CI was red on `ruff format --check`: 23 files. I ran `ruff check` after every
change this session and never once ran `ruff format --check`, though CI runs
both. Lint passing is not formatting passing, and I treated one as evidence for
the other for the whole session.
Formatting applied. The full CI sequence now reproduces locally: format, lint,
mypy over 94 files, 735 tests, three gates byte-identical, compileall, and the
semantic job's 16 marked tests.
Also records run 5 of the pipeline measurement, which the vampi fix lifted as
predicted:
hunter (K=5) pipeline (K=1)
pair-correct 76.7% 90.0%
leak 12.3% 6.7%
detection -- 96.7%
cost ~$0.69 $0.0068
27 of 30 pairs. Five runs, each step from a measurement contradicting an
assumption: 73.3 (no pipeline path) -> 73.3 (routing fixed, IDENTICAL, which
exposed the discarded verdict) -> 80.0 (residual question matched to its
arbiter) -> 86.7 (aiohttp settings) -> 90.0 (dominance question no longer
contradicting its own evidence).
One miss left: dsvpwa-dsvpwa-main, whose setting is an argparse --ssl default
rather than a security-setting call. Out of reach before the settings work and
still is; contorting the arbiter around one pair would be worse than the miss.
The caveat is recorded beside the number: 30 pairs of one slice, and the slice
iterated against all session. The +16% overfitting gap against vfc-js is why
contributor-scan Req 8.1/8.2 exist.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
hunter (K=5) pipeline (K=1)
pair-correct 76.7% 86.7%
leak 12.3% 6.7%
detection -- 93.3%
cost ~$0.69 $0.0067
Ten points of recall over the hunter, half the leak, a fifth of the model calls,
~100x cheaper. 26 of 30 pairs correct.
Four runs, and each step came from a measurement contradicting an assumption --
twice one I had stated:
1 73.3% the absence and config families had no pipeline path at all
2 73.3% routing fixed and the result IDENTICAL, which was the clue: the
arbiter was reached and its verdict thrown away, because every
corroboration was put to the judge as a taint question
3 80.0% residual question phrased for the arbiter that raised it
4 86.7% aiohttp Application(debug=True) and RedisStorage(httponly=False)
added to the settings table
The division of labour keeps moving the right way: model_entailed 9 -> 11 while
suspicion held at 15, so both findings gained in run 4 came from the MODEL, not
the LLM, and cost fell from $0.0126 to $0.0067 as work moved onto the free
deterministic path. That is the payoff curve for fact-table coverage, measured
rather than argued: closing a gap moves findings out of the LLM's column into
the model's, improving precision and cost at once.
Recorded corrections and limits, because the numbers are only worth what the
caveats are. The three silent config pairs were called corpus defects on a
similarity threshold borrowed from the OWASP minimal-fix analysis; it does not
apply to a slice that deliberately pairs a vulnerable function with a different
safe one, where 36% and 34% pairs both score pair_correct. Two were a settings
gap and now split. And permissive_values is a flat literal list with no keyword
association, holding both True and False -- debug=True is dangerous where
httponly=True is safe, and the model cannot tell them apart. It did not bite
here, and it is a real limit of the fact schema.
Four remaining misses, each with a named cause. Four runs cost about $0.04.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
hunter (K=5) pipeline (K=1)
pair-correct 76.7% 80.0%
leak 12.3% 6.7%
cost ~$0.69 $0.0076
determinism none entailed band deterministic
Higher recall, half the leak, a fifth of the model calls, ~90x cheaper.
Three runs, and the middle one earned its keep by changing nothing. Run 2 fixed
family routing and returned numbers IDENTICAL to run 1 -- which was the clue
that the dominance arbiter was being reached and its verdict thrown away. Every
corroboration was put to the judge as a taint question ("a sanitizer appears on
the path -- does it suffice?"), meaningless for a missing guard, so the judge
said no and the finding was dropped. Run 3 phrases the residual question for
the arbiter that raised it and converts 2 of 3 access_control pairs from silent
to pair-correct, arriving at model_corroborated -- a rung that existed but had
never reached a finding.
This settles a question open since the start of the session, and reverses an
earlier answer of mine. "Detection has regressed" was measured with the
proposer disconnected: an arbiter compared against a detector, before four
wiring bugs were found.
Division of labour: 15 suspicion, 9 model_entailed, 2 model_corroborated. The
model arbitrates 11 of 26 findings, and every conversion from suspicion to a
model rung removes a leak and a model call at once -- the payoff curve for sink
coverage and interprocedural resolution, now measured rather than assumed.
Still silent: 3 config_secrets (the settings table is Flask/FastAPI-shaped
while the pairs are aiohttp) and 1 access_control still to diagnose.
Three full runs cost about $0.03 in total.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
The first like-for-like comparison with the LLM hunter, on the same 30 vibe-py
pairs and the same metric.
hunter (K=5) pipeline (K=1)
pair-correct 76.7% 73.3%
leak 12.3% 6.7%
cost ~$0.69 $0.0126
determinism none entailed band deterministic
Half the leak, 55x cheaper, a fifth of the model calls, one pair short of the
hunter's recall (22 against 23 of 30). And understated: all six both_silent
pairs are access_control and config_secrets, which had no pipeline path to
their arbiters when this ran. A corrected run supersedes those rows.
The rung split says what is doing the work: 15 suspicion to 9 model_entailed on
the vulnerable side. The LLM supplies most of the recall; the model supplies
the precision, halving the leak by dropping claims it could contradict. That is
the division of labour the architecture was designed around and the first
evidence it pays.
It also revises an earlier conclusion of mine. "Detection has regressed" was
measured with the proposer disconnected -- an arbiter compared against a
detector. With the enumerator wired in, the comparison reverses on precision
and cost while holding recall.
And it makes the strategy concrete: the model arbitrates about a third of
findings today, and every suspicion converted to model_entailed is leak removed
for free and one fewer model call. Sink coverage and interprocedural resolution
now have a measured payoff rather than a hoped-for one.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
