The 7.8 trace showed the completion loss was a sizer death spiral, not
expensive families: after two kills the budget fell to 3 sink visits and
never recovered, because portions finishing in 25-30 s sat under an assumed
70 s fixed start and read as "no information", so the budget could only
shrink.
- taint.sc streams each answer's own cost (`ms`); the backend carries it as a
reserved `__timing__` list through shards, partitions and reuse.
- The sizer budgets predicted seconds from per-family seconds-per-visit rates
learned from those costs, and measures the fixed start as the wall time the
answers do not explain (median), instead of assuming it.
- A kill names its culprit: the first unanswered id in request order. The ids
behind it never started and are re-queued at once; the culprit is asked
alone after the rest. One that still fails alone after more than a
portion's target is reported as `query_too_expensive`, apart from
`query_failed`.
- Portions grow id by id from the queue head, and a single-portion scan now
goes through the queue too, so a kill there re-asks its remainder.
- Fix: the reuse wrapper wrapped only `run_batch`, so since 7.8 every
portioned query bypassed the graph-reuse cache and its source-root
normalization. `run_batch_once` is wrapped too.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Two permanent guards, and a correction that motivates both.
THE CORRECTION. There is no php2cpg defect. Every PHP build on this machine
ran through a local `php` shim proxying stdio through `docker run`, and
php2cpg depends on the INTERLEAVING of its parser's stderr banners with its
stdout JSON to attribute each document to a file. The proxy scrambles it:
streaming shim drops 72-88 767KB / 969KB / 1.2MB different every run
buffered shim drops 0 5,703 bytes empty, and silent
NATIVE php drops 0 2,872,996 bytes identical x3
The 637-file tree with native php: 0 drops, a 4,329,471-byte CPG, 20.0s,
1.9GB peak -- which is exactly what task 5.10 said before I started
"correcting" it. No intermittent failures, no malformed graph, no dataflow
assertion, no scale limit.
GUARD 1: the interpreter must prove it can read the tree, before anything is
built. Every instrument failure this project has had takes one shape -- the
input was unreadable and the tool reported a plausible ZERO -- and it has cost
four wrong diagnoses in a day:
a php that could not see ~/.cache -> "cannot handle 637 files"
a php whose stdio was proxied -> "drops half its batches"
a container missing a bind mount -> "0 dropped", 5,703-byte graph
None announce themselves: file_exists() is false, the parser writes nothing,
the frontend exits 0, and "no errors" reads exactly like success. Checking the
output cannot separate that from an empty repository. Checking that the input
arrived can, and costs one exec.
GUARD 2: the graph census is taken on EVERY build, not only one the frontend
warned about. A graph holding nothing arrives with no warnings at all --
silence is the symptom that has no symptom -- and the shortfall is now
reported as "the frontend reported no failures but the graph holds N of M
source files".
`cpg_build_failed` carries that detail through to the report instead of being
a bare reason.
Also adds tests/test_cpg_queries_compile.py: the queries are 900 lines of
Scala that nothing compiled. A rowsFor signature change shipped with 850 tests
passing and broke every query at runtime, found by a measurement failing hours
later. Each query is now compiled AND run against a real graph wherever joern
exists, and must return a parseable payload.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Paid Memberships Pro yields 4,463 regions from 637 files. The default budget
examines 500 of them -- 11% of the repository -- and nothing in the output
said so. A report whose silence covers the other 89% invites that silence to
be read as a clean bill of health.
`budget_exhausted` is a different fact and already reported: that is the JUDGE
running out on work already collected. This is the work never being collected.
Renders in "What could not be analysed" as: only the N highest-ranked of M
regions were examined, and nothing above says anything about the rest.
850 tests pass.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
A dropped file is dropped from the ONLY graph there is, so every question
about it has no answer -- which is how a 637-file plugin became a scan of
nothing. But those files build perfectly well alone: the failure is
per-invocation, not per-file. So a file the main build refuses is excluded
from it, given a second CPG, and both are queried with rows concatenated per
request id.
pmpro two-file slice before: cpg_empty, 0 findings
after: 18 entailed incl. CVE-2023-23488 at :936,
reported with cpg_sharded
What this does NOT buy is a flow that crosses the shard boundary: a source in
the excluded file whose sink is in the rest of the tree is invisible to every
question asked, and no merging of results recovers it. That is why the split
is REPORTED rather than quietly served as if it were whole.
