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
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
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
Agent Substrate's egress gateway denies an actor without an EgressPolicy, so
no task could deliver its artifacts or reach a model. egress.py grants each
task exactly: http/80 to ousast-receiver.ax-system.svc.cluster.local, and
TLS passthrough/443 to the bound Workspaces' https Git hosts and the bound
Model's openultrasast.io/egress-hosts (api.deepseek.com for deepseek-flash).
No IPs, no wildcards, no https (MITM) rules.
The reconciler writes the policy with kubectl-ate between ax apply and
resume, reads it back, waits OUSAST_EGRESS_SETTLE (11 s, the gateway's
cached deny), and after ax delete task confirms the policy went with its
actor (kubectl-ate has no delete verb; the store cascades).
The receiver is a selector-less Service (ops/ax/receiver-service.yaml.tmpl,
applied by up.sh together with the no-build agentgateway egress gateway)
on port 18090 of the host. Measured live: the gateway checks the policy on
Host but connects to the address the actor dialled, so the runner dials the
Service's ClusterIP (OUSAST_ARTIFACT_DIAL) with the receiver's name as Host.
The runner now labels its delivery with the Run's task name (OUSAST_TASK),
which is what the receiver accepts; the ax Task name was refused with 403.
The Receiver moved to egress.py to keep reconciler.py at 460 lines.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Agent Substrate's golden-snapshot boot runs the same image with the same task env,
so the runner no longer starts its command by itself: after the workspaces are
ready it waits for POST /ousast/v1/start (run/task checked, once per boot, refused
after a delivered run). The reconciler sends it through svc/atenet-router with
ate-target-actor, which never addresses the golden actor (plane/router.py), and the
bound Model's secretKey variable travels only in that body, into the child's env.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
The first live run on the local ax cluster and ax's source (docs/runner.md,
docs/sandbox.md, internal/controller/reconciler.go, internal/server/server.go)
showed a lifecycle the task-3/4 code did not model:
- `ax apply` creates a Task Suspended (Ready=False, TaskSuspended); it runs only
after `ax resume task`. The first resume of a new image can fail with
DeadlineExceeded while Agent Substrate builds the golden snapshot, and ax's
server then stores phase Failed; a later resume succeeds.
- ax has no Completed phase and never reads a command's exit back.
- The runner is PID 1 and must keep serving after the command exits; stop and
suspend deliver SIGTERM, to be forwarded to the command's process group.
Our runner imported the task in-process and exited, which stopped the
container ("Container guest is stopped") and made every resume time out.
- ax's controller probes /readyz?check=workspace; the query string made our
handler answer 404.
Runner (Req 4.1-4.4): spec.command runs as a child
`python -m openultrasast.plane.tasks.<name>` in its own process group, cwd the
first workspace, AX_METADATA_URL set; ax's metadata paths are served. After the
child exits the runner reads summary.json (a crash without one becomes failed
with the exit code and stderr tail), delivers the output directory, writes
/workspace/.ousast-state/<task>.done.json and serves until SIGTERM (forwarded
to the group, SIGKILL after OUSAST_TERM_GRACE, exit 0). A boot with the marker
never reruns the command; a failed delivery is retried alone. Workspaces are
prepared on the first boot only. AX_RUNNER_EXIT_AFTER_COMMAND=1 is the
unit-test escape.
Reconciler (Req 2.2, 2.3, 3.1-3.3): `ax resume task` after apply, retrying
DeadlineExceeded/Unavailable with backoff up to OUSAST_RESUME_TIMEOUT (900 s);
completion is the artifact delivery; Failed after a resume went through, a
vanished task, or no delivery within OUSAST_TASK_TIMEOUT (7200 s) fail the
task with ax's condition message; `ax delete task` follows. Ax gains resume()
and phase() returns the condition message. doctor moves to plane/doctor.py
(tasks.md task 0 allows the sibling) to keep reconciler.py under 500 lines.
