-Benchmark/BenchmarkJava already straddled the train/holdout split before these pairs (owasp-java-cmdi train, owasp-java-sqli holdout), so the new all-holdout pairs enlarge an existing straddle rather than create one. The two vendored-count guards did their job -- a corpus change could not land silently -- and are updated as the acknowledgement. Hygiene artifact regenerated: 277 -> 317 vendored pairs. 720 passing, mypy/ruff clean, gates byte-identical. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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fcb12e116a |
feat(model-grounded-detection): Java web sinks, and the post-closure-fix ceiling
Java shipped with injection, deserialization and crypto sinks only, so the path and output_encoding families had nothing routing to them by CWE and any Java pair in either was unreadable -- the gap hutool-cve-2018-17297 reported as "no path spec for java". Adds the JDK filesystem entry points, the servlet response writers, the outbound/redirect APIs, and the ESAPI / canonical-path sanitizers the OWASP suite itself uses. Documented APIs, provenance per entry. Post-closure-fix ceiling: 41.3% entailed (38/92) and 26.1% distinguished, up from 39.1% / 23.9%. path went 0/8 to 1/8 with its first distinguished pair; injection 29->31 entailed and 18->19 distinguished. Two points -- real, and the mechanism is understood, but the remaining gaps are data rather than machinery. Carrying a correction: the measurement harness routed config_secrets through TAINT rather than the constant-abstraction arm, so its 0/10 in this artifact measures the wrong arbiter and did so in both full runs. The harness is fixed; that family's real number is the earlier targeted re-measure, and this artifact's config row should not be read. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
0c71c956fd |
feat(model-grounded-detection): group 6, the execution tier as a seam not a build
The rung this project deliberately does not build. Clearwing already runs sandbox lifecycles, container pools, sanitizer images, PoC replay and stability classification, and the corrective brief concluded -- from this project's own defect history -- that reimplementing that machinery is where we go wrong. So model/execution.py decides eligibility, calls an adopted tool, and records what happened. A test asserts structurally that it imports no container or subprocess machinery and defines no build/run/compile/patch function, because the failure mode to prevent is this file quietly growing a sandbox. Eligibility is narrow in both directions: only a family a static model cannot arbitrate (memory safety in C, where the bug turns on runtime layout -- Req 10.3 forbids any web/logic family depending on this tier, and each now has taint, dominance or constant abstraction instead), and only a finding the model left undecided. The tier escalates uncertainty; it never re-checks a verdict. One deliberate asymmetry: a failure to reproduce is NOT a refutation. A sandbox that does not trigger a bug has not shown the bug is absent, only that this attempt did not trigger it, so the candidate stays where the model left it. Reading a failed reproduction as a clean bill of health is the same error class as reading an absent engine as "no vulnerability". With no adopted tool present -- the normal case -- the rung is never reached and clearwing_unavailable is recorded. All 16 tasks of model-grounded-detection are now done. 720 passing, mypy/ruff clean, gates byte-identical, zero orphaned modules. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
86f434ab89 |
docs(model-grounded-detection): tick 5.1/5.2 and record the closure-scoping fix
The boxes were left unticked and the notes unwritten when group 5 was committed. Records the closure fix and the three bugs it uncovered, each of which would have raised the number while making the answer untrue, and the correction that req.url was not a wrong diagnosis but an untestable one -- the scoping bug blocked everything downstream of it. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
f1c341df6c |
feat(model-grounded-detection): group 5, corpus calibration and honest reporting
5.1 inverts what the corpus is for. Previously a pair the detector missed was a
DETECTOR failure, to be averaged away over K runs and chased with a prompt
change. The arbiter is deterministic: it will miss that pair the same way every
time, and the only thing that moves it is better modelling. So a pair the model
cannot split is a NAMED GAP IN THE MODEL, recorded with its reason, and every
gap carries one even when the caller supplies none -- a bare gap teaches
nothing.
`covered` means the model reached a strictly higher rung on the vulnerable side
than on its fix. Flagging both sides equally is not coverage: it distinguishes
nothing, which is exactly what group 2 measured taint reachability doing on
every parameterised SQL pair. The harvest seam takes the fix commit and derives
the vulnerable side as its parent, with the fix diff as the oracle -- nothing
hand-labelled.