The excluded-file tree is symlinks at the original relative paths. A region
asks about `classes/class.memberorder.php` and the queries match a filename by
suffix, so a flattened copy would cheerfully answer about a file nobody asked
about.
Reports gain "What could not be analysed", rendering cpg_sharded, cpg_empty,
files_unparsed, query_failed and budget_exhausted in the reader's terms. A
degradation is a fact about coverage, not a warning to file away: silence in
the coverage table means "no fact table covered this", and silence about an
empty or split graph means something else entirely.
Also removes the WordPress vocabulary from the query, which was the other half
of this request. `apply_filters` was a hardcoded Set in taint.sc and
`add_action|add_filter|add_shortcode` a hardcoded alternation in mapping.py.
Both are now a `[[dispatch]]` fact:
[[dispatch]]
id = "wordpress_hooks"
register = ["add_action", "add_filter", "add_shortcode"]
apply = ["apply_filters", ..., "do_action", ...]
The shape is general -- a registry keyed by a string literal at both ends, as
Django signals, jQuery events and Symfony's dispatcher all are -- so a second
framework is a row in a TOML rather than an edit to a query. `dispatch_apply`
is empty for every language that declares none, and the hook half then does
nothing at all rather than looking for names that mean nothing there.
848 tests pass; gates byte-identical. wpstatistics 3/2 with the CVE at :225 on
the vulnerable side only, mwwpform 1/0, pmpro 18/1, VAmPI 4 unchanged.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
contributor-scan 2.17, completing Req 5.5.
Task 2.11 made static pattern matches proportionate and left the model
layer's own suspicions alone, reasoning that a proposer had reasoned about
them. That does not survive inspection. A model suspicion is what the
enumerator proposed at a site the graph could NOT decide, affirmed by a
judge with no coverage there -- and its own rationale says exactly that:
"the model resolved no flow here; its sink table may not cover this API".
It is a pattern match with an opinion attached, and it reads like a
reasoned claim only because a model produced it.
vampi 60 findings, 61 sections -> 26, report 1085 lines -> 446
libpng 198 findings, 39 sections -> 30
**What it costs, which is the number that decides whether the rule is
right.** For each known vulnerability the recipe declares, take every
finding row at its location and ask whether any survives the rule. All
three of vampi's -- the SQL injection and both documented BOLAs -- are
still found at model_entailed and all three survive. Nothing known is
hidden. Three vulnerabilities in one repository is also a statement about
how small the evidence base is, and the artifact says so.
The obligations checker sits at `suspicion` too and is NOT grouped: its
output comes from a structural analysis, an obligated operation with no
discharging guard, rather than from a guess. An earlier attempt at this
rule filtered on rung alone and would have collapsed it; six tests failed
and they were right to.
"Pattern matches" is now "Proposed, not corroborated", since it holds two
shapes. Model rows group by FAMILY rather than by rule, because their rule
is always `model`.
Gates byte-identical.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
contributor-scan 2.16, implementing the approved Req 5.6. `c/memory`
scored zero true positives across 89k lines of C and three distinct
classes of false one, for a structural reason: it models string CALLS and
libpng overflows through arithmetic.
The task allowed retiring the family. Kept, scoped and stated instead --
`strcpy` misuse is real in plenty of C and the family earns its place for
that. Retiring it would have removed the disclosure along with the
coverage, leaving a C scan silent for a reason nobody could read.
**Its own description was the quiet-failure risk.** It claimed "bounds,
lifetime, arithmetic", which is exactly what it does not decide. It now
names the APIs it models, and a new `limits` field in families.toml says
what it cannot: an overflow expressed as an index, a pointer offset, or a
memcpy whose size is computed -- which is how most real C overflows are
written, and how every one of libpng's is.