Tests: the fake ax follows the real lifecycle (Suspended, a failing first
resume that leaves Failed, Running forever, delivery only after a resume went
through), plus Failed-with-message, vanish, resume-timeout and task-timeout
scenarios; runner tests cover the child's cwd/group/metadata URL, stay-up and
SIGTERM in a real subprocess, SIGKILL of a SIGTERM-ignoring group, and the
marker. The opt-in live test now checks resume, Running, suspend and a second
resume instead of a Completed phase. design.md and tasks.md state the
lifecycle; the module audit manifest counts plane.doctor.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Model deepseek-flash (declared provider extension, endpoint prices in
parameters, secretKey naming the DEEPSEEK_API_KEY secret), digest-pinned
Task templates repo-facts / verify / agree, and a generator
(plane/generate.py, `ousast plane workspaces --validation-set ...`) that
emits per case the vulnerable-pin Workspace, a files-only inputs Workspace
(recorded candidates after the recorded triage, declared sites, triage),
per-case Tasks and one Run validation-46: facts -> verify a, verify b ->
agree for the 15 cases, 46 candidates (43 kept), budgets 1.5x the recorded
per-pass share. A Run entry cannot add a binding or env, and Run.inputs
names only task artifacts, so the case is fixed by per-case Tasks.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
ai-service-plane task 3, Requirements 4.1-4.4 (with 2.2 and 2.3 honoured by the
exit-code contract):
- src/openultrasast/plane/runner.py: the ax-task-runner entrypoint. Reads the Task
from AX_TASK_YAML and the bound Workspaces from AX_WORKSPACES_YAML (task-1 loaders),
serves /healthz and /readyz on port 80 (AX_RUNNER_PORT; 0 picks a free port,
AX_RUNNER_HTTP=0 disables it for unit tests), materialises each workspace at its
bound path (spec.files verbatim; spec.git at <path>/<name>, exported from the
OUSAST_CASE_CACHE checkout whose origin matches, else a shallow clone at the pin;
a repo already present on a resumed volume is kept) and only then answers 200 on
/readyz. Runs spec.command by importing openultrasast.plane.tasks.<name> with the
remaining elements as argv; sets OUSAST_WORKSPACE_DIR from the first binding when
the Task's env leaves it unset. A crash writes summary.json with status failed and
the traceback. When OUSAST_ARTIFACT_URL is set, the whole OUSAST_OUTPUT_DIR is
POSTed as an uncompressed tar (Content-Type application/x-tar, X-Ousast-Task,
X-Ousast-Run) with three attempts and backoff; a failed delivery rewrites
summary.json to failed and exits 2. Exit codes: done 0, failed 2, unfinished 3.
- plane/Dockerfile.runner: python:3.12-slim + git, `pip install .` (no extras), a
shell wrapper at /usr/local/bin/ax-task-runner exec'ing the module; built and
loaded into kind by ops/ax/up.sh, not by the tests.
- tests/test_plane_runner.py (+ tests/plane_stub_task.py registered as the task
module): env contract on a files workspace, readyz 503 then 200, delivery to a
local receiver with the headers and tar checked, delivery failure -> failed and
exit 2 after three attempts, crash -> failed with traceback, exit codes per
status, case-cache export at the pin, shallow-clone fallback, resume keeps the
checkout.
- model/audit.py: SPEC_OWNED row for plane.runner; module-audit measurement
regenerated (122 modules, no orphans, note kept).
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Requirements 3.1, 3.2, 3.3, 3.4, 3.5, 2.2, 7.1, 7.3 of ai-service-plane.
- plane/reconciler.py (492 lines, test-counted): a Run's tasks submitted to ax
in dependency order through the injectable `ax` CLI (apply / get task /
delete task), serialize labels never overlapping, --workers, state.json with
pending|running|suspended|done|failed|unfinished, a PID lock per run, done
tasks skipped on rerun, unfinished tasks holding their dependants, a failed
task halting the run. Budgets, inputs, output dir, artifact URL and the bound
Model (annotation openultrasast.io/model; identity and priced parameters) go
to the task as env in the rendered Task; consumed artifacts travel as files
of a generated Workspace bound under /workspace. A threaded HTTP receiver
takes each task's tar delivery, validating run/task and rejecting traversal.
Outcome comes from the delivered summary.json (done only when every unit
finished), never from ax's phase alone. status prints the token attribution
table per task and Model from the summaries' usage fields (n/a, never an
estimate) and writes attribution.json; doctor runs the four host checks.
- plane/workspaces.py: one Workspace manifest per case pin of a population.
- CLI: ousast plane run|status|doctor|workspaces.
- tests/test_plane_reconciler.py: a fake ax executable drives the DAG;
tests/test_plane_ax_live.py (marker ax) applies ops/ax/smoke-task.yaml on a
live cluster and skips unless doctor passes.