5.2 keeps three disciplines from the deleted publish layer, each because its
absence caused a real error here:
* per-slice rows always -- `render` RAISES without them. A mixed headline
once hid agent-written code scoring 44% against the development slice's 83%.
* the overfitting gap beside the headline, and ABSENT rather than zero when a
slice is missing: reading an unmeasured slice as "no overfitting" is the
flattering failure, not a neutral one.
* the rung on every finding, so a suspicion cannot be read as a result.
Run end to end against the committed ceiling artifact, the report says things
worth not hiding: agent-vfc 0/28 covered, vibe-py 40% against vfc-js 24%, an
overfitting gap of +16%, and gaps grouped by family with their reasons --
injection 32 (21 no verdict, 11 distinguished nothing), path 8 (all no
verdict), untrusted_destination 5 (4 entailed on BOTH sides).
713 passing, mypy/ruff clean, gates byte-identical, zero orphaned modules.
A full re-measure with the closure-scoping fix is in flight; these numbers are
the pre-fix baseline and will move.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
|
d651455d23 |
fix(model-grounded-detection): closure scoping, and the three bugs it uncovered
The group-4 finding: JavaScript is callback-heavy, so the method containing a
sink routinely is not the labeled function. `fs.readFileSync` inside the
callback passed to `fs.stat` has enclosing method `<lambda>0`, and filtering on
`sinkMethod == function` dropped it -- the whole path family measured 0/8.
Scope is now decided by line-range containment within the same file
(astParentFullName is empty for lambdas, so nesting is not readable there), and
reported as `inLabeledScope`; the Python side trusts that flag and keeps the
name comparison only as a fallback, so an engine that cannot report scope never
has its sinks silently treated as scoped. Parameter sources stay bound to the
labeled function's OWN parameters -- a callback's `err`/`stats` are not
attacker input.
Fixing the scope exposed three further bugs, each of which would have raised
the number while making the answer untrue:
* Shortest-flow-wins picked the wrong witness. With closures in scope the
same sink has flows from `req.url...` (10 steps, the vulnerability), `res`
(3) and `this` (7). A flow whose source matches the source model now
outranks a shorter one from a bare captured name; shortest still breaks
ties, so the order stays total and the verdict deterministic.
* The JS fact file had NO sanitizers at all, so no JavaScript fix of any
family could ever be recognised. Added the documented path-normalisation
and HTML-escaping APIs, with provenance.
* Adding them regressed the witness, because `resolveUrl` CONTAINS `resolve`
and the substring test marked the vulnerable flow sanitized. This is the
same bug class already fixed in dominance.sc and not carried across -- the
exact fix-the-instance-miss-the-class failure. taint.sc now matches on word
boundaries too.
And the principled rule the fix turns on: parameter sources are a FALLBACK,
never an override. When a modelled source reaches the sink, those flows are the
evidence; otherwise a captured `res` reaching a sanitized sink reports the fix
as still vulnerable on a flow that names no attacker input.
