Decision-engine task 6.7 (partial). After the advisory-fix harvest (memory 724 -> 904 static examples),
one compile per evaluable family and an outer grouped 5-fold evaluation of that family's own candidates,
input profile v1, priors off, k = 5, cross-fitted Platt, nested operating points (repository-cluster
bootstrap 95%):
family n AUC(s) ADVISORY R / P 'vulnerable' P / R ECE canary
injection 110 0.77 [0.67, 0.88] 0.88 / 0.57 0.84 / 0.36 0.093 0.77
untrusted_dest. 62 0.85 [0.76, 0.94] 0.84 / 0.73 0.96 / 0.66 0.071 0.87
path 35 0.89 [0.79, 0.97] 0.86 / 0.69 0.89 / 0.76 0.119 0.90
output_encoding 44 0.91 [0.82, 0.97] 0.86 / 0.81 0.94 / 0.55 0.045 0.90
deserialization 46 0.90 [0.82, 0.97] 0.83 / 0.76 0.93 / 0.83 0.095 0.93
access_control 37 0.96 [0.90, 1.00] 0.91 / 0.91 1.00 / 0.61 0.076 0.83
BLOCK offered for none: calibration fails everywhere except output_encoding, where no fold reaches a
Wilson lower bound of 0.95. Within paired groups (a function and its own fix) AUC is lower, 0.72-0.93:
every single-label group in the new families holds positives only. Injection learning-curve point,
paired (same program, candidates, folds): memory 724 -> 904, AUC 0.76 -> 0.77 (no injection example
was added). $2.88 metered DeepSeek (ceiling $8; balance 17.73 -> 16.73), $0.0009 embeddings.
Instrument fix: compiled demonstrations now follow the near-duplicate rule (cos >= 0.98) that retrieval
and the pre-call assertion apply; the assertion had stopped the untrusted_destination evaluation.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
The 97 advisory-fix pairs (194 sides) harvested for the decision engine: verify a/b + agree on the 34
untrusted_destination sides and model roles on every side as plane Runs on ax ($0.38 metered, task
budgets $1.49, ceiling $5; DeepSeek balance 17.88 -> 17.73), engine features on the host (frozen
7b17eac, one container, --memory 3g: 54 ran, 58 asked no question, 82 no language model), feature
records for all 194 sides, static memory rebuilt with the merged harvest units: 724 -> 904 examples.
No unit was skipped for the 20 KB inline limit.
Instrument fix: SideReader read only the first pair-kind source, so the first rebuild left all 194
advisory sides `unread` (0 examples). It now reads every pair-kind source; test added.
Leak audit: the static profile flags only engine signals and facts.function_lines (withheld by v1);
the plane profile newly flags verify.flag_b and ms.operation (plane-only, not in any v1 prompt).
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
6.2: the 724 static examples' 616 distinct excerpts embedded with openai/text-embedding-3-small via
OpenRouter, 198,819 tokens, $0.0040 metered (ceiling $1).
6.6, family injection only, memory = every family (at most 2 contrast examples per prompt), input
profile v1: smoke fixed k = 5 (repeat prefix-cache hit share 0.96); one compile (program c1237a49,
proposed instruction 5, balanced Brier 0.128 on 13 C_val candidates vs 0.199 baseline); outer grouped
5-fold, 110 out-of-repository candidates in 29 groups: ADVISORY recall 0.90 [0.82, 0.97], precision
0.54 [0.44, 0.65], AUC 0.74; BLOCK not offered (ECE 0.064 > 0.05; no fold reached a Wilson lower bound
of 0.95); canary re-ask agreement 0.83. $0.91 metered at list price; DeepSeek balance $18.20 -> $17.90.
Instrument fixes found on the way, each with a test:
- memory build: pair sides join the record stored under their harvest unit pin (--units), and only the
built profile's records are read (static and plane shared a key; 85 -> 724 examples);
- retrieval: stage 1 per side (family / contrast) and at most 2 contrast examples, never padded;
- program: the JSON answer contract is part of the signature, appended to proposed instructions
(DeepSeek JSON mode refused a prompt without "json");
- compile: candidate_families for a one-family compile; compile.toml k = 5 from the smoke rule.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
The leak audit found facts.function_lines separating fixed from vulnerable pair
sides (a fix guard lengthens the function: 0.80 untrusted_destination, 0.75
config_secrets). Input profiles may now name single features; v1 withholds the
engine and the function length from prompts, retrieval and the proposer summary.
A withheld feature keeps its instrument block in the distance (state still counts).
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer decision "Neutralise both for v1".