- Module audit rows and regenerated manifest.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Requirements 5.3, 2.1, 2.2, 2.3, 7.2 of ai-service-plane.
- plane/tasks/verify.py: the batched hunt of verify_batched.py moved into
the package (system prompt, one hunt per file, up to 6 candidates, 6
steps, candidate-anchored sites). New against the reference: each
candidate's known callers from facts.json in the prompt (capped at 8),
tool turns and usage per hunt in units.jsonl, per-unit resume (rows
with "error" are re-asked), pass label from env, MeteredClient wrapper:
BudgetExhausted -> unfinished (exit 3), AccountError -> failed with the
provider message (exit 2); summary.json is written last, names the bound
Model and the summed usage, and is done only when every unit finished.
- plane/tasks/agree.py: verify_batched.agreement over two pass outputs,
agreed.json / disputed.json, per-candidate cost and turns, declared-site
match via benchmarks/independent/evaluate.matches_v2 loaded by file;
metrics block in summary.json; no model calls.
- tests/test_plane_verify.py: a scripted usage-recording hunter client;
callers line, resume, 402, budget stop and resume, model and summed
usage, unpriced usd None, both main() env contracts, agreement metrics.
- model/audit.py + module-audit manifest: SPEC_OWNED rows for plane.tasks,
plane.tasks.verify, plane.tasks.agree; manifest regenerated.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
plane/tasks/repo_facts.py computes, from source text only and without a
model or git, the product files of a workspace (tests, vendored trees,
node_modules, dist/build, minified bundles and dot-directories excluded via
the layout facts plus the model_sinks.py filter), the functions each file
declares (Python, PHP, JavaScript/TypeScript, Java, Go, Ruby declaration
patterns extended from sink_candidates.py) and, per candidate function, the
call sites in other product files with path, 1-based line and enclosing
function, capped at 40 and sorted (Requirement 5.1). Output is byte-identical
across runs over the same tree (Requirement 5.2).
Under the task contract the unit is one file: a row goes to units.jsonl as
each file finishes and a restart reuses the rows computed for the same
candidate set (Requirement 2.2); facts.json is assembled from the rows and
summary.json is written last, refusing `done` unless every unit finished.
The entrypoint reads OUSAST_WORKSPACE_DIR, OUSAST_OUTPUT_DIR and optional
OUSAST_INPUT_CANDIDATES only (Requirement 2.1) and exits 0 on done, 2 on
failed with the traceback recorded in summary.json.
tests/test_plane_repo_facts.py: a three-file tree with a tests/ file, a
vendored file, a node_modules bundle and a dot-directory; cross-file callers
with line and enclosing function, same-file and declaration lines excluded,
the 40 cap, byte-identical second run, resume that recomputes only the
missing unit, a crash that leaves `failed` with the unit counts and resumes
to `done`, and the declaration patterns per language.
The two new modules are registered as spec-owned in the module audit (their
importer, the runner, lands in task 3) and the audit manifest is regenerated.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Add openultrasast.plane.manifests: frozen dataclasses and loaders for ax's
Task, Workspace and Model kinds (ax.io/v1alpha1, ax's fields only) and the
Run kind (openultrasast.io/v1alpha1: tasks, dependsOn, inputs, outputs,
budget, serialize). Every violation raises ManifestError naming the kind,
metadata.name and field path: unknown fields at any depth, wrong apiVersion
or kind, a provider outside google/anthropic unless deepseek is declared via
the openultrasast.io/provider-extension annotation, a Run with a dependency
cycle, an input naming no task, and a Run task naming no loaded Task.
load_manifests reads multi-document YAML files and indexes by kind and name.
PyYAML becomes the one declared core dependency; the two zero-dependency
guards now pin exactly that, and the module audit registers the plane
package under the ai-service-plane spec.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
MeteredClient wraps a ChatClient, sums the provider's usage rows per call,
prices them from a Model manifest's parameters (cache-hit, input, output per
million), and raises BudgetExhausted before a call once the usd or calls
ceiling is reached. A 401/402 or "Insufficient Balance" failure becomes
AccountError with the provider's message. An unpriced model reports usd None,
never zero, and refuses a usd budget; summary() gives the spend block of a
task's summary.json.