Also recorded: req.url was NOT wrong as a diagnosis, it was untestable in
isolation -- the scoping bug masked it. Both were needed. On the pair that
motivated all of this:
vuln model_entailed req.url.split('?')[0] -> readFileSync(possibleFilename)
fixed model_corroborated same flow, sanitized by path.normalize
distinguished: true
700 passing, mypy/ruff clean, gates byte-identical.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
|
91c0931a05 |
measure(model-grounded-detection): group 4 ceiling, 39.1% entailed against the flat IR's 2.2%
Full model layer over 92 pairs: 39.1% model_entailed (36/92), 45.6% reaching any verdict, 23.9% distinguished -- an 18x lift in entailment from an arbiter that makes no model calls at all. Per family: untrusted_destination 0.80 entailed, injection 0.58 (0.36 distinguished), deserialization 0.33, prototype 0.14, config_secrets 0.10, path 0.00. access_control reaches 6 of 8 pairs at 0.38 distinguished -- the class no crash oracle can reach, arbitrated deterministically. A hypothesis tested and discarded. From the group-2 misses I diagnosed path's 0/8 as a missing req.url source token and said so. The probe -- the same 8 pairs with and without the token -- returned 0/8 -> 0/8. Falsified. The real cause is that the sink sits lexically inside an anonymous callback, so its enclosing method is the closure (<lambda>0) and the sinkMethod == function filter drops it; verdict(function="") on the same CPG returns model_entailed. And the obvious fix for that is wrong. Relaxing the filter raises the number with a FALSE witness: the flows found in the closure are `res -> readFileSync` and `this -> ...`, neither of which is the vulnerability, whose real path crosses a helper (req.url -> url -> resolveUrl -> possibleFilename). Turning a miss into a wrong entailment is worse than the miss -- it is the arbiter asserting something untrue, the one failure mode this architecture exists to prevent. The correct fix is two-part and recorded: scope a sink to any method lexically nested in the labeled function, and scope parameter sources to that function's own parameters, then re-measure counting only real witnesses. Every miss is classified and none is architectural: 23 sink absent from the excerpt or unmodelled, 13 no recognisable source in the excerpt, 3 source unmodelled with the idiom named. Two honesty items in the artifact. 16 of 92 pairs carry no function label and are arbitrated over the whole file, so a verdict can credit a different finding than the labeled one -- owasp-python-hash (CWE-327) is credited for a permissive set_cookie elsewhere in its file, on both sides. And config_secrets conflates three abstractions: permissive setting values (implemented), weak algorithm choice (facts already carry weak_literals, unwired) and hardcoded secrets (needs a credential-literal check). 694 passing, mypy/ruff clean, gates byte-identical, zero orphaned modules. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
eebca3f663 |
fix(model-grounded-detection): the absence arbiter was silencing its own family
Two bugs, together making 4.1 report "no bug" on a textbook IDOR. Both found by running it against a real corpus pair rather than a fixture. 1. constraint_params were treated as dischargers. A field named `user_id` is what an IDOR is MADE OF -- `filter_by(id=user_id)` with user_id off the request is the bug, not the fix. The deleted checker only counted an identity constraint whose value's PROVENANCE was the authenticated context, so the parameter name alone evidences nothing. This is the mirror of group 2's sanitizer bug: a thing that must never count as a discharge, counted. 2. Guard tokens matched as substrings. With `user` in the token list, the string "SELECT * FROM users WHERE id = ?" contains `user`, so the query reported every operation in the handler as guarded. Matching is word-boundary now. On threatbyte-api-v1-delete the arbiter previously found three operations, all "guarded" by evidence like '"SELECT * FROM users WHERE id = ?"' and '(user_id,)', and returned no verdict at all. After the fix, on the real pairs: threatbyte-api-v1-delete vuln=model_corroborated fixed=none vampi-users-update-password vuln=model_corroborated fixed=none Both distinguished, deterministically, with no model call. Corroborated rather than entailed is right for these: they are single-handler excerpts with no guarded sibling to be inconsistent with, which is the asymmetry the rung distinction exists to express. identity_sources still discharge; request_sources and permissive_values remain excluded because they describe the vulnerable shape rather than the fix. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
4f8f590b93 |
fix(model-grounded-detection): type the config literals list
mypy could not see that row["literals"] is a list on an object-valued dict. Narrowed at the use site rather than widened at the boundary. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
d9ceeb6a09 |
feat(model-grounded-detection): 4.2 constant abstraction, the third arbiter form
A configuration bug has no flow to follow and no guard to dominate.
CORS(app, origins="*") is dangerous because of the value it is SET TO, so the
arbiter evaluates the setting's arguments against the closed permissive set in
the obligation facts -- the third form beside taint reachability and guard
dominance, and the one Req 6.3 names.
ENTAILED a security setting is passed a literal in the permissive set
CORROBORATED the argument is computed, so constant abstraction cannot decide
it -- that residual is the judge's, never silently "safe"
(none) every literal is outside the set, or the call is not a setting
Two Joern node-shape bugs, both found by running it and both of the same class
as group 2's receiver bug -- the graph's indexing is not what the source text
suggests:
* a keyword argument (`origins="*"`) is an assignment-shaped node, so
isLiteral is false on the argument itself and the literal sits in its
subtree; literals are collected from the argument's AST now.