1. Phantom candidates. A pair side's candidates are now only the functions that
side declares (plane/harvest.py `declares`, the excerpt builder's
function_bounds); a labelled function absent from a side is listed under
`absent_on_side` and never asked, and a side declaring none of its verify
family's functions is no verify unit. The recorded harvest units held 58
phantom verify candidates (53 fixed, 5 vulnerable sides). build_features.py
skips label rows whose side does not declare the function and drops their
earlier rows (MemoryStore.drop_rows); rebuilt the pair records from the
recorded signals, no model call: 126 label rows absent_on_side (117 fixed,
9 vulnerable) -- access_control 13, config_secrets 4, deserialization 20,
injection 28, memory 4, output_encoding 16, path 15, untrusted_destination
26 -- so 252 feature rows dropped (1712 -> 1460); no other row changed.
2. Engine withheld. Input profiles in learn/schema.py (`v1` withholds the
engine instrument, `full` withholds nothing); ProgramSpec.inputs defaults to
v1 and compile.toml records it. Neither the engine's state nor its values
reach the candidate, examples, demonstrations or the instruction proposer's
error summary, and the Gower retrieval distance leaves the engine block out.
Stored records are unchanged. design.md Risks records the decision and that
the engine returns only through a pre-registered A/B on assumed-benign and
benign-control negatives.
3. `ousast learn audit-leaks` (learn/leaks.py): per feature and per instrument
state, separation = (pairs the signal alone puts on the right side - on the
wrong side) / pairs, over all pairs and per family; flagged at >= 0.20 over
>= 10 pairs. Counts under
benchmarks/measurements/2026-10-01-decision-engine-leak-audit/ (before and
after the rebuild). After: facts.function_lines (0.80 untrusted_destination,
0.75 config_secrets, 0.26 injection; higher on fixed), verify.turns_mean
0.67 config_secrets, eng.* 0.35-0.40 output_encoding, verify.flag_a/votes/
site_offset and agree.final 0.20-0.30 on 10-12 pair families. Before the
rebuild the phantom fixed sides drove state.source/state.roles/ms.flagged
to 1.0 and state.engine to 0.95 on deserialization.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
The pair-corpus engine job finished: 682 of 686 sides (199 ran, 475 read their input
and asked no question, 8 no engine model; the 4 sides of the two family-unknown
pairs were not run). Feature records rebuilt (1712); counts-only record updated.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
`ousast plane harvest` (plane/harvest.py) writes two Runs over a label snapshot:
verify a/b + agree on the verify-family units (both sides of each labelled pair as
an opaque files-only Workspace, candidates outside the hunt's root, no declared
sites; v1 and dev-php pins with repo-facts first) and model roles on the candidate
files (pair excerpts grouped, one Task per population pin). Budgets sum under the
ceiling by construction. New roles task template; the generator re-pins it.
Spent by attribution: verify $2.54 (1961 calls, 150 units, 182 hunts per pass),
roles $1.40 (1034 calls, 799 files, 0 unclassified chunks); DeepSeek balance
19.63 -> 18.23. Engine on the pair corpus (benchmarks/learn/engine_pairs.py,
frozen e6ed785, one 3g container) is PARTIAL: 384 of 686 sides at commit, still
running; unfinished sides stay missing. benchmarks/learn/build_features.py built
1712 static+plane records into the memory store; counts-only record under
benchmarks/measurements/2026-10-01-decision-engine-harvest/.
source/roles parts are `none`, not `failed`, for a language without a declaration
pattern (C). ops/ax/README: Substrate golden-actor disk leak and the inline
Workspace limit (~20 KB).
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
97 advisory-fix pairs, one repository each (309 advisories examined, 212
rejected with reasons in research-2026-10-01.md): a public fix commit
distinct from its parent, the advisory-named function declared on both
sides with a changed body. Pointer pairs: materialize.py reads one file
per side from a shallow blobless fetch into the pair cache and verifies
them with the label builder's matcher (verification.json, bytes read).
learn/sources.toml lists the catalog as the advisory-fixes source. Label
snapshot: negative repository groups untrusted_destination 8->25, path
3->21, output_encoding 1->22, deserialization 0->21, access_control 0->20;
all five reach the per-family floor. Five pairs on reserved repositories
were dropped after the reserved-population guard flagged them.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
A pair now gives a fixed-side negative only when the fixed side's source declares the
labelled function (the excerpt builder's matcher; a name on a code line where the matcher
resolves neither side), or when a deterministic rename at a distinct fix commit replaces it
(one new function, same parameter list, changed body: provenance fixed_side_moved). Anything
else is counted under pair_fixed_side.absent_fixed_side, not labelled.
108 pair labels lose their negative: 80 seeded pointer pairs whose fixed side is a guard
helper of the same commit, and 28 Real-Vuln-Benchmark traps whose fixed side is a sibling
handler of the same snapshot (4 of those handlers are labelled positives themselves). The
memory build no longer substitutes the catalog's fix_function on the fixed side. The pair
cache is an allowed label-source tree so pointer sides are read through the guard.