The two plane modules are registered as spec-owned in the module audit (their
consumers land in tasks 3-4) and the audit manifest is regenerated.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
48 findings, every 5th reviewed against source: 2 true (both privileged),
8 false; 6 of the 8 share one cause, object fields treated as attacker
input when they are loaded from the database. benchmarks/push/finding_dump.py
writes every finding of chosen families for review.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
b4b9308 was committed with two failing tests: the module audit read
`from . import profiling` as an import of the package, called profiling
orphaned, and the committed audit manifest drifted. My check read tail's
exit status, not pytest's. Now a from-import; manifest regenerated (109
load-bearing, 0 orphaned); 1,444 pass with pytest's own exit checked.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Same 500-region PHP run on frozen source: completion 281/406 against 153
(hard cap) and 131 (7.8), no portion killed, per-family findings never
lower, declared target still entailed. Records the 7.9 task, its limits,
and corrects the 7.8 reuse note, which was stated backwards.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Final end-to-end on the 500-region PHP subject: findings identical at 125 either way, completion
slightly lower under portioning (131 of 406 vs the hard-cap-plus-reserve 153), wall time higher
(2,982 vs 2,338 s). Told straight: portioning does not beat the cap on throughput here; it matches
it on what it finds, and what it buys is kill-safety -- a killed batch keeps its streamed answers
instead of losing them. Reaching the same 125 findings is the evidence that streaming loses nothing
the cap kept.
Completion did not rise because the requeue re-asks a killed portion remainder, and on this subject
those are the expensive output-encoding questions that keep hitting the ceiling in smaller pieces,
spending budget without completing. Telling a transient kill from a too-expensive family is the next
lever, not more portioning. Neither approach is release-ready anyway: 32-38% completion against a
95% gate on development data that qualifies nothing.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
classes/class.memberorder.php:936:getMemberOrderByCode, injection. A value concatenated into SQL with
no prepare, which is the declared M3 target for PHP, found for the first time.
Nothing about detection changed. The population became affordable: tier-zero pruning scoped to its
partition, batches budgeted by the sink work they imply, a per-query timeout no longer cancelling the
scan, and a reserve kept for arbitration. Before those this population completed zero questions at
every budget tried.
At 500 regions with a 3,600 s deadline: 271 of 406 questions completed, 213 findings, 2,950 s, and
eight findings in the target file including the target itself.
The earlier bounded attempt failed for a reason no budget increase could fix -- 25 regions of 4,461
is 0.6 percent of the population and the target was never asked about -- so this sizes the replay
before spending hours on it. One scan at this budget is 2,950 s, a push transaction is two scans plus
discovery and preparation, so the three declared comparisons are roughly five and a half hours.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
PMPro at 25 regions, which completed before this work and still does: 27 questions completed and 19
findings before and after, injection 1 and output encoding 18 in both. The batching changes what a
large population can afford and nothing about what a small one answers.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
On the 500-region population where every earlier run produced nothing: 153 of 406 questions completed
and 125 findings, inside the deadline. 380 requests carrying 5,807 sink visits split into 11 batches,
each answering in 80 to 197 s, none near the 300 s ceiling. Every family that can speak on this
subject now does, injection included, which is the one holding a true positive here.
Four defects, each hidden by the one before it, and three of them one rule.
Batches sized by request count, when cost is per matched sink.
Packing heaviest-first, which starved injection of all 114 of its questions.
Any per-query timeout cancelling the whole scan, abandoning batches with 33 minutes left.
No budget kept for arbitration, which removing that cancellation exposed: rows are not findings.
The reserve had to be checked per BATCH. Checked between query kinds it changed nothing at all,
because one kind is a single loop iteration that runs every one of its batches inside it.
What it does not claim: 253 of 406 questions still went unanswered and one batch still records a
timeout; the raw finding count is dominated by output encoding, the family already measured to be
imprecise on PHP; and the reserve share of a fifth is a first value, not a tuned one.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Open since September and re-measured nine times. The cost of a taint batch is the number of CALL
NODES a family sink names match in the repository. Not the request count, not the declared sink
count, not any request parameter. reachableByFlows is asked per matched sink, and PHP
soutput-encoding sinks are echo and print, which a WordPress plugin writes thousands of times.
Three controlled sweeps on one graph, built once.