* Joern indexes the receiver at 0, positional arguments from 1, and NAMED
arguments at -1. Settings are configured overwhelmingly by keyword, so
argumentIndexGt(0) -- correct when counting arity for the taint shape test
-- dropped exactly the arguments that matter and every keyword-configured
setting read as "computed".
Live, deterministic, zero model calls:
create_app -> model_entailed CORS(app, origins = "*"): permissive literal "*"
safe_app -> no finding (origins = "https://app.example")
All three arbiter forms now work against a real CPG: taint reachability for the
flow families, guard dominance for absence, constant abstraction for config.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
|
fec67253f3 |
feat(model-grounded-detection): 4.1 guard dominance, 4.2 the missing sink facts
4.1 is the arbiter for the class no crash oracle reaches. An access-control bug
is the ABSENCE of a guard: no flow to follow, no crash to reproduce. What the
model can establish is a consistency violation -- an obligated operation no
discharging guard governs, in a file where its siblings are governed by one.
"Governed" turned out not to be one relation, and asking pure CFG dominance of
the operation gets two of three shapes wrong. Measured against the real engine:
identity constraint the guard is INSIDE the operation's arguments
filter_by(owner_id = current_user.id)
identity branch abort(403).controlledBy == [note.owner_id != current_user.id]
-- the check runs AFTER the fetch, so it never dominates
the operation, but it controls whether the value escapes
dominating guard a discharger-naming call that dominates the operation
That is why the query uses controlledBy rather than dominatedBy for shape 2.
Two bugs found by running it, both the same class as the sanitizer bug in group
2 -- part of a fact flattened, the part carrying the signal dropped:
* dominance_specs used only `calls` and `decorators`, omitting
identity_sources (current_user, request.user) and constraint_params
(owner_id, user_id) -- which is how an identity constraint is actually
written. The arbiter reported "no guard" on handlers that plainly had one.
request_sources and permissive_values stay excluded: they describe the
vulnerable shape, and including them would let the arbiter read a bug as
its own fix.
* matching operations by code containment yielded four or five desugared rows
per operation (the bare `tmp1.filter_by` receiver among them, carrying none
of the guards the real call has), so a guarded operation could be flagged.
Matching is by call name now.
Live, deterministic, zero model calls:
leaky -> model_entailed no discharging guard, while 2 sibling handlers are guarded
edit_note -> no finding (ownership check controls the denial)
list_notes -> no finding (identity constraint in the query)
4.2 authors the sink facts the go/no-go measured as its largest miss class (11
of 23): Django ORM raw SQL, and the four families that previously had no fact
routing to them by CWE at all -- path, output_encoding, untrusted_destination,
prototype. Each entry is a documented dangerous API of the named library with
its provenance recorded inline, and rule_ids left empty because a sink fact is
not a detection rule. Flagged for maintainer review as data, given the standing
"no LLM-authored patterns" constraint. The exact-set assertion written in task
1.1 to hold this gap open is updated rather than deleted, so it still fails if a
family ever loses its seed.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
|
b8f8f727c6 |
feat(model-grounded-detection): group 3, the evidence ladder and the judge
3.1 adds StaticFinding.rung beside evidence_level rather than renaming it. The existing evidence_level carries `static_corroboration`, emitted by the flat IR overlay -- a path Req 4.4 demotes to the suspicion band. Restating that as model_corroborated would claim a CPG arbitrated something it never saw, and rewriting every legacy finding to suspicion would be a product-visible regression nobody asked for. So `rung` defaults to suspicion and is raised only by ladder.at_rung(finding, verdict), which is the structural form of Req 5.4: nothing rises above suspicion without a Verdict behind it. The witness travels with the rung, because a rung without its evidence is just a louder assertion. It reaches markdown, JSON and SARIF. 3.2 is the judge, and it is defined as much by what it lacks. Three exhaustive outcomes: the model entails (report it, never call the model), the model contradicts (drop the claim, record why), the model cannot decide (report suspicion). The LLM gets one bounded typed question with tools=[] so it cannot search for the site, asked only about the residual the graph cannot settle -- whether a sanitizer on a REAL flow suffices. Its answer can only confirm a flow the CPG already found; it can never conjure one. Req 8.5 is asserted structurally: a test parses judge.py and fails on any AST name like k_runs/majority/vote/average/acceptance/floor/budget, any for/while loop, or an import from the deleted round machinery. That test first matched the word "votes" in the module's own docstring, which is why it now inspects code rather than text. Verified live against a real CPG, not only scripted rows: vuln rung=model_entailed request.args["name"] -> db.execute(...) fixed rung=suspicion no source reaches this sink model calls made: 0 Both sides arbitrated by the graph with the LLM never consulted -- the thing the prior architecture could not do at any price. 678 passing, mypy/ruff clean, gates byte-identical, zero orphaned modules. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
|
7387d2f190 |
measure(model-grounded-detection): task 2.3 reads GO on Joern
Injection slice, 50 pairs, ~30 minutes of real engine time.
