Maintainer correction: execution isn't possible for all SAST targets, and the
bugs this project owns -- IDOR, missing authz, injection -- don't crash
anything. Execution is the easy arbiter only for memory-safety-in-C, which is
not our world.
What execution provided was an independent, deterministic arbiter of whether a
claim is real -- not the same LLM that made it, not a noisy vote. That arbiter
can be built statically, from the code, as a program model the claim is checked
against. LLM proposes; the model disposes. Same architecture, same repair of
the root cause, no runnable target required.
The evidence ladder becomes static and deterministic:
suspicion -> model_corroborated (claim consistent with resolved dataflow /
a guard its siblings carry is absent) -> model_entailed (the model alone
establishes it) -> execution_confirmed (deferred tier, C memory-safety only).
model_corroborated and above are deterministic -- run the checker twice, same
verdict -- which removes the run-to-run noise the same way a crash would, with
no K-runs and no target to build. Our IR candidate enumerator (96.6% ceiling),
binding chains and obligation checker stop being prompt context and become the
arbiter itself.
The course: promote those assets from inputs to arbiter, delete the noise
layer, make the LLM a proposer the model audits, keep the corpus as the
model's calibration set (the fix diff is what the model should see), and defer
execution to Clearwing for the memory-safety classes only. Build the arbiter we
can build from code; adopt the one we cannot, when a class demands it.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Written after five evolve rounds produced zero decisions distinguishable from
noise, and after reading Clearwing, Big Sleep, and the AIxCC 2025 finals.
The premise we got wrong: OpenUltraSAST treats the LLM as a noisy oracle to
average (K runs), floor (noise budget), gate (sign test) and slowly improve
(evolve loop). 120 runs produced 120 distinct trajectories -- the variance is
the loop's freedom to search, not the model's uncertainty. And the premise is
empirically false as a claim about LLMs: Big Sleep found a real SQLite zero-day
in Nov 2024; AIxCC finalists uncovered 18 real 0-days across 54M LOC, with the
2nd-place system using non-reasoning LLMs at 90% accuracy and $181/point. The
lever is architecture, not prompt optimization.
What the winners share, and Clearwing is the open instance of: the LLM is a
judgement layer on an execution substrate, and truth comes from the sandbox --
a crash, a sanitizer report, a patch that stops the crash -- not from a score
against a labeled corpus. Clearwing's poc_runner (apply diff, recompile,
re-run, does-the-crash-still-happen) replaces our entire noise architecture. We
built K-runs because we had no ground truth; they generate their own.
The course: adopt Clearwing as the substrate (the maintainer already forks it),
delete the noise architecture rather than fix it, and contribute the one thing
Clearwing lacks -- the web/logic detection path. Our IR candidate enumerator
(12.1% ruleset vs 96.6% IR, measured) and our obligation/absence-bug checker
reach the dangerous web bugs that never crash a sandbox, which Clearwing's
crash-oracle model cannot. The open research problem is a differential oracle
for those classes: the vulnerable-parent/fix-commit pair as ground truth, with
a request the fixed version rejects and the vulnerable one serves.
Recommendation is explicitly not to rebuild: eighteen defects this session,
all ours, against a 19k-line battle-tested MIT substrate. Not approved.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
The maintainer settled the conflict the group 2 review escalated: a detector
configuration has no skills set. The skill router belongs to the tool's earlier
shape, and a family's prompt, checklist, tools and counterexamples already carry
what a round evolves.
The README described the project as an editor-agent harness whose skills steer
it, and pointed users at the maintainer's spec directory. The tool is a CLI now
and the agent integrations are optional, so the opening says that, the skills
section describes what it actually is, and the spec pointer is gone from the
user-facing text.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
The maintainer settled both items the group 1 review escalated.
The family that covers server-side request forgery is named untrusted
destination and also covers open redirect, because both are untrusted input
choosing where a request goes and the corpus carries 34 open-redirect rows that
would otherwise be unclassifiable. The requirement, the brief and the design
example now say so, and the approval note records the amendment.
The shipped taxonomy is flat, so no answer can earn hierarchical partial credit.
Rather than leave that as prose, two new acceptance criteria require every
agreement figure and every family figure to say it, and both report types now
carry the flag, computed from the taxonomy rather than asserted. A taxonomy that
later declares a parent flips it without any other change.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 2.4. Per family, and per slice when asked: outcome counts, recall, silence,
Youden, a directional-bias index and recall at a fixed false-positive ceiling.
Rows that cannot be scored are listed by reason and leave every denominator, and
a family with nothing scorable reports no rate rather than a perfect one.
Two numbers exist because Youden cannot tell apart a detector that flags
everything from one that says everything is safe: the directional-bias index
separates them, and recall at a fixed ceiling is forfeited entirely once a
family leaks more than the budget allows.
Change against a baseline is not a difference of means. Flips are counted per
pair in both directions and a two-sided exact sign test decides whether the
change is bigger than the noise, so one flip in ten reads as noise and six reads
as a real regression.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Foundation: taxonomy, DeepSeek endpoint, corpus fields, TypeScript grammar,
curated hunter tools. Core: classifier tiers and measurement, per-pair family
scoring and aggregation, detector configurations and runner, verifier registry
with oracle callback, canary templates and second judge, journal and archive.
Integration: one teacher rule, family scoring on the pairs hunter path, round
zero with per-model noise floors, scan-path tags and manifest block, proposers,
acceptance and revert, round orchestration, publish, subcommands. Corpus
repairs and validation last. Both sanity reviews' findings recorded in the
plan; K-run evaluation honors requirement 3.5 at every stage.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
A learning package layered families -> classify -> scoring -> split ->
detectors and verifiers -> rounds -> publish, composing the existing seams:
the tool hunter gains prompt, context and tool parameters; the hunter path of
the pair scorer takes an injected family matcher so pairs never imports
learning; every learning path selects teachers through one train-only rule;
the regress verdict takes an optional canary oracle; the chat endpoint resolves
DeepSeek before OpenRouter with thinking disabled and reasoning replayed, while
embeddings keep their own endpoint; HarnessX proposes behind the seam with a
scripted proposer for offline runs. Fifty-nine acceptance criteria traced;
boundary sections, file structure plan and migration phases populated.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Scope: catch the ten taxonomy families reliably on real-world and generated
application code, including forgotten authorization checks; not browser
engines, interpreters or memory-safety fuzzing. Decisions: verifier-aligned
families, a measured per-family noise floor as the regression budget, a cheap
model on an independent chat endpoint with embeddings elsewhere, canary-oracle
verifiers for the four most dangerous families first, the obligations lever
folded into the access-control family's first round, web families before the
C slice. Eleven requirements in EARS form; awaiting approval.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