Batch size exonerated: 400 requests answer in 170 s, and cost per request FALLS with size because
roughly 70 s is fixed JVM start and graph load.
Request parameters exonerated: at 200 requests the full production shape, call depth 3 with
parameter sources, costs 221 s against a 150 s baseline.
The family is the variable: at the same 100 requests, SQL injection 147 s with six declared sinks,
path 194 s with sixteen, output encoding 704 s with seven.
And the driver named its own expensive step once it was asked to: 2,435 evidence requests in 45.5 s,
then 315 arbitration requests unanswered at 1,200 s, then a split abandoned for want of time.
It survived since September because every reproduction outside the driver carried the SQL sinks --
the family the declared target belongs to, and the one family that is cheap on this subject. Nine
hypotheses were eliminated by re-measuring the cheap case.
Splitting never rescued it because a failed batch is split by REQUEST COUNT and each part carries the
same everywhere-sinks. contributor-scan 7.7 carries the fix: budget a batch by the sink nodes it
implies, using the count the evidence pass already computes, and report a family whose sinks are
everywhere as expensive rather than as failed.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Four census runs on this subject spent whatever query ceiling they were given: 363 s of 300, 1,255 s
of 1,200, 1,874 s of 1,800, at question counts of 2,526 and 406. Cost that tracks the budget rather
than the work is not per-question cost, and tier-zero pruning cutting the count six-fold changed
nothing. A census runs one batch, so no census could answer whether the batch itself scales.
One graph, built once, five batch sizes, every one answered:
25 requests 71.4 s 2.86 s each
50 requests 76.0 s 1.52 s each
100 requests 155.4 s 1.55 s each
200 requests 166.7 s 0.83 s each
400 requests 169.7 s 0.42 s each
Sublinear, and roughly 70 s of it is fixed JVM start and graph load. Four hundred dataflow requests
answer in 170 seconds on the graph production cannot finish 406 on.
So the difference is in the request PARAMETERS. The probe sent callDepth=0 and no parameter sources;
production sends callDepth=3 with parameter sources on every entry point, plus a hook table, a
dispatch list and context evidence. Task 13.2 recorded in September that every reproduction outside
the push path answered in under a minute and the difference was not known. Size is now removed as a
candidate and a four-variant probe is running to isolate the rest.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
The mechanism already existed. A question whose evidence vector says no sink of its family is in
reach cannot produce a flow, and tier-zero pruning defers it. That was switched off for the whole
scan whenever any of twelve degradation reasons appeared, and a multi-language repository always
carries two of them, so the measured plugin arbitrated every question it could ask. Third instance
this week of one blast radius, and the invariant written into the design on Monday named it.
A prune blocker that names a partition now withholds pruning from that partition questions and from
nobody else. cross_partition_semantics_unresolved leaves the blocker set entirely, for the reason it
left the integrity gaps: a PHP family sink is PHP, so a second language cannot be why its vector saw
none.
Measured on Paid Memberships Pro. At 25 regions arbitration falls from 127 questions to 34 with 93
recorded deferred as tier_zero, and 19 findings before and after, identical family by family. At 500
it falls from 2,526 to 406 with 2,120 deferred.
It is not sufficient, and that is the useful half. The 500-region run still fails in the same way at
the same cost, because 406 questions at roughly one and a half seconds each still exceed the
300-second per-batch ceiling. This lever moved the volume; the ceiling is a separate limit.
The census now records what a scan skipped and why, per family, so a cheaper scan and a narrower one
stop looking identical: a family whose every question was pruned reports no sink of this family in
reach rather than region offered, no question asked.
1,420 tests pass, eighteen skipped.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
NodeGoat models 18 of 175 distinct call names, covering 12 percent of 1,185 calls. That share is not
a score: most of what a program calls is not security-relevant, and a high number would mean the
tables were too loose. It is a baseline to move deliberately.
The top product candidate is res.render, 23 calls with five in a method that reads untrusted input.
Task 14.3 reasoned its way to that same gap by hand and declined to model it as a flow sink; the
instrument found it in one run, which is the validation that matters. It also surfaced Math.random in
the user data-access module, and weak randomness is a weakness class this ontology has no family for
at all -- so no family census could have found it, because the family list is itself the blind spot.
That is the kind of gap only a coverage instrument finds.