model_entailed on the vulnerable side 54% (27/50) vs the flat IR's 2.2%
... on minimal-fix pairs only 61.5% (16/26)
pairs distinguished 32% overall, 38.5% on minimal-fix
determinism (Req 6.4) every verdict identical on re-run
cost median 32s/pair, max 58s
CPG build failures 1
The engine is not the constraint: all 23 misses trace to our own data model --
11 sinks absent from the fact tables (Django ORM objects.raw, self.query,
GraphQL resolvers), 11 where the sink is modelled but no source reaches it, and
one C translation unit that does not parse standalone, counted separately so it
can never read as "no vulnerability". That is exactly the result that justifies
adopting a CPG rather than building one.
The finding that changes the design: classifying what the 50 injection FIXES
actually do gives other_or_validation 37, bound_parameters 10, argv_list 2,
sanitizer_call 1. The dominant fix is a validation guard on the path, not a
sanitizer and not a sink shape -- and taint reachability can never separate
those pairs, because the flow still reaches the sink and what changed is that a
guard now dominates the path. The arbiter must be TAINT + DOMINANCE, which is
the same primitive task 4.1 builds for access_control, with the guard
vocabulary ported in task 1.1 as its lookup list. Two planned mechanisms
collapse into one, and 4.1 should now precede the remaining TaintSpecs.
Reported per stratum because only 26 of 50 pairs are genuine minimal-fix pairs:
12 are different programs (local-python-vulnerable is 7% similar with 83
changed lines) and 12 are large rewrites. A raw distinguished_rate would
measure corpus quality and report it as model quality.
Also corrected in the notes: the Flask-shaped source table is a real gap but a
minor lever -- adding Django patterns would gain a source for exactly one pair.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
|
e127e430ed |
feat(model-grounded-detection): parameter sources, so the measurement is like-for-like
Only 11 of the 50 injection pairs contain a framework source token at all. The
other 39 receive untrusted input as a FUNCTION PARAMETER, because SVEN-style
pairs are function-level extractions:
def add_user(username, password):
os.system("useradd ... -p " + encPass + " " + username)
A framework-anchored source model finds nothing there, which caps entailment on
this slice near 22% by construction -- a property of the source model, not of
the engine. More to the point, the flat-IR baseline this feature is measured
against already counted these (source_kinds: ["parameter"]), so a run without
them is not comparable to the 2.2% it is supposed to beat.
The flag is opt-in and off by default, because the two settings are right in
different places: in a function-level pair the function boundary IS the trust
boundary, while across a whole repository most parameters carry internal
values and treating them all as untrusted is not sound. The corpus harness
opts in; a repository scan does not.
On the pair that exposed this the model now resolves both flows, including one
through an intermediate binding:
username -> os.system(...) 2 steps
password -> crypt.crypt(...) -> encPass -> ... 7 steps
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
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1765e51da1 |
feat(model-grounded-detection): the safe-shape test, without which the model arbitrates nothing
The first live run produced the finding that shapes this design: taint
reachability alone does not distinguish a fix from a bug. On the canonical SQL
pair the flow request.args["name"] -> name -> execute exists on BOTH sides --
vuln db.execute("select ... '" + name + "'") one argument, interpolated
fixed db.execute("select ... = %s", (name,)) two arguments, bound
-- so the model entailed the fixed twin exactly as readily as the vulnerable
one, and arbitrated nothing. What separates them is the SHAPE of the sink call,
which is precisely what safe_shape_sinks preserved from the earlier sanitizer
fix. The query now reports sinkArity and sinkArg0Literal, and a flow ending at
a sink written in its declared-safe form is not a vulnerability.
