from __future__ import annotations import argparse import json import tempfile from collections.abc import Sequence from dataclasses import dataclass, replace from pathlib import Path from time import perf_counter from . import tool_hunter from .benchmark import ( create_benchmark_run, evaluate_benchmark, load_baseline_findings, load_benchmark_manifest, load_findings, resolve_benchmark_source, write_benchmark_artifacts, ) from .calibration import ( FalsePositiveLearning, calibrate_rankings, fp_reachability_overrides, learnings_from_verifications, load_false_positive_learnings, merge_false_positive_learnings, write_false_positive_learnings, write_ranking_calibrations, ) from .complexity import map as complexity_map from .complexity.ledger import persist_verdicts from .complexity.map import Hotspot from .config import load_config, load_dotenv from .findings import StaticFinding, quick_scan_findings, write_findings from .fusion import FusionDecision, fuse_findings_dispatch from .gate import FALSE_POSITIVE_CEILING, RECALL_FLOOR from .harness import HarnessRuntime, HarnessTraceWriter, write_harness_config from .harness_ext import has_harnessx from .hunter import run_hunter_pool, write_hunter_trajectories from .hunter_harness import HxScanOrchestrator from .improve import RoundOutcome, run_improvement from .index import build_code_chunks from .mapping import analyze_entry_points, attach_reachability_hints, ingest_sarif, write_entry_points, write_static_hints from .pair_gate import print_pair_metrics from .pairs import ( DEFAULT_CATALOG, SLICE_NAMES, PairCase, evaluate_catalog, load_pair_catalog, make_hunter_scan, result_payload, select_profile, select_slice, select_split, select_vendored, ) from .policy import assert_rules_resolve, load_policy from .preprocess import FileTarget, preprocess_repository, write_preprocess_artifact from .provenance import fingerprint from .provider.openrouter import OpenRouterEmbeddingClient, OpenRouterError from .rank import rank_targets, write_rankings from .regress import TRIGGERABLE, CandidateVerdict, run_regression, write_verdicts from .regress.candidate import hotspot_from_finding from .reports import scan_exit_code, write_manifest, write_markdown_report, write_sarif_report from .ruleset import DEFAULT_RULESET_DIR, load_ruleset from .run import ScanRun, create_scan_run from .sandbox import resolve_sandbox_probe, resolve_sandbox_runner from .scoring import build_score_artifact from .semantic import ( MechanismStore, OverlayRecord, adjudicate, append_mechanism, filter_promoted_hotspots, finding_from_coverage, load_facts, order_promotions, write_overlay, ) from .semantic.facts import FactLoadError, SemanticFacts from .stages import Stage, plan_for_mode, record_completed, record_skip, skip_as_degradation, stages_payload from .verification import VerificationResult, verify_findings, write_verification_results from .verify_judge import verify_findings_dispatch CALIBRATION_DIR = ".openultrasast/calibration" @dataclass(frozen=True) class ScanOutcome: scan_id: str run_dir: Path file_target_count: int ranked_target_count: int finding_count: int calibrations_applied: int exit_code: int def main(argv: list[str] | None = None) -> int: load_dotenv() parser = argparse.ArgumentParser(prog="ousast") subparsers = parser.add_subparsers(dest="command", required=True) scan = subparsers.add_parser("scan", help="scan a local repository") scan.add_argument("path", type=Path) scan.add_argument("--mode", choices=("quick", "standard", "deep"), default="quick") scan.add_argument("--config", type=Path, default=Path("openultrasast.toml")) scan.add_argument("--fail-on", choices=("never", "findings", "verified", "worth-fixing"), default="never") index = subparsers.add_parser("index", help="chunk a local repository for embedding index construction") index.add_argument("path", type=Path) index.add_argument("--config", type=Path, default=Path("openultrasast.toml")) index.add_argument("--chunk-lines", type=int, default=80) benchmark = subparsers.add_parser("benchmark", help="run a benchmark manifest and write scoreboard artifacts") benchmark.add_argument("manifest", type=Path) benchmark.add_argument("--mode", choices=("quick", "standard", "deep"), default="quick") benchmark.add_argument("--config", type=Path, default=Path("openultrasast.toml")) improve = subparsers.add_parser( "improve", help="run the bounded self-improvement loop against a benchmark manifest and update the loop-owned ruleset ledger", ) improve.add_argument("manifest", type=Path) improve.add_argument("--max-rounds", type=int, default=5) improve.add_argument("--recall-floor", type=float, default=RECALL_FLOOR) improve.add_argument("--fp-ceiling", type=float, default=FALSE_POSITIVE_CEILING) improve.add_argument( "--ledger", type=Path, default=None, help="ruleset ledger path (default: /.openultrasast/calibration/rule_policy.json)" ) improve.add_argument("--journal", type=Path, default=None, help="improvement journal path (default: alongside the ledger)") improve.add_argument( "--ruleset-dir", type=Path, default=DEFAULT_RULESET_DIR, help="ruleset directory to improve (default: the bundled ruleset)" ) improve.add_argument("--dry-run", action="store_true", help="run rounds against a throwaway ledger; never touch the target's ledger") improve.add_argument( "--pair-catalog", type=Path, default=DEFAULT_CATALOG, help="pair catalog whose holdout split gates every round per provenance profile", ) improve.add_argument( "--no-pair-gate", action="store_true", help="skip the per-profile holdout clause (faster; not for accepted ledgers)" ) improve.add_argument( "--profile-tolerance", type=float, default=0.0, help="allowed per-profile drop in pair_correct/Youden before rejecting" ) improve.add_argument("--min-holdout-pairs", type=int, default=5, help="profiles with fewer holdout pairs are reported, not gated") pairs = subparsers.add_parser( "pairs", help="scan isolated vuln-vs-fixed pairs (TP on vuln, silent on fix) and emit improve-loop signals", ) pairs.add_argument("--catalog", type=Path, default=DEFAULT_CATALOG) pairs.add_argument("--slice", choices=SLICE_NAMES, default="all") pairs.add_argument( "--profile", choices=("all", "human", "agent", "mixed", "synthetic"), default="all", help="filter pairs by provenance profile" ) pairs.add_argument("--split", choices=("all", "train", "holdout"), default="all", help="filter pairs by declared split") pairs.add_argument("--hunter", action="store_true", help="also score the LLM tool hunter on overlay slices (needs a hunter model)") pairs.add_argument("--hunter-model", default=None, help="model id for --hunter (default: [models].hunter from openultrasast.toml)") pairs.add_argument("--pointers", action="store_true", help="allow network for non-vendored pointer pairs this run (nightly; CI never)") pairs.add_argument( "--loo", action="store_true", help="leave-one-out: score each pair against a store seeded from the other trusted pairs" ) pairs.add_argument("--loo-out", type=Path, default=None, help="where to write loo.json (default: reports/loo.json)") pairs.add_argument("--json", action="store_true", help="print the pair scoreboard as JSON") subparsers.add_parser("mcp", help="run the narrow MCP server over stdio for OpenCode integration") mechanisms = subparsers.add_parser("mechanisms", help="maintainer: mechanism memory seeded from trusted pairs") mechanisms_sub = mechanisms.add_subparsers(dest="mechanisms_command", required=True) export = mechanisms_sub.add_parser("export", help="derive mechanism records from seeded/reviewed pairs (offline)") export.add_argument("--slice", choices=SLICE_NAMES, default="all") export.add_argument("--catalog", type=Path, default=DEFAULT_CATALOG) export.add_argument( "--store", type=Path, default=Path(CALIBRATION_DIR) / "mechanisms.jsonl", help="append-only mechanisms.jsonl the scan