Snapshot: benchmarks/measurements/2026-10-01-decision-engine-labels (counts only).
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
learn/compile.toml: the pre-registered spec -- balanced Brier score of the
raw score on C_val (cells by family x label, repositories capped at 5,
unsure = 0.5, ties within 0.005 to fewer prompt tokens), seed, the
compile split (25%, boot 60/40, compile-split-only memory), bootstrap
(150 candidates, confidence >= 0.7, 4 sets of 3), search (6 proposals +
baseline, 20 errors shown, 60 validation candidates), k = 1, the
retrieval settings, and the $3 / 1,200-call ceiling.
learn/compile.py: bootstrap few-shot keeps only right, confident
rationales that cite a line and name no repository, path or CVE/GHSA id;
seeded demo sets (positive, negative, any; families mixed) with
other-framework alternates; one proposal call; 7 instructions scored with
set 1, the best with sets 2-4. The artifact is canonical JSON stored as
programs/<program_id>.json, program_id = sha256 of the artifact without
its id; it records the spec sha, memory/compile-groups/folds digests and
the response-cache digest, with spend priced from cached usage, so a
rerun from the cache is byte-identical at $0 (tested on FileStore and
MinioStore through the fake client; real MinIO gated as today). A ceiling
ends the compile `unfinished` with no artifact. packageable() keeps only
permissively licensed demonstrations (MIT, BSD, Apache-2.0, ISC, zlib),
swapping in a permissive alternate or dropping, under a new program id.
learn/calibrate.py: Platt scaling on logit(s), pure Python Newton, per
family / pooled with family intercepts / insufficient data by the floors;
cross-fitted (fold j's map is fitted on the other folds only); the check
(5-bin reliability, slope and intercept with repository-bootstrap CIs,
ECE <= 0.05), ADVISORY only when it fails.
learn/evaluate.py: predictions under each fold's boundary, families below
the floor never asked; operating points read from the calibrated,
cross-fitted predictions of the other folds (BLOCK: Wilson lower bound
>= 0.95, never on a majority unsure; ADVISORY: recall >= 0.90);
repository-cluster bootstrap intervals; strata with "insufficient data";
>5% parse failures is an instrument failure; explanations keep only
signals the record holds; version mismatch names the fallback reason;
the memory-size learning curve.
`ousast learn compile|evaluate|curve`: paid runs need --budget-usd (exit
3 at the ceiling), --replay-only runs from the cache. Nothing was spent:
every test and the dry run used scripted clients.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
learn/program.py: the declared signature (code, signals, roles, family in;
verdict, family, confidence, rationale with cited lines out), the
baseline instruction, rendering as [system: instruction + demonstrations]
-- byte-identical for every candidate, so the provider's prefix cache can
hit -- then [user: retrieved examples with their labels, the candidate
without one]. Signals pass validate_x first (no EXCLUDED_FIELDS, missing
instruments in words). Answers are one JSON object; one that does not
parse or cites no line inside the excerpt is asked once more at
temperature 0 and otherwise becomes unsure/parse_failed, never
not_vulnerable. Classify(k): sample 1 at 0.0, the rest at 0.7; score s =
mean p_j; majority verdict, ties unsure; a majority unsure cannot BLOCK.
Caller caches each response as responses/<sha>.json keyed by (model,
Model parameters digest, messages, temperature, sample, json_object) and
replays at $0; spend is priced from the recorded usage once per distinct
request, so a replay reports the same usd. Program.decide asserts the
evaluation boundary on the rendered ids before any call.
MeteredClient.complete passes temperature/logprobs through (only when
set); DeepSeekChatClient sends temperature beside thinking=disabled.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
learn/retrieve.py: stage 1 is a per-instrument Gower distance over the
profile's allow-listed features with explicit missing (both ran: mean
feature distance; differing states 1; same non-ran state 0; null in one 1;
priors dropped with priors=off), n1 = 40 with seeded tie-breaks; stage 2
re-ranks by r = lam(1 - D) + (1 - lam)cos, signals_only without the
target's vector; the balance rule takes k_ret = 6 (<= k/2 per label, <= 2
per group, <= 2 other families; `short` recorded). eligible() is the only
way into a prompt: not the target's group, not an evaluated group, not the
held-out source or framework, and outside deployment not a near-duplicate
(cos >= 0.98). assert_boundary repeats the check on rendered ids;
demonstrations of a held-out framework swap to their alternates;
deployment_fold excludes a scanned repository that is a corpus group;
seed_memory never hands a case its own group.
learn/folds.py: compile split (25% of groups per primary family, 60/40
boot/val), outer grouped 5-fold stratified by family and label mix,
leave-one-source-out, leave-one-framework-out (insufficient data below 10
groups), assert_disjoint and a folds digest; all seeded.