It had to be taught what is not an API. The first run ranked _tmp_3.push and __ecma.Array.factory
first, which is how the frontend lowers a destructuring and an array literal, and a Cypress suite and
a Gruntfile held four of the next five. Both are separated now, because noise at the top of a
worklist is how a worklist stops being read.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
The scoping fix looked applied and was not. The same census gap is raised in TWO passes, arbitration
and evidence, and only the first carried the partition it came from, so an unscoped copy kept
demoting every question in the repository. One key in one dict.
What found it was the record, not the code: the census now prints each outcome reason rather than
only its status, and three separate causes had been looking identical as a bare count of unresolved.
Measured on Paid Memberships Pro at 25 regions: questions completed goes from 0 to 120 of 127, with
its 19 findings unchanged. The seven still unresolved are the JavaScript partition own questions,
which is correct, because the four missing files are its files and its answers are the ones that
could be wrong.
Still open, and recorded rather than implied: the push policy _blocking_degradations exempts declared
exclusions but does not yet respect a partition, and a post-hoc diagnostic copy carries no language,
so a JavaScript gap would still block admission of a PHP finding.
1,419 tests pass, eighteen skipped.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Membership of GRAPH_INTEGRITY_GAPS should turn on whether a gap can make a reported answer WRONG,
not on whether it cost coverage. An unparsed file may hold the sanitizer that would have cleared a
flow this scan reports, so it belongs there. A flow this tool cannot follow from one language into
another can hide a finding and never invent one, because a PHP sink is sanitised in PHP.
cross_partition_semantics_unresolved therefore moves to DECLARED_EXCLUSION_GAPS. It touches exactly
two places, both of which already honour the distinction: the outcome demotion in scan.py, and
_blocking_degradations in the push policy, whose docstring already says a declared exclusion is not
one of them. The reason stays published in the scope unresolved boundaries either way, so the limit
is still reported; the class decides only whether every answer in the repository is demoted.
This corrects the task note written earlier today, which said not to reclassify on the grounds that
it would claim cross-language flows are out of scope rather than unresolved. That was a misreading
of the class.
PMPro still completes none of its questions, and the measurement now names why: the JavaScript
partition expected 36 files and holds 32, so partition_file_census_incomplete fires and is still an
integrity gap. The four absent files are js/blocks/blocks.build.js, js/jsapi.js, js/select2.min.js
and webpack.config.js -- a build output, a source file, a minified third-party library and a build
config. A bundled select2 costs 4,425 PHP regions their admissibility. Same over-broad blast
radius, third instance of one principle in this thread, and the fix direction is recorded.
The census harness now records each degradation whole rather than only its reason, because a
partition census that reports files missing carries WHICH files and without them the gap is a label
rather than a lead.
1,417 tests pass, eighteen skipped.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
No PHP question is ever marked completed, and PHP was never the variable. partitions.py raises
cross_partition_semantics_unresolved whenever more than one language partition exists, that reason
is a member of GRAPH_INTEGRITY_GAPS, and a graph-integrity gap demotes every completed outcome in
the scan. Admission requires a completed outcome.
The evidence is one column wide. NodeGoat is JavaScript alone and completes 299 of 299. VAmPI is
Python alone and completes 35 of 35. PMPro is PHP with 36 regions of bundled JavaScript and
completes 0 of 127 and 0 of 520 while establishing 19 and 43 findings. NodeGoat own degradation,
vendor_semantics_unresolved, is a DECLARED exclusion rather than an integrity gap, which is exactly
why it does not demote.
This lands on the declared v0.1 target. A WordPress plugin is PHP plus its admin JavaScript, so
every one of them is multi-language, so on the target this tool was built for it can detect defects
and never admit one. Neither subject that currently passes could reveal it, which is why a census
across a third subject was worth running.
Recording the boundary is right: a flow from PHP into JavaScript is semantics the partitioned graph
cannot follow. Demoting every question is not, including one wholly inside a single PHP file. Task
12.3 already fixed this bug class one level down by making a gap question-owned so it could not
block another question comparison; the same principle applies to a partition boundary. Reclassifying
the reason as a declared exclusion is explicitly the wrong fix, because that would claim
cross-language flows are out of scope when they are unresolved.
Task 13.4 rewritten with the diagnosis, and the milestone amendment now ranks it with M2 rather
than behind it: a narrow JavaScript-only release can proceed without it, the WordPress target
cannot proceed at all.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
PHP is not silent. Paid Memberships Pro establishes findings, including a true positive, and
this project's own instrument is why it looked otherwise.
