Add the Kiro-style agentic SDLC tooling used to drive this project: the
.opencode/skills/kiro-* skill set (discovery, spec init/requirements/design/
tasks, validation, implementation, review, steering), the .kiro/settings
templates, and AGENTS.md describing the workflow. Update the spec docs
(assessment/design/requirements/tasks) to current state.
Scope tests/test_opencode_skills.py to the project's own openultrasast-*
skills so the third-party kiro-* tooling (which uses a different frontmatter
convention) no longer breaks the frontmatter check.
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]>