Tests: test_learn_retrieve.py (hand-computed Gower, lam 0/0.5, balance,
near-duplicates, test_boundary over outer/LOSO/LOFO folds with
demonstrations), test_learn_folds.py.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
plane/models/openrouter-embedding.yaml: openai/text-embedding-3-small via
OpenRouter (embeddings only), input_per_m 0.02, cache/output 0, egress
openrouter.ai; `openrouter` joins the provider extensions.
plane/budget.py MeteredEmbeddingClient: prompt_tokens priced from the
Model manifest, usd/calls ceilings stop the next call, a priced call that
reports no tokens raises UnmeteredUsage (a silent zero is an instrument
failure). OpenRouterEmbeddingClient keeps one usage row per call
(parse_embedding_payload); its api key is out of repr.
learn/embeddings.py + `ousast learn memory embed`: excerpts embedded in
batches into embeddings/<model-slug>/<excerpt_sha>.json (float32 base64),
cached by content hash so a rerun makes no call; the declared dimension is
checked; --dry-run estimates tokens and cost without a call; a paid run
needs --budget-usd and exits 3 (unfinished) at its ceiling. Not run against
the provider: the store embedding waits for its budget go-ahead.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
learn/excerpt.py: the labelled function at its pin as numbered lines, bounded
(80 lines / 4,000 chars for candidates, 40 / 2,000 for memory examples),
centred on focus lines; advisory ids anywhere and comments with security
wording (labels.security_reason, multilingual) become <redacted>; owner,
owner/name, path and commit ids are replaced; a delta unit appends the
base->head diff (<= 40 lines). C/C++ and other brace languages without a
repo-facts declaration pattern use a brace matcher (346 c_cpp pair rows
had no excerpt without it).
learn/examples.py + `ousast learn memory build`: label + feature record +
excerpt -> `example` rows (no rationale), excerpts as content-addressed
blobs; refuses origin: user and sources outside sources.toml (exit 2);
conditional labels skipped; exit 2 when >10% of a source's recorded
candidates have no excerpt. A fixed side that was read but no longer
declares the labelled function is counted apart (absent_fixed_side): a
label defect, not an unread clone.
plane/memory.py: `example` in KINDS; put_blob/get_blob/blob_names on the
MemoryStore base, so FileStore and MinioStore share them (tested on both,
MinIO through the fake object client).
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
In-house DSPy-style program (retrieve -> classify(k)) on the metered client;
example store with signal-profile Gower neighbours re-ranked by code embeddings;
balanced Brier metric, bootstrap few-shot and instruction search on a compile
split; Platt calibration on other folds; cached replay; tasks 6.1-6.7 with paid
steps flagged for go-ahead.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer: data will not scale; the decision engine is an AI classifier, not ML,
with local memory, DSPy style. In-house on the metered client; retrieval by signal
profile re-ranked by code embeddings; compiled (bootstrap few-shot, instruction
search) per training fold; retrieval never crosses the evaluation boundary;
confidence calibrated on held-out repositories. Design and tasks 6+ to revise.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
learn/sources.toml is the fail-closed source list: every read goes through one guard
that refuses an unlisted path or an [[excluded]] file name before opening it, and a
source whose evaluation record is missing cannot label. Population v3 is named only
as excluded, by file name, and is never opened (tested with a v3-named fixture and
open/read_text tracked).
learn/labels.py and `ousast learn labels` apply the design's rules: v2 declared
sites and v1 fix-range functions positive, the same function on the fixed pin
negative only when the fix changed it, introduce/repair delta units, benign control
pushes as conditional negatives; pairs vulnerable/fixed (unscorable and title tier
out, title as a sensitivity arm); dev-php recipe known functions; recorded
adjudications with their reasons. v2 rows are labels marked design_informed, as the
design says. Repository groups: normalised owner/name, merged by shared CVE/GHSA,
shared fix commits and [[alias]] rows.
assumed_benign (maintainer decision): ordinary commits of the population
repositories, >= 90 days from any fix, no later security commit touching their
files, weight 0.5, conditional, reported separately and never merged with verified
negatives. The security filter is multilingual (CJK, Korean, Russian, European and
other languages) plus CVE/GHSA/CWE references and dependency bumps; widened after
reading accepted pushes (validation, passwords, guards).