Two bugs in that shape test, both found only against the real engine:
Joern's `call.argument` includes the RECEIVER at argumentIndex 0, so `db` was
counted as an argument -- making a one-argument interpolated call look like a
two-argument bound one, the exact inversion of the test -- and the "first
argument" whose literalness decides a format-string sink was the receiver
rather than the format. Both fixed with argumentIndexGt(0).
The rule is narrow on purpose: only sinks named in safe_shape_sinks are subject
to it (os.system(x) is not safe merely because it takes one argument), and a
row carrying no shape fields is judged on the flow alone -- an unknown shape
must never be read as a safe one.
End to end on the pair, deterministic and with no model call:
vuln -> model_entailed request.args["name"] -> db.execute(...) (line 4)
fixed -> None
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
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f59e0f5b5a |
fix(model-grounded-detection): two modelling bugs the first live Joern run exposed
Both were found by running the real engine, and neither was visible offline.
1. A sink was its own sanitizer. The shipped facts mark some sanitizers with a
*shape* qualifier -- parameterized ("execute(sql, params) binds rather than
interpolates") and literal_format_arg ("printf with a constant format string
is safe"). These describe a safe FORM OF THE SINK CALL, not a call that
cleans a value on the path. Flattening them into the sanitizer list made
`execute` both the injection sink and its own sanitizer, so every SQL flow
would have reported as already-clean and nothing could ever be entailed.
Fixed as a class, not an instance: any fact carrying a shape qualifier is
excluded from `sanitizers` and kept in `safe_shape_sinks` for the shape test
task 4.2 will model properly. Fixing only the Python `parameterized` case
would have left C's entire printf family -- which the generalised test then
caught immediately.
2. labeled_family lost its deterministic derivation in group 1. The deleted
classifier resolved a pair's family by declared label, else obligation, else
mechanism, else CWE, and only asked an LLM when every link failed -- which
for a labeled corpus was never. Group 1 reduced this to the declared label
alone, which silently emptied every family whose pairs are labeled by CWE:
the injection slice went to zero pairs. The chain is restored, still with no
model call, and the family census again matches the committed entailment
ceiling exactly (injection 50, memory 176, access_control 8, ...).
Also: the Joern seam now runs in the CPG's own scratch directory, because
`joern --script` writes a `workspace/` beside the working directory and would
otherwise leave one inside the repository being analysed.
The taint query itself was corrected against the live engine: the sink is the
call's ARGUMENTS (data flows into arguments, not into the call node) and
reachableByFlows is called on the sink with the source. With that, the
three-hop chain our flat IR could not resolve is found:
request.args["cmd"] -> cmd -> "echo " + cmd -> full -> os.system(full)
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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Marius CiepluchandClaude Opus 5
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2b973dc0db |
feat(model-grounded-detection): tasks 2.1-2.2, the Joern seam and the taint verdict
The whole engine integration is one subprocess boundary: joern-parse builds a cpg.bin, joern --script runs a shipped CPGQL query that prints its JSON between fence markers, because Joern's stdout carries a banner and a REPL prompt around whatever a script emits. Nothing is imported in-process, so the core install keeps dependencies = [] -- a test walks the seam's AST to assert it imports only the standard library and names no JVM binding. Fail closed everywhere. A build or query that errors, times out, or prints something unparseable yields no verdict and the caller reports suspicion. The taint verdict distinguishes this from absence deliberately: a query returning None means "the engine could not decide", which is not "there is no flow", and conflating them is how a missing engine silently becomes a clean bill of health. The verdict is where the ladder earns its keep. An unsanitized source->sink path is model_entailed -- the graph establishes it and the LLM's claim is no longer load-bearing. A path a sanitizer may break is model_corroborated: the flow is real, but whether the sanitizer suffices is the semantic residual the LLM answers, checked against this verdict rather than averaged over runs. The chosen flow is picked by a total order (shortest, then lexicographic), so two runs over one CPG cannot disagree -- Req 6.4 asserted directly. The query itself is the part our flat IR could not do: reachableByFlows walks the PDG, so `q = "..." + request.args["x"]; execute(q)` is found, which is exactly the one-hop limit that held measured entailment at 2.2%. Capability detection reuses semantic/engines.joern_available rather than growing a second notion of "installed". The audit manifest is regenerated; the new modules are named by Req 4/5/6 rather than counted as orphans. Task 2.3, the go/no-go measurement, needs Joern installed and has not run. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 5
