Make the >=90% recall / <10% FP detection gate a first-class, score-independent
CI step with non-regression guards.
- openultrasast.gate: canonical corpus map + DetectionMetrics/DetectionGateVerdict,
evaluate_corpus/detection_gate (no score input — cannot be unblocked by score),
merge_gate (ANDs the hard detection gate with the advisory-first score gate),
within_tolerance, and a `python -m openultrasast.gate` entrypoint that exits
non-zero on a breach. Wired as an explicit step in both the GitHub and Dagger
pipelines.
- 8.1: pipeline tests prove a high/passing score cannot unblock a recall-breaching
change; the gate fails below 90% recall or at/above 10% FP.
- 8.2: tests/golden/detection_baseline.json + a byte-identical baseline test for
the default ruleset / empty ledger (44/47 = 93.62% recall, 0% FP).
- 8.3: within_tolerance band logic + a capability-gated HarnessX tolerance test.
- 8.4: zero-dependency guard — governance/scoring/benchmark planes never import
HarnessX (sys.modules check + module-scope import scan).
- test_detection_benchmarks now sources the corpus/aggregation from the gate module.
128 passed, 3 skipped; ruff + mypy clean; detection gate PASS.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
Rebuild the quick-scan detection engine and ground-truth benchmarks to hit
>=90% recall / <10% false positives for C/C++, Python, JavaScript, and Java.
Engine (findings.py, preprocess.py):
- Scope each PatternRule by language (fixes cross-language false positives such
as the Python eval/exec rule firing on JS .exec()).
- Add JavaScript/TypeScript rules (command exec, eval/Function, reflected XSS,
SQLi, path traversal, SSRF, weak hash, deserialization); execFile/spawn-argv
excluded as safe alternatives.
- Add Java rules (Runtime exec, SQL string concatenation, weak hash, readObject)
and a Groovy server-side template rule (yieldUnescaped of a bound variable).
- Broaden C/C++ and Python coverage; precision guards for format strings and
parameterised vs concatenated SQL; #-comment skipping; CWE in rationale.
- Register .java/.groovy/.tpl source extensions.
Benchmarks:
- Add realistic corpora modeled on Damn Vulnerable C/C++, PyGoat/DVPWA, and
NodeGoat, plus the real kiview/damn-vulnerable-spring-boot-app vendored
verbatim (MIT, attribution in LICENSE/NOTICE.md). Each manifest carries
complete, independently-labeled ground truth including genuinely
hard-to-detect vulns (integer overflow, second-order SQLi, prototype
pollution) so recall is measured honestly, plus safe-API files for precision.
- Fix pre-existing smoke-manifest ground-truth errors (Python/JS line offsets,
missing C/C++ strcpy entry, Java SQLi line).
benchmark.py:
- Fix sink matching to normalise the expected sink consistently so sinks with
underscores (e.g. render_template_string) can match.
Tests:
- Add tests/test_detection_benchmarks.py enforcing the >=90% recall / <10% FP
goal per language and overall.
Measured: C/C++ 93.3%, Python 93.3%, JavaScript 92.3%, Java 100%;
overall 93.6% recall, 0.0% false-positive rate.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