First snapshot, counts only (benchmarks/measurements/2026-09-30-decision-engine-
labels/labels.json): verified pin labels in positive/negative repository groups
injection 44/41, untrusted_destination 35/34, deserialization 20/20 (per-family),
output_encoding 15/15, path 15/14, access_control 12/12 (pooled), config_secrets 5/5,
memory 5/5 (insufficient). assumed_benign: 969 pushes in 27 groups from 36,000
commits scanned.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Wrapper inference (learn/roles.py, no model): a function whose parameter reaches a
call already in the role set becomes a derived sink (or a sanitizer when it returns
the cleaned value) of the same operation, at depth + 1; a function returning a
source's value becomes a derived source. The role set starts from language-level
entries only (priors off by default, optional), grows to depth 3, and reads
vendored dependency source when present, so a vendored database class reaching
mysqli makes `->query` a sink without a framework table. A name the repository
declares more than once becomes a role only when every definition derives it.
Model roles (plane/tasks/roles.py): the model_sinks.py classification moved into
the package and extended to sink/source/sanitizer/guard/dispatch, per file chunk,
metered and resumable, client injected; a failed chunk is unclassified, never
"no roles". vocabulary_overlay merges inferred and model roles over the
language-level facts in the shape taint_specs takes, origin in the entry id.
Features: the roles (static) and model_sinks (plane) instruments are filled.
On the recorded validation-46 facts (15 cases, 14,368 product files, 87 MB read):
1,999 wrapper roles (1,753 sinks, 157 sources, 89 sanitizers; depth 1/2/3 =
973/651/375), 14 of 15 cases with at least one; the WordPress gopay plugin has
none with priors off, which is what model roles are for.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
ruleset/frameworks.toml lists 19 framework and library ids (wordpress, flask, django, express,
spring, jakarta-servlet, react, vue and 11 libraries), each with its languages, dependency
package names and a symbol lexicon used only by the lint and for stratification.
Every framework/library entry of section 2 is tagged `framework`/`library`, and mixed entries
are split so a tag covers a whole entry (split pieces keep the entry's id and flags, in place):
semantic facts 38 tagged entries (wordpress 6, django 6, flask 4, express 4, jakarta-servlet 3,
pyyaml 2, one each for spring, sqlalchemy, requests, httpx, lodash, node-http-clients,
escape-html, sanitize-html, dompurify, esapi, commons-text, react, vue); the dispatch fact
behind the hook-as-source path is wordpress; the PHP layout row is renamed php_layout and stays
untagged (a product filter). Quick rules: 9 tagged (wordpress 6, flask, pyyaml,
node-http-clients); php-unserialize is split into php-unserialize and php-maybe-unserialize;
the lint also caught WordPress escapers in exclusions and axios/yaml.load in mixed rules, so
those keep their ids as tagged rules and gain a `fallback_for` language-level rule (6) that is
active only when the prior is off.
load_facts and load_ruleset take priors ("all" by default -- today's scan --, "off", or a set
of ids). tests/test_semantic_priors.py: the lint (no untagged entry contains a lexicon symbol
of its language), "off" removes exactly the tagged entries and activates the fallbacks, split
entries keep their language-level calls. Rule and layout counts in three existing tests follow the split and the rename.
Unchanged defaults: test_benchmark_output_is_byte_identical_to_golden_baseline passes, and the
quick findings of all 15 benchmark manifests (full StaticFinding records) are byte-identical
before and after. priors=off on the same manifests: 61 -> 57 findings; the 4 lost are js-ssrf
(axios), php-wpdb-sql-composition, python-ssti and python yaml.load; 5 PHP and 1 Python finding
move to their fallback rule at the same location. Semantic calls with priors off: php 75/92,
python 32/77, javascript 42/70, java 24/41, c 27/27 (the engine-side effect needs Joern and was
not measured).
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
learn/schema.py is the closed, typed allow-list of candidate features: 66 features over
11 instruments (language one-hot, quick rules with 23 mechanism buckets and a prior count,
engine, repo-facts, source span, entry-point distance, roles, delta, verify, agree, model
sinks), two profiles (static 57; plane adds verify, agree and model sinks for 66), schema_version and a feature-set digest.
No repository, path, file, function, rule or commit identity is a feature; site_match,
in_fix_range, fixed-pin alerts, case.json fields and costs are refused. Missing is not zero:
each record carries per-instrument state (ran | none | failed | not_applicable), and every
feature of an instrument that did not run is null.
learn/features.py builds records on the host (build_for_scan, whole repository or base..head
delta); plane/tasks/features.py builds them per case in a Run from the delivered artifacts,
dropping site_match and in_fix_range on read and ignoring fixed-pin alerts. The generator
adds a model-free `features` entry per case before `remember`, which stores the records as
`features` rows (re-validated). memory KINDS gains features, label, decision, experiment,
arm_outcome and experiment_result. The committed validation-46 manifests are regenerated
(entries appended; the first Run's entries are unchanged). The runner image must be rebuilt
and re-pinned before a Run executes the new task.
Proof on real artifacts (scratch store, not committed): the recorded validation-46 run gives
75 feature records over 15 cases (43 verify candidates plus vulnerable-pin alerts), each
case's vulnerable pin exported and read (0.5k-9k files); remember stored 75 features rows
beside 43 verdict, 84 alert, 61 unit_cost, 15 facts and 8 coverage rows.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Three instruments scored separately at one analyzer commit (quick PHP rules
with a fixed rule-to-family table, the Joern engine, the plane pipeline
triage -> verify a/b -> tie-break -> 2-of-3), protocol v2's six pins, one
matching rule for all six families (declared site or fix range, same family;
evaluate.matches_v2 unchanged), per-family meaning of a match, the M4 gates
copied from protocol v2 and applied per instrument, a $60 total / $6 per case
model-spend ceiling, and the retune rule.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
freeze.py, extended for v3 and still reading only git history, file lists and
fix diffs:
- a file the advisory names without a function resolves to the function(s)
the fix's diff changes at the vulnerable pin (maintainer's decision 2),
recorded with its basis (advisory-named / resolved-from-fix-diff); a new
helper inserted at file scope adds no site;
- an inferred fix must change a declared site function, else the case drops;
- a single-language population gets a strict benign proposal (PHP change,
merges judged with all they bring in, pre-vulnerable history as fallback).
All 15 cases verified, none dropped (pagible and podlove fixes confirmed).
Nine sites in eight cases resolved from diffs; five benign proposals
replaced by hand.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Assumed-benign pushes mined from non-security commits as a separate label source
(maintainer decision). Ten tasks in the design's commit order; the harvest,
exp-001 and the v3 check wait for budget go-ahead.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer: hard-coded application specifications are problematic; WordPress is
not the only PHP application, same for Python and JS. Language-level knowledge
stays; framework roles are inferred per repository; existing framework entries
become tagged optional priors kept only by A/B evidence; leave-one-framework-out
evaluation.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer's selection 2026-09-30, "Recommended 16, minus v2 mentions": the
research's rule-based 16 with Winter, elFinder and Easy!Appointments replaced
by the next candidate in their family by the same rule (named fix first, then
newest first), v2-mentioned alternates (Grav, Backpack CRUD, Koel) skipped.
Winter -> mantisbt-tokenizer-eval-rce; elFinder -> pagible (inferred fix, to
be confirmed at freeze); Easy!Appointments -> empty slot (no remaining
untrusted_destination candidate), so 15 cases are reserved.
The reservation guard now covers v3: all six families, a declared-language
population, status "reserved".
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer: the tool will never run against the test cases it is trained with;
its purpose is a repo safety net. Repository-grouped folds, repository-agnostic
features, BLOCK/ADVISORY operating points for changed code, out-of-repo
calibration, adaptation kept separate.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer direction: A/B testing to extrapolate detection instead of hard-coding
it, and a trained decision engine. Instruments become signal producers; labels
from ground truth with provenance; a simple calibrated engine with population
folds; experiments as plane Runs; extrapolation with intervals, checked once on
frozen v3; the engine is what scan/pre-push/plane report.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Quick mode had no PHP rules, so a PHP quick scan read the files and reported a plausible zero.
ruleset/php/rules.toml adds 13 line/statement rules built on semantic/php.toml's sinks, sources and
sanitizers (sql/wpdb, command, code_eval, unserialize, echo, file_inclusion, filesystem,
outbound_request, redirect). 9 are enabled and 4 are shadow (sql-call-composition, code-eval,
unserialize, xss-echo-input). A rule is shadow when it fired on the corpus but never at a known site.
precision_estimate is the measured lower bound.
Measured in-sample on the development corpus only (pmpro introduce/repair/benign, population-v1 PHP
cases; population-v2 was not read). Known site hit on the vulnerable pin in 4 of 5 cases (LearnPress
missed). The hit disappears on the fixed pin for pmpro and YesWiki. Numbers, per-pin files/bytes read
and the engine comparison are in benchmarks/measurements/2026-09-30-php-quick-rules/.
- fixtures php-vulnerable / php-benign plus manifests: ousast benchmark covers PHP
(9/14 reported, 13/14 including shadow; the benign twin records 2 known false alerts)
- PHP `#` comments are comments; `#[...]` attributes are not
- the alerts task now reports PHP as quick coverage; alerts-engine keeps running the engine for PHP
next to the quick rules (ENGINE_ALONGSIDE_QUICK)
- other languages' benchmark findings and metrics are unchanged
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Quick mode has rules only for c/cpp, groovy, java, javascript/typescript and
python, so every PHP case's "0 alerts" read as clean. The `alerts` task now
records per language of the files read whether quick mode covers it
(derived from the bundled ruleset, not a list): coverage quick | none, with
file counts per pin, and `uncovered`. `remember` turns that into `coverage`
memory rows (a new kind; the Run passes the alerts summary.json as input),
loop-measure reports coverage per language, and improve/memory.py's M1/M2
skip a repository+pin whose coverage for the rule's language is only
`none` (counted under `coverage_none` in the proposal's excluded).
`ousast plane alerts-engine <Run.yaml> --source <frozen export>`
(plane/engine_alerts.py) does the alerts task on the host for cases whose
files include a language the engine covers but quick mode does not (PHP):
exports both pins from the case cache, runs the quick scan and the
openultrasast:dev engine container (finding_dump.py, --network none,
--memory 3g, one at a time, per-pin deadline), writes engine findings in
the alerts.jsonl row shape (rule_id engine:<family>, source engine,
in_fix_range from case.json) with a summary (files read, questions,
completed, seconds, degradations per pin), and marks the task done via
reconciler.mark_done. No result, no files read, or questions == 0 fails
loudly and leaves the run untouched. Refuses a git checkout as source.
Runner image rebuilt and re-pinned (sha256:ebb40a9a...); validation-46
regenerated (digest only). Live, recorded in
benchmarks/measurements/2026-09-30-php-engine-alerts/: froxlor 366 files,
130/130 questions per pin, 6 alerts per pin, 0 in the fix range; wp-gopay
19 files, 101/101 questions, 0 alerts. The loop: 32 run rows (coverage 8,
php=engine), 0 proposals, gate no_proposals, $0.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
README: "Configuring the HarnessX agentic plane" becomes "The agentic
plane (ax)" (ousast plane run|status|doctor|remember, per-task Model,
budget and egress, pointer to ops/ax/README.md, one tagged retirement
line); "The HarnessX self-improving cycle" becomes "The self-improving
cycle", and its MetaAgent paragraph is replaced by the memory proposer
(`ousast improve --memory`, provenance, train-on-test guard). The fusion
panel_model/decider_model lines and the [harnessx] spend caps are gone.
The README reference test now passes without its strict xfail.
docs/examples.md section 6 runs the plane; docs/threat-model.md describes
the per-task EgressPolicy, the usd/calls budgets and the start-request
credential path. RELEASE_NOTES.md gets an Unreleased entry: HarnessX
removed, the retired keys and their errors, the silent loss of the
second hunter for users who set [models] hunter, and the plane as the
agentic path.
Dated retirement notes (2026-09-30) head the requirements, design and
brief of harnessx-self-improving-rulesets, learning-harness,
three-stage-scan and openrouter-sast-harness, and follow the spec table
in steering/overview.md; history is unchanged. All tasks are checked, so
spec.json moves to implementation-complete.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Reran benchmarks/harnessx_removal_equality.py record from a git archive
export of b9afb66 (tasks 1-6 landed; same venv freeze hash as the
baseline, harnessx not importable, no API keys, empty config) and
compared it against 2026-09-30-harnessx-removal-baseline:
equal=True differences=0
All 13 output files (pairs.json, benchmark, standard scan, improve
--dry-run) are raw-byte identical to the baseline. The suite map
(suite-map.json) accounts for 29 deleted ids, 2 renamed ids and 83
added ids; each deleted or renamed id cites the design's Tests table and
commit cbadc38 or b9afb66. Every surviving test id keeps its outcome
(1740 passed, 38 skipped incl. the README xfail).
Instrument note: a first record failed test_push_hook twice because the
recording shell's PATH lacked .venv/bin (the hook execs `ousast`); the
same two tests fail at b9afb66 without it and pass with it. The
committed record has .venv/bin on PATH.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Deletes harness_ext.py and hunter_harness.py with their HarnessX-only
tests (the judge path, verify_judge.py, went in task 5). The harnessx
extra, its mypy override and its uv.lock entries are removed (uv lock
drops only packages; no remaining version changes). The conftest
fixture becomes assert_cold_import; test_gate's two never-import checks
become one allow-list (stdlib, openultrasast, yaml) over the governance
planes' module-level imports, and the skipped live-extra tolerance test
is deleted. Audit rows for the two modules are removed, stage_processors
is reworded, and the module-audit manifest is regenerated (no orphan).
test_removed_plane_references implements Req 2.4 over src, tests and
pyproject.toml: a match must carry a `retired 2026-..` tag (the spec's
own slug is not counted). The README part is a strict xfail until task 8
rewrites the README's plane sections.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
The standard scan now runs run_hunter_pool, verify_findings and the
deterministic fuse_findings directly; the HarnessX hunter orchestrator,
the LLM judge dispatcher (verify_judge.py, deleted with its test) and the
LLM fusion panels are gone, and so are their extra-absent degradations.
host_under_harnessx is dropped from stage_processors.
config: [models] verifier and [fusion] panel_model/decider_model raise
RetiredConfigError naming the plane replacement (the CLI prints it and
exits 2); a [harnessx] section loads with one warning and is otherwise
ignored. harness.json keeps "verifier": null. Tests that asserted the
fallback are renamed to the behaviour they assert; the module-audit
manifest drops verify_judge. Nothing outside the two modules deleted in
task 6 imports harness_ext or hunter_harness.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
`ousast plane workspaces --validation-set ... --loop` appends the loop to a Run:
per case `alerts` (plane/tasks/alerts.py: the quick-mode scan on the vulnerable
and the fixed pin, a fixed-pin Workspace per case, alerts attributed to their
enclosing function by the repo-facts patterns, `in_fix_range` from the fix's
old-side and new-side ranges, failing on an unread tree) feeding `remember`,
then `memory-snapshot` -> `loop-measure` -> `loop-propose` -> `loop-improve`
(plane/tasks/loop.py). Every new task binds no Model and has budget
{usd: 0, calls: 0}. The snapshot is the store's rows after the train-on-test
guard, written on the host by memory.seed (a sandboxed run of it fails loudly);
propose runs M1/M2 against this repository at the commit under test; improve
runs one unchanged evolve.run_round and delivers gate.json, journal.json, the
sidecar, rule_policy.json only when accepted, and proposal_outcome rows that
the host ingests. Adoption stays a maintainer commit.
`remember` is now a Run entry per case (the image contains it), with the facts
row keyed by the image the facts were computed on (repo-facts records it in
its summary; unknown -> the row is omitted, never guessed). Alert rows carry
their own pin; improve/memory.py counts a repeated alert once.
Runner image rebuilt (+0.39 MB: only the new modules; the quick scan needs no
extra and the rulesets were already in the package), pushed, plane/ re-pinned
and regenerated (validation-46: 105 entries). Fixture test runs the whole loop
graph through the fake ax, whose new `exec` mode runs the real task modules.
Live: one loop Run over three validation-46 cases on the local ax, recorded in
benchmarks/measurements/2026-09-30-harnessx-removal-loop/ (all done, $0 new
spend, 0 proposals, gate verdict no_proposals).
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
improve/memory.py: rules M1 (enabled->shadow on repeated false alerts across
>=2 repositories, no agreed or site_match hit) and M2 (shadow->enabled on
>=2 agreed declared sites no enabled rule catches); M3 disputed families are
advisory (signals.json) and never reach the validator. The train-on-test
guard drops rows of --qualify-population, gated benchmark cases and holdout
pair repositories before any rule sees a row. run_round(proposals=...) adds
memory edits after the benchmark's through the unchanged validator and gate;
provenance (row ids, object keys and versions, counts, exclusions, index
digest) goes to memory_proposals.jsonl and the journal's evidence field;
proposal_outcome rows are written back after a non-dry run.
ousast improve --memory is opt-in; default improve output is byte-identical
to the task-1 baseline.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
plane/memory.py: the MemoryStore interface with FileStore (index.jsonl, facts by
content hash, repos/<host>__<owner>__<name>/<pin>.jsonl, idempotent ingest,
deterministic row ids, refusal below 1 GiB free naming the path) and MinioStore
(object metadata and tags, S3 Select pushdown probed once per process with a
logged local fallback, object version ids on every row read, versioning and
runs/ lifecycle helper, presigned URLs). OUSAST_MEMORY selects the backend;
MINIO_* settings come from .env; the minio SDK is the optional extra `minio`.
plane/tasks/remember.py: model-free rows (facts, verdict, unit_cost, alert)
from a case's artifacts; remember_run computes them on the host for a recorded
run without writing into it. reconciler.mark_done (477 lines total), seed
before and ingest after `ousast plane run`, `ousast plane remember <run>`.
The generator annotates each repo-facts Task with openultrasast.io/memory-key
(repo, pin, candidates digest, image digest) and the Run with its population
and split; plane/ regenerated.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Equality script (design section 3) records suite outcomes, `ousast pairs --json`,
the python-vulnerable quick benchmark, one standard scan and `improve --dry-run`
from a frozen export with the harnessx extra absent and no model reachable.
Two independent records are byte-identical (determinism.json).
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer: use MinIO features including its JSON support; a server exists.
One MemoryStore interface with FileStore (laptop, tests) and MinioStore (the
store of record for the separate Kubernetes deployment): tags for indexing,
S3 Select pushed-down queries probed with a local fallback, versioning for
proposal provenance, lifecycle for raw outputs, presigned URLs and bucket
notifications kept for the follow-on that removes the host receiver.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