The census harness ordered its verdict on completion before findings, so a family with eighteen
findings and no completed question reported "asked but no question completed". Findings are now
checked first: a question-outcome count is bookkeeping and cannot hide the thing being censused.
NodeGoat and VAmPI were never affected, because they complete every question and their verdicts
reached the findings check. The scan summary now also carries the total findings and the outcome
status counts, so the two numbers can be seen disagreeing instead of one silently winning.
The second mistake was mine rather than the harness's: only the 500-region runs had been looked
at, which are the ones that time out.
What PHP actually detects on PMPro. At 25 regions, 19 findings in seven minutes with no timeout;
at 100 regions, 43 findings in twelve minutes with no timeout. Among them adminpages/
discountcodes.php:749, where sanitize_text_field($_REQUEST["s"]) reaches
$wpdb->get_results($sqlQuery, OBJECT) through `$sqlQuery .= "WHERE code LIKE '%$s%' "` with no
prepare. That is the class of CVE-2023-23488, which php.toml's own comment names as the reason
those WordPress sanitizers are kept out of the sanitizer list. The prediction and the measurement
agree.
The precision cost beside it is measured too: output encoding reports eighteen findings at 25
regions and the three sampled are all properly escaped, two with esc_attr and one coerced with
intval. Those escapers are absent from a sanitizer list that is flat across families, because
none of them stops SQL injection. Keeping the injection finding costs this family its precision.
The scale limit is roughly one to two seconds per question. 25 and 100 regions complete cleanly;
500 regions asks 2,526 questions, needs more than the 1,800-second ceiling and is killed.
Four things are measured NOT to be the problem: the malformed-CPG bug (fixed in 4.0.625, verified
on the real trigger file), the dataflow overlay (42 s on the full 637-file graph), the engine
version (4.0.625 and 4.0.631 indistinguishable), and the bundled Stripe, Braintree and Twocheckout
SDKs under includes/lib that the vendored list does not cover -- that was my hypothesis for the
cost and the overlay measures 42 s with them and 37 s without, so it is refuted.
What remains is bookkeeping, not detection: questions_completed is zero in every PHP run including
the ones that find things, with cross_partition_semantics_unresolved and
partition_file_census_incomplete present throughout. The admission policy reads those outcomes.
1,417 tests pass, eighteen skipped.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
The graph bug is fixed and was never what these runs were hitting. joernio/joern#6281, this
project's own report, is a PHP `global` inside a closure giving a node two AST parents, which made
the dataflow overlay throw and cost the whole graph. Filed against 4.0.623, fixed by #6270 in
v4.0.625, which the image has pinned since before any measurement recorded under task 13.2.
Verified today on the real trigger rather than a transcription of the repro, which does not
reproduce: PMPro's includes/fields.php builds and applies the default overlays cleanly on 4.0.625
and on 4.0.631, files=2 methods=158, no ReachingDefPass assertion.
What remains is runtime. A taint batch of 2,526 questions over the 673-file checkout does not
complete in thirty minutes. Raising the query ceiling six-fold, from 300 to 1,800 seconds, changed
nothing but how long the failure took: query time went from 363 s to 1,874 s, and zero questions
completed in both. The eight-way split retry did not rescue it either, finishing its parts in
about three minutes against a deadline with 3,300 seconds still on it, which is worth its own
look.
The engine pin stays at 4.0.625. An image on 4.0.631, today's release, was built and measured and
is indistinguishable on this subject: 627.7 s against 619.1 s, 363.4 s against 367.3 s of query
time, zero completed in both. There is no demonstrated benefit to weigh against re-verifying every
JavaScript and Python measurement on a new engine. Should that change, the bump is three values
rather than a port: the upstream AstGenRunner.scala is byte-identical in 4.0.631 and its patch
anchor still appears exactly once, so only the jar path, the jar checksum and the source URL tag
move. Both are recorded.
Also closes task 14.5. Neither prototype pollution nor deserialization has a subject that can
answer, each for its own reason. Deserialization's mechanism exists on exactly one available
subject and that subject cannot be queried at all. Prototype pollution has no available subject
containing the mechanism, so its ability to answer is established on a control instead.
1,417 tests pass, eighteen skipped.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