A limit recorded in a fact table nobody reads is no better than silence, so
the report renders it. **What was analysed** lists every family offered to
a region whether or not it found anything, because a family that ran and
reported nothing is the case a reader is most likely to misread, and it
states plainly that silence means no fact table covered the code rather
than that the code is safe.
The counts are derived from the fact tables the arbiter actually used, so
they cannot drift from what the engine looked for; only the limit sentence
is authored, because "cannot decide an overflow expressed as an index" is a
statement about an abstraction and no table can produce it.
A C scan that finds nothing now says:
| c | `memory` | 121 | 9 |
**`memory` (c)** — Models CALLS, not arithmetic...
Gates byte-identical.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
contributor-scan 2.11. libpng emitted 198 sections, 163 of them asserting
that `memcpy` and `strcpy` are PRESENT in a library that uses them
correctly throughout. That is the noise developers have learned to skip in
every other tool, and shipping it costs the findings beside it their
credibility.
**Filtering on rung was the obvious rule and it was wrong.** The
obligations checker emits at `suspicion` too, so hiding that rung would
have hidden an entire detector -- six tests failed and they were right to.
The discriminator is evidence_level: on libpng 163 of 198 rows are
`static_corroboration`, on vampi 3 of 60 are, and the model layer and the
obligations checker carry `suspicion` there instead. That is exactly the
distinction the ladder exists to make -- an undecided CLAIM about a site
is not an observation that an API exists.
So a pattern-only rule is grouped once it has more than three sites, and
keeps its full sections under that. One `python-flask-debug` is a finding a
contributor can act on and must not be summarised away; fifty-four
`c-unsafe-copy` are a property of the codebase.
libpng 198 findings, 198 sections -> 39, 161 grouped into three lines
vampi 60 findings, 61 sections -> 61, nothing grouped
Counted, never dropped: findings.json stays complete because it is the
record a baseline is compared against, and the grouped section says the
model reached none of these and that its silence means no fact table
covered the site rather than that the site is safe.
What it does not fix, recorded rather than glossed: vampi still emits 61
sections for a 520-line application, 51 of them reasoned claims this rule
does not touch. It fixes pattern noise, not volume.
Also records what group 2 established about the three corpus asks, so none
is started on a wrong assumption. Joern ships php2cpg but our fact tables
have zero PHP families, so WordPress would parse and produce nothing --
fact tables first, corpus second. An older vulnerable libpng will not help
for a reason no version can fix. Vibe-code repositories are the best fit
and the cheapest route to a second true-positive checkout, because Python
and JavaScript are the two languages whose fact tables are complete.
And Group 8, for per-repository state a developer owns: a committed
dismissal file the tool reads and never writes, dismissals as a split
evaluation set, and GEPA/SIMBA-style evolution scoped to the QUESTION --
the judge and residual prompts -- never to the fact tables. That boundary
is the whole design: a prompt is how we ask, a fact table is what we
claim, and the closed-loop leak measured on 2026-09-05 is why the
distinction is not negotiable.
Gates byte-identical.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
`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
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
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
Task 3.4. A standard scan now classifies each hotspot region and runs the
detectors of every admitted family plus the generalist, reading the
configurations round zero wrote. A scan never creates them: a missing directory
is a recorded reason and the stage is skipped, which is also what happens when
no chat endpoint resolves.
Every claim passes through its family's verifier and carries what produced it:
the family, the detector version and what the verifier said. Nothing rises above
suspicion here, because the verifier writes a tag and the evidence ladder
belongs to another boundary. Reports gain a section naming the three, SARIF
gains the matching properties, and the manifest gains a learning block with the
taxonomy version, the per-family counts, the classifier report and the round.
The feature is off unless a project turns it on, so an existing scan is
unchanged, and all three gate outputs stay byte-identical.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 3.2. Each obligation finding prints its operation, the missing discharger
with the provenance that failed, the evidence behind the label, the known fix
learned from the corpus and the hunter's intent verdict; the Obligations section
lists the sibling sets evaluated and the declared policy version. SARIF carries
the same as properties.