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9ed0e45c5f |
feat(model-grounded-detection): group 1, subtract the noise architecture
99 source modules -> 81. 832 tests -> 646. The whole learning/ package (16 modules, ~4,100 lines) and six semantic/ modules removed, with the three gates byte-identical to 2026-09-06-gate-baseline.txt at every step. The sequence is what made it safe: vocabularies ported first (1.1), importers detached second (1.2-1.3), deletion third (1.4). Every import break during the deletion was therefore a detachment miss in the importer and was fixed there -- no module was restored to satisfy one. Both misses were the same shape and were fixed as a class: operations.py and shapes.py each imported trailing_name from the deleted variants.py, so it moved to semantic/ir.py, its natural home, and both importers changed in one edit. Ported before deleting: the closed guard vocabulary (verbatim, nothing else holds it) into model/specs.py, which derives TaintSpec and DominanceSpec from the retained ruleset facts rather than copying them. Re-homed: families -> model/taxonomy, candidates -> model/candidates, endpoint -> model/endpoint, plus Region, labeled_family and labeled_spans. labeled_family no longer falls back to the LLM classifier -- an unlabeled pair is honestly unknown. Four deviations, each recorded in the spec notes with its reason. improve/ evolve.py and validator.py were stripped, not deleted: each mixes the mechanism lever (gone) with the deterministic rule-status loop `ousast improve` still runs, which has no LLM in it; a test now asserts the lever's absence by symbol so it cannot creep back. The learning CLI group became `model candidates`, because deleting it outright would have orphaned the retained enumerator by removing its only entry point. thinking moved from [learning] to [models]. A gap named rather than papered over: no retained sink fact routes by CWE to path, output_encoding, untrusted_destination or prototype -- the same hole the entailment ceiling diagnosed. The test asserts the seeded set and the gap set exactly, so task 4.2 must update it when it authors those sinks. The audit found exactly one genuine orphan: vectorstore.py, a bakeoff harness with no production caller and no CLI, alive only through its own test. The five named subsystems each carry an explicit decision. Manifest committed, with a test asserting the tree still matches it. Group 2 (the Joern go/no-go) has not started. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 4.8
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fc4dab97fb |
docs(model-grounded-detection): attach Req 7.4 (verifier boundary) to task 1.1
The prior commit's edit did not match and 7.4 was left unclaimed. model/specs.py is read-only data -- no LLM and no optimiser writes it -- and task 1.1 now carries that criterion with a test asserting the module exposes no writer. All 49 criteria trace to a task. Co-Authored-By: Claude Opus 4.8 <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |
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Marius CiepluchandClaude Opus 4.8
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6fe8682993 |
docs(model-grounded-detection): 16 tasks in 6 groups, with a coupling map so Opus does not crash-discover
Design approved. All 49 criteria trace to a task. The tasks carry an agent-guidance preamble aimed squarely at the escalations this session hit: the eighteen-defect lesson (fix the class, not the instance), the gate-baseline as the safety net (the whole learning/ package is off the gate path, so its deletion cannot move a gate -- a moved gate means you touched a retained path), the zero-dep capability-detection pattern to copy verbatim, and -- most important for the subtraction -- a full coupling map naming every touchpoint of the removed code (cli, pairs hunter path, config, reports, semantic seed/loo, the mechanism loop across improve/ and semantic/). The subtraction is not discovery by import error. Group 1 is pure gate-protected subtraction, sequenced so vocabularies are ported (1.1) and importers detached (1.2-1.3) before anything is deleted (1.4), then tests reduced in the same spirit (1.5) and the tree audited for orphans (1.6). Group 2 is a Joern go/no-go: task 2.3 reads go or stop, and groups 3-6 do not begin until it is green -- building past a red 2.3 is the mistake the whole spec exists to prevent. Parallelism is deliberately absent; the subtraction is sequential and the spike gates the rest. Not approved for implementation. Co-Authored-By: Claude Opus 4.8 <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG |