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
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: 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