Obligation findings rank by resource sensitivity times label weight, declared
above consistency above function-local, and stay strictly below every proven
finding. Proof rungs live on verdicts rather than on static findings, so the
cap takes the proven ids from the run's triggerable verdicts.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 5.1 (Req 6.1, 6.3).
- reports.py: the markdown and SARIF writers take the mechanisms a run
cites; a finding cites one through its mechanism:<id> tag (variant) or
its merged overlay record's mechanism_id; markdown shows Mechanism,
Known fix guard and Learned from pairs per finding plus a Mechanisms
section, SARIF properties carry mechanism_id/summary/guard/pairs;
variant findings keep suspicion, merged inventory findings keep their
own evidence level; reports without mechanisms are unchanged.
- cli: _search_variants returns the cited mechanisms from the store and
the scan passes them to both writers.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 2.3 (Req 3.4, 3.5, 3.6, 6.2).
- [variants] enabled / max_mechanisms config; MAP runs variant search
after the overlay in standard and deep scans only, appends the variant
findings, rewrites overlay.json with merged ids, and the manifest
records mechanisms_searched, files_searched, findings and
merged_into_overlay.
- REGRESS: variant findings join the candidate list after the promotions
as zero-score hotspots, under the existing safety check and cap.
- Spec notes for group 2 and the design synced (variant_search.py,
seed.py, derive_shape signature).
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 6.1.
- provenance.py: classify a checkout as human / agent / mixed / unknown
from deterministic signals only (.claude/, AGENTS.md, Lovable meta,
agent git trailers on a bounded commit sample); no model involved.
- cli scan runs a `provenance` stage; the manifest records the result.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Regex inventory stays the proposer. MAP attaches promote/demote/unadjudicated/coverage
from intra-file taint (tree-sitter when a grammar parses, Python ast as last resort).
Deep sandboxes promotions only, ordered by the existing heuristic ranker plus optional
OpenRouter mechanism recall. The SAST pair slice scores overlay outcomes; the stage-1
detection gate and local pair gate stay inventory-only.
Adopt HarnessX at the hunter loop and verifier, capability-gated so the default
zero-dependency scan is byte-identical and CI (no extra) exercises the fallback.
- 6.1/6.2 build_sast() (single, lazy HarnessX composition site in harness_ext.py,
mirrors DocuHarnessX make_docgen) + HxScanOrchestrator (hunter_harness.py): a
drop-in for run_hunter_pool that runs one bounded HarnessX agent task per ranked
target (ModelConfig(main=AnthropicProvider).agentic(build_sast()).run(BaseTask)),
parses findings from a JSON structured-output contract, returns the existing
HunterPoolResult, maps scan_id->session_id and emits per-target exit_reason/cost/
steps to the outer trace.
- 6.4 verify_judge.verify_findings_dispatch: a drop-in superset of
verification.verify_findings — structural verifier verbatim by default (rejects
unknown reachability), HarnessX llm-judge (build_judge_prompt/parse_judge_response)
when the extra + a verifier model are present; verdict mapped to VerificationResult
evidence-gated (plausible>=0.6 accepted; unsupported/refused rejected; infra
failure never auto-rejects).
- 6.5 cli capability gate (hx_hunter/hx_verify): selects HxScanOrchestrator /
judge only in standard mode with a configured model AND has_harnessx(); otherwise
falls back and records a {stage,reason:harnessx_extra_unavailable} degradation in
the manifest (new append-only write_manifest degradations kwarg). With no model
configured the manifest stays byte-identical.
- pyproject: mypy override ignore_missing_imports for harnessx.* (absent in the
type-check env).
Verified the HarnessX path constructs against the real extra in a throwaway venv
(build_sast -> HarnessConfig; orchestrator -> Harness; build_judge_prompt/
parse_judge_response/verdict mapping) — only the live agent.run()/provider.complete()
LLM calls are offline-untestable and stay gated. 109 tests pass (2 construction
smokes skip without the extra), ruff + mypy clean, detection gate 93.6%/0% intact.
Tasks 6.1/6.2/6.4/6.5 done; 6.3 (deterministic stages as HarnessX processors) deferred.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
