mirror of
https://github.com/norandom/OpenUltraSAST.git
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Marius CiepluchandClaude Opus 4.8
091303c7c0
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
32 lines
2.8 KiB
JSON
32 lines
2.8 KiB
JSON
{
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"feature_name": "learning-harness",
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"created_at": "2026-09-06T06:32:41Z",
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"updated_at": "2026-09-08T00:00:00Z",
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"language": "en",
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"phase": "implementation",
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"approvals": {
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"requirements": {
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"generated": true,
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"approved": true
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},
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"design": {
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"generated": true,
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"approved": true
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},
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"tasks": {
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"generated": true,
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"approved": true
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}
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},
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"ready_for_implementation": true,
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"depends_on": [
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"pair-corpus-honesty",
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"corpus-seeded-mechanisms",
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"authorization-obligations",
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"tree-sitter-overlay-extra",
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"harnessx-self-improving-rulesets"
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],
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"approval_note": "2026-09-06 requirements approved by the maintainer in session ('approve the requirements and run the design phase'); 2026-09-06 design approved by the maintainer in session ('approve the design and generate the tasks'); 2026-09-06 tasks approved by the maintainer in session ('approve the tasks and go on with the implementation'); 2026-09-06 requirements amended by the maintainer in session ('amend the requirements to untrusted_destination and note the flat taxonomy'): Req 1.1 names untrusted destination instead of server-side request forgery, and Req 2.7 and 3.7 require every agreement and family figure to state that hierarchical partial credit could not apply while the taxonomy is flat; 2026-09-06 Req 6.1 amended by the maintainer in session ('drop the skills set from the requirement'): a detector configuration has no skills set, because 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; 2026-09-06 design amended during task 4.1 (recorded in design.md): a recipe may name cross-file registration documents, because connexion registers by operationId in an OpenAPI file and express in a router module, so Req 10.2 is unreachable from a single-file excerpt for those rows; 2026-09-06 task 5.3 amended by the maintainer in session (asked and answered): the first evolve rounds target `injection` rather than `access_control`, because under the split and review-tier rules access_control has one train teacher and four holdout pairs and teaches zero obligation shapes, while injection has twelve teachers and ten holdout pairs; 2026-09-08 SUPERSEDED by execution-grounded-detection: this session's measurements showed the noise architecture (K-runs, per-family floor, regression budget, sign test, evolve loop) is a statistical treatment for a structural problem, and that the field (Big Sleep, AIxCC finals, Clearwing) grounds detection in execution rather than in scoring against a labeled corpus. The taxonomy, the IR candidate enumerator (96.6% ceiling) and the obligation checker survive as the web/logic detection path to contribute to a Clearwing-based substrate.",
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"superseded_by": "model-grounded-detection"
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}
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