reads" ) export.add_argument("--json", action="store_true") args = parser.parse_args(argv) if args.command == "scan": return _scan(args.path, args.config, args.mode, args.fail_on) if args.command == "index": return _index(args.path, args.config, args.chunk_lines) if args.command == "benchmark": return _benchmark(args.manifest, args.config, args.mode) if args.command == "improve": return _improve( args.manifest, max_rounds=args.max_rounds, recall_floor=args.recall_floor, fp_ceiling=args.fp_ceiling, ledger=args.ledger, journal=args.journal, ruleset_dir=args.ruleset_dir, dry_run=args.dry_run, pair_catalog=None if args.no_pair_gate else args.pair_catalog, profile_tolerance=args.profile_tolerance, min_holdout_pairs=args.min_holdout_pairs, ) if args.command == "pairs": return _pairs( args.catalog, args.slice, json_out=args.json, profile=args.profile, split=args.split, hunter=args.hunter, hunter_model=args.hunter_model, pointers=args.pointers, loo=args.loo, loo_out=args.loo_out, ) if args.command == "mechanisms": return _mechanisms_export(args.catalog, args.slice, args.store, json_out=args.json) if args.command == "mcp": from .mcp import serve # lazy: keeps the import cycle (mcp -> cli) one-directional return serve() return 2 def _scan(path: Path, config_path: Path, mode: str, fail_on: str) -> int: outcome = _run_scan(path, config_path, mode, fail_on) _print_scan_outcome(outcome) return outcome.exit_code def _run_scan(path: Path, config_path: Path, mode: str, fail_on: str) -> ScanOutcome: if not path.exists() or not path.is_dir(): raise SystemExit(f"scan path is not a directory: {path}") plan = plan_for_mode(mode) config = load_config(config_path if config_path.exists() else None) # Capability gates for the optional HarnessX agentic plane. When the extra is # absent (default/CI), both stay False and the deterministic path runs unchanged. hunter_model = config.models.hunter verifier_model = config.models.verifier harnessx_present = has_harnessx() hx_hunter = mode == "standard" and bool(hunter_model) and harnessx_present hx_verify = mode == "standard" and bool(verifier_model) and harnessx_present run = create_scan_run(path, config) runtime = HarnessRuntime( scan_id=run.scan_id, config=config, trace_writer=HarnessTraceWriter(run.root / "trace" / "events.jsonl", redact=config.hardening.redact_secrets), ) write_harness_config(config=config, processors=[], contract_mode="strict", path=run.root / "harness.json") runtime.start(mode=mode, target=run.target) policy = runtime.run_stage("policy_load", load_policy) ledger_path = run.target / CALIBRATION_DIR / "false_positive_learnings.json" rule_ledger_path = run.target / CALIBRATION_DIR / "rule_policy.json" ruleset = runtime.run_stage("ruleset_load", lambda: load_ruleset(DEFAULT_RULESET_DIR, rule_ledger_path)) runtime.run_stage("policy_check", lambda: assert_rules_resolve(ruleset, policy)) static_hints = runtime.run_stage("static_mapping", lambda: _load_static_hints(config.static_analysis.sarif_paths)) write_static_hints(static_hints, run.root / "mapping" / "static_hints.json") snapshot, targets = runtime.run_stage( "preprocess", lambda: preprocess_repository(run.target, run.root / "preprocess" / "file_targets.json", static_hints), ) entry_points = runtime.run_stage("entry_point_mapping", lambda: analyze_entry_points(run.target, targets)) write_entry_points(entry_points, run.root / "mapping" / "entry_points.json") targets = attach_reachability_hints(targets, entry_points) write_preprocess_artifact(snapshot, targets, run.root / "preprocess" / "file_targets.json") provenance = runtime.run_stage("provenance", lambda: fingerprint(run.target)) rankings = runtime.run_stage("rank", lambda: rank_targets(targets)) prior_learnings = load_false_positive_learnings(ledger_path) rankings, calibrations = runtime.run_stage("calibrate", lambda: calibrate_rankings(rankings, prior_learnings)) applied_calibrations = [calibration for calibration in calibrations if calibration.applied_learning_ids] write_rankings(rankings, run.root / "rank" / "ranking.json") write_ranking_calibrations(calibrations, run.root / "calibration" / "applied_calibrations.json") if mode == "standard": if hx_hunter and hunter_model: orchestrator = HxScanOrchestrator( provider_model=hunter_model, provider=config.harnessx.provider, max_cost_usd=config.harnessx.max_cost_usd, token_threshold=config.harnessx.token_threshold, ) hunter_result = runtime.run_stage( "hunter_pool", lambda: orchestrator.run_pool( run.target, targets, rankings, scan_id=run.scan_id, ruleset=ruleset, policy=policy, emit=runtime.emit ), ) else: if bool(hunter_model) and not harnessx_present: runtime.state["degradations"].append( {"stage": "hunter_pool", "requested": "harnessx", "reason": "harnessx_extra_unavailable", "fallback": "run_hunter_pool"} ) hunter_result = runtime.run_stage( "hunter_pool", lambda: run_hunter_pool(run.target, targets, rankings, scan_id=run.scan_id, ruleset=ruleset, policy=policy), ) findings = hunter_result.findings trajectories = hunter_result.trajectories else: findings = runtime.run_stage( "quick_findings", lambda: quick_scan_findings( run.target, targets, rankings, ruleset, policy, min_emit_priority=config.ruleset.min_emit_priority, min_emit_precision=config.ruleset.min_emit_precision, ), ) trajectories = [] # Shadow-status rules fire but are excluded from the report and the score; # their outcomes are preserved separately for precision tracking. shadow_findings = [finding for finding in findings if finding.status == "shadow"] if shadow_findings: findings = [finding for finding in findings if finding.status != "shadow"] write_findings(shadow_findings, run.root / "shadow_findings.json") # Bounded-CI budget: cap the reported finding count (0 = unlimited). Findings are # severity/priority-sorted, so truncation keeps the most severe and is disclosed. max_findings = config.hardening.max_findings if max_findings and len(findings) > max_findings: runtime.state["degradations"].append( {"stage": "budget", "reason": "max_findings_exceeded", "requested": max_findings, "actual": len(findings)} ) findings = findings[:max_findings] findings_path = run.root / "findings.json" verification_path = run.root / "verification.json" markdown_path = run.root / "report.md" sarif_path = run.root / "report.sarif" manifest_path = run.root / "manifest.json" trajectories_path = run.root / "traces" / "hunter_trajectories.jsonl" write_findings(findings, findings_path) if trajectories: write_hunter_trajectories(trajectories, trajectories_path) if mode == "standard" and bool(verifier_model) and not harnessx_present: runtime.state["degradations"].append( {"stage": "verify", "requested": "harnessx", "reason": "harnessx_extra_unavailable", "fallback": "structural_verifier"} ) verifications = runtime.run_stage( "verify", lambda: verify_findings_dispatch( findings, verifier_model=verifier_model, verifier_provider=config.harnessx.provider, use_harnessx=hx_verify, ), ) write_verification_results(verifications, verification_path) runtime.run_stage( "record_calibration", lambda: _persist_calibration_feedback(run, ledger_path, prior_learnings, findings, verifications), ) # Fusion: two-panel adjudication for triggered findings (standard mode). Runs # deterministically by default; routes panels through the configured provider when # a panel model is set and the extra is present, else falls back + records a degradation. fusion_decisions: list[FusionDecision] = [] fusion_path = run.root / "fusion.json" if mode == "standard" and config.fusion.enabled: hx_fusion = bool(config.fusion.panel_model) and harnessx_present if bool(config.fusion.panel_model) and not harnessx_present: runtime.state["degradations"].append( {"stage": "fusion", "requested": "harnessx", "reason": "harnessx_extra_unavailable", "fallback": "deterministic_panels"} ) fusion_decisions = runtime.run_stage( "fusion", lambda: fuse_findings_dispatch( findings, verifications, panel_model=config.fusion.panel_model, decider_model=config.fusion.decider_model, provider=config.harnessx.provider, use_harnessx=hx_fusion, high_assurance=config.fusion.high_assurance, ), ) if fusion_decisions: fusion_path.write_text(json.dumps([decision.to_dict() for decision in fusion_decisions], indent=2, sort_keys=True) + "\n") cwe_by_rule = {rule.rule_id: rule.cwe for rule in ruleset} rule_cwe_by_id = {finding.finding_id: cwe_by_rule.get(finding.finding_id.split(":", 1)[0], "") for finding in findings} # A confirmed false positive (prior-scan learning) lowers a finding's effective # reachability multiplier in the score instead of deleting the rule. reachability_override = fp_reachability_overrides(findings, prior_learnings) score_artifact = runtime.run_stage( "score", lambda: build_score_artifact( findings, rule_cwe_by_id, policy, k=config.score.k, min_score=config.score.min_score, block_severity_reachable=config.score.block_severity_reachable, blocking=config.score.blocking, reachability_override=reachability_override, ), ) score_path = run.root / "score.json" score_path.write_text(json.dumps(score_artifact.to_dict(), indent=2, sort_keys=True) + "\n") plan = record_completed(plan, Stage.STATIC) complexity_payload: dict[str, object] | None = None complexity_map_path = run.root / "complexity_map.json" overlay_path = run.root / "overlay.json" built_map = None overlay_records: list[OverlayRecord] = [] wrote_overlay = False variants_payload: dict[str, object] | None = None variant_findings: list[StaticFinding] = [] if Stage.MAP in plan.requested: built_map = runtime.run_stage( "map", lambda: complexity_map.build_complexity_map( targets, findings, complexity_map_path, repo_files=[target.path for target in targets], ledger_path=run.target / CALIBRATION_DIR / "complexity_ledger.json", ), ) facts_or_error: SemanticFacts | FactLoadError try: facts_or_error = load_facts() except FactLoadError as exc: facts_or_error = exc runtime.state["degradations"].append({"stage": "overlay", "reason": "facts_unavailable", "detail": str(exc)}) overlay_records = list( runtime.run_stage( "overlay", lambda: adjudicate(root=run.target, targets=targets, findings=findings, facts=facts_or_error), ) ) write_overlay(overlay_records, overlay_path) wrote_overlay = True coverage_findings = [finding_from_coverage(record) for record in overlay_records if record.disposition == "coverage"] if coverage_findings: seen = {finding.finding_id for finding in findings} extra = [item for item in coverage_findings if item.finding_id not in seen] if extra: findings = findings + extra write_findings(findings, findings_path) if config.variants.enabled: variant_findings, overlay_records, variants_payload = _search_variants( root=run.target, targets=targets, overlay_records=overlay_records, facts=facts_or_error, max_mechanisms=config.variants.max_mechanisms, runtime=runtime, ) if variant_findings: findings = findings + variant_findings write_findings(findings, findings_path) write_overlay(overlay_records, overlay_path) plan = record_completed(plan, Stage.MAP) if not hunter_model: runtime.state["degradations"].append(skip_as_degradation(Stage.MAP, "hunter_model_unavailable")) else: built_map, findings, verifications = _attach_tool_hunter( root=run.target, built_map=built_map, findings=findings, verifications=verifications, hunter_model=hunter_model, max_hotspots=config.complexity.max_hunter_hotspots, max_findings=max_findings, findings_path=findings_path, verification_path=verification_path, complexity_map_path=complexity_map_path, runtime=runtime, ) complexity_payload = { "hotspot_count": len(built_map.hotspots), "heuristic_only": built_map.heuristic_only, } verdicts_path = run.root / "verdicts.json" wrote_verdicts = False verdict_records: tuple[CandidateVerdict, ...] = () if Stage.REGRESS in plan.requested: probe = resolve_sandbox_probe() if not probe.available(): plan = record_skip(plan, Stage.REGRESS, "sandbox_unavailable") runtime.state["degradations"].append(skip_as_degradation(Stage.REGRESS, "sandbox_unavailable")) else: sandbox = resolve_sandbox_runner() hotspots = built_map.hotspots if built_map is not None else () hotspots = filter_promoted_hotspots(hotspots, overlay_records, findings) store = MechanismStore(run.target / CALIBRATION_DIR / "mechanisms.jsonl") embed_client = None embed_model = config.embeddings.model if embed_model: try: embed_client = OpenRouterEmbeddingClient.from_env() except OpenRouterError: embed_client = None hotspots, budget_degradation = order_promotions( hotspots, overlay_records, findings, store=store, client=embed_client, model=embed_model, ) if budget_degradation: runtime.state["degradations"].append({"stage": "prove_budget", "reason": budget_degradation}) promoted = [ finding for finding in findings if finding.finding_id in {record.proposal_id for record in overlay_records if record.disposition == "promote"} ] # Variant findings are candidates after the promotions (Req 3.4): same safety check, same cap, lowest rank. variant_ids = {finding.finding_id for finding in variant_findings} promoted = promoted + [finding for finding in findings if finding.finding_id in variant_ids] hotspots = hotspots + tuple(_hotspot_from_variant(finding) for finding in findings if finding.finding_id in variant_ids) cwe_by_rule_id = {rule.rule_id: rule.cwe for rule in ruleset} rule_cwe_by_finding = {finding.finding_id: cwe_by_rule_id.get(finding.finding_id.split(":", 1)[0], "") for finding in promoted} languages_by_path = {target.path: target.language for target in targets} records = runtime.run_stage( "regress", lambda: run_regression( hotspots, promoted, max_candidates=config.regress.max_candidates, policy=policy, rule_cwe=rule_cwe_by_finding, languages_by_path=languages_by_path, repo_root=run.target, sandbox=sandbox, sandbox_limits=config.sandbox, images=dict(config.regress.images), ), ) write_verdicts(records, verdicts_path) persist_verdicts(run.target / CALIBRATION_DIR / "complexity_ledger.json", records) _append_proven_mechanisms(store, records, overlay_records, findings, run.target) verdict_records = records wrote_verdicts = True plan = record_completed(plan, Stage.REGRESS) worth_fixing_payload = _worth_fixing_payload(verdict_records) if wrote_verdicts else None runtime.run_stage( "report", lambda: write_markdown_report( findings, markdown_path, verifications, redact=config.hardening.redact_secrets, complexity_map=built_map, verdicts=verdict_records if wrote_verdicts else None, overlay=overlay_records if wrote_overlay else None, ), ) runtime.run_stage( "sarif", lambda: write_sarif_report( findings, verifications, sarif_path, overlay=overlay_records if wrote_overlay else None, ), ) runtime.run_stage( "manifest", lambda: write_manifest( run=run, findings=findings, verifications=verifications, artifact_paths=_artifact_paths( findings=findings_path, verification=verification_path, markdown=markdown_path, sarif=sarif_path, score=score_path, trajectories=trajectories_path if trajectories else None, fusion=fusion_path if fusion_decisions else None, complexity_map=complexity_map_path if complexity_payload is not None else None, overlay=overlay_path if wrote_overlay else None, verdicts=verdicts_path if wrote_verdicts else None, ), path=manifest_path, score=score_artifact.to_dict(), degradations=runtime.state["degradations"] or None, fusion=[_fusion_summary(decision) for decision in fusion_decisions] or None, stages=stages_payload(plan), complexity=complexity_payload, worth_fixing=worth_fixing_payload, provenance=provenance.to_dict(), variants=variants_payload, ), ) runtime.finish(status="succeeded") return ScanOutcome( scan_id=run.scan_id, run_dir=run.root, file_target_count=len(targets), ranked_target_count=len(rankings), finding_count=len(findings), calibrations_applied=len(applied_calibrations), exit_code=scan_exit_code(findings, verifications, fail_on, worth_fixing_verdicts=verdict_records), ) def _append_proven_mechanisms( store: MechanismStore, verdicts: tuple[CandidateVerdict, ...], overlay_records: list[OverlayRecord], findings: list[StaticFinding], target: Path, ) -> None: overlay_by_id = {record.proposal_id: record for record in overlay_records} finding_by_id = {finding.finding_id: finding for finding in findings} for verdict in verdicts: if verdict.verdict != TRIGGERABLE: continue for finding_id in verdict.inventory_finding_ids: record = overlay_by_id.get(finding_id) if record is None or record.disposition != "promote": continue finding = finding_by_id.get(finding_id) append_mechanism( store, summary=record.reason, cwe=record.cwe, language=record.language or verdict.language, tags=finding.tags if finding is not None else (), what_made_it_exploitable=record.reason, source_finding_id=finding_id, source_repo=str(target), ) def _attach_tool_hunter( *, root: Path, built_map: complexity_map.ComplexityMap, findings: list[StaticFinding], verifications: list[VerificationResult], hunter_model: str, max_hotspots: int, max_findings: int, findings_path: Path, verification_path: Path, complexity_map_path: Path, runtime: HarnessRuntime, ) -> tuple[complexity_map.ComplexityMap, list[StaticFinding], list[VerificationResult]]: client = tool_hunter.resolve_hunter_client() if client is None: return built_map, findings, verifications hunter_hotspots = list(built_map.hotspots[: max(max_hotspots, 0)]) hunter_findings = runtime.run_stage( "tool_hunter", lambda: tool_hunter.run_tool_hunter( root, hunter_hotspots, client=client, model=hunter_model, max_steps=tool_hunter.DEFAULT_MAX_STEPS, ), ) seen = {finding.finding_id for finding in findings} extra = [finding for finding in hunter_findings if finding.finding_id not in seen] if extra: findings = findings + extra if max_findings and len(findings) > max_findings: if not any(entry.get("reason") == "max_findings_exceeded" for entry in runtime.state["degradations"]): runtime.state["degradations"].append( {"stage": "budget", "reason": "max_findings_exceeded", "requested": max_findings, "actual": len(findings)} ) findings = findings[:max_findings] kept = {finding.finding_id for finding in findings} extra = [finding for finding in extra if finding.finding_id in kept] if extra: verifications = verifications + verify_findings(extra) write_verification_results(verifications, verification_path) write_findings(findings, findings_path) built_map = replace(built_map, heuristic_only=False) complexity_map.write_complexity_map(built_map, complexity_map_path) return built_map, findings, verifications def _persist_calibration_feedback( run: ScanRun, ledger_path: Path, prior_learnings: list[FalsePositiveLearning], findings: list[StaticFinding], verifications: list[VerificationResult], ) -> list[FalsePositiveLearning]: # Store rejected or held findings as scoped learnings for the next scan. new_learnings = learnings_from_verifications(findings, verifications) merged = merge_false_positive_learnings(prior_learnings, new_learnings) write_false_positive_learnings(merged, ledger_path) write_false_positive_learnings(new_learnings, run.root / "calibration" / "false_positive_learnings.json") return merged def _print_scan_outcome(outcome: ScanOutcome) -> None: print(f"scan_id={outcome.scan_id}") print(f"run_dir={outcome.run_dir}") print(f"file_targets={outcome.file_target_count}") print(f"ranked_targets={outcome.ranked_target_count}") print(f"findings={outcome.finding_count}") print(f"calibrations_applied={outcome.calibrations_applied}") def _load_static_hints(sarif_paths: tuple[str, ...]) -> list[object]: hints: list[object] = [] for sarif_path in sarif_paths: path = Path(sarif_path) if path.exists(): hints.extend(ingest_sarif(path)) return hints def _worth_fixing_payload(verdicts: tuple[CandidateVerdict, ...]) -> dict[str, object]: worth = [record for record in verdicts if record.worth_fixing] return { "count": len(worth), "verdicts": [ { "path": record.path, "function_name": record.function_name, "verdict": record.verdict, "reason": record.reason, "worth_fixing": True, } for record in worth ], } def _artifact_paths(**paths: Path | None) -> dict[str, Path]: return {name: path for name, path in paths.items() if path is not None} def _fusion_summary(decision: FusionDecision) -> dict[str, object]: return { "finding_id": decision.finding_id, "disposition": str(decision.disposition), "decision_source": decision.decision_source, "triggers": decision.triggers, } def _benchmark(manifest_path: Path, config_path: Path, mode: str) -> int: if not manifest_path.exists() or not manifest_path.is_file(): raise SystemExit(f"benchmark manifest is not a file: {manifest_path}") manifest = load_benchmark_manifest(manifest_path) target = resolve_benchmark_source(manifest_path, manifest) run = create_benchmark_run(target, manifest) started_at = perf_counter() outcome = _run_scan(target, config_path, mode, "never") runtime_seconds = perf_counter() - started_at findings = _load_scan_findings(outcome.run_dir) baseline_findings = load_baseline_findings(manifest_path, manifest) result = evaluate_benchmark( run=run, mode=mode, findings=findings, scan_id=outcome.scan_id, scan_run_dir=outcome.run_dir, runtime_seconds=runtime_seconds, baseline_findings=baseline_findings, ) write_benchmark_artifacts(run, manifest_path, result) _print_scan_outcome(outcome) print(f"benchmark_run_id={run.benchmark_run_id}") print(f"benchmark_run_dir={run.root}") print(f"benchmark_expected={result.metrics.expected_findings_total}") print(f"benchmark_matched={result.metrics.matched_findings_total}") print(f"benchmark_missed={result.metrics.missed_findings_total}") return 0 def _improve( manifest_path: Path, *, max_rounds: int, recall_floor: float, fp_ceiling: float, ledger: Path | None, journal: Path | None, ruleset_dir: Path, dry_run: bool, pair_catalog: Path | None = None, profile_tolerance: float = 0.0, min_holdout_pairs: int = 5, ) -> int: if not manifest_path.exists() or not manifest_path.is_file(): raise SystemExit(f"benchmark manifest is not a file: {manifest_path}") pair_cases: tuple[object, ...] = () if pair_catalog is not None: if not pair_catalog.is_file(): raise SystemExit(f"pair catalog is not a file: {pair_catalog} (pass --no-pair-gate to skip the per-profile holdout clause)") pair_cases = select_split(load_pair_catalog(pair_catalog), "holdout") manifest = load_benchmark_manifest(manifest_path) target = resolve_benchmark_source(manifest_path, manifest) policy = load_policy() # The loop writes its accepted ledger where `scan`/`benchmark` read it, so a # subsequent scan of this target automatically picks up the improved ruleset. default_ledger = target / CALIBRATION_DIR / "rule_policy.json" with tempfile.TemporaryDirectory(prefix="ousast-improve-") as scratch: if dry_run: ledger_path = Path(scratch) / "rule_policy.json" journal_path = Path(scratch) / "improve_journal.json" else: ledger_path = ledger or default_ledger journal_path = journal or ledger_path.with_name("improve_journal.json") outcomes = run_improvement( target, manifest, ledger_path=ledger_path, journal_path=journal_path, ruleset_dir=ruleset_dir, policy=policy, max_rounds=max_rounds, recall_floor=recall_floor, fp_ceiling=fp_ceiling, pair_cases=pair_cases, profile_tolerance=profile_tolerance, min_holdout_pairs=min_holdout_pairs, ) _print_improve_outcomes(outcomes, manifest_path, target, ledger_path, dry_run=dry_run) return 0 def _pairs( catalog: Path, slice_name: str, *, json_out: bool, profile: str = "all", split: str = "all", hunter: bool = False, hunter_model: str | None = None, pointers: bool = False, loo: bool = False, loo_out: Path | None = None, ) -> int: if not catalog.exists() or not catalog.is_file(): raise SystemExit(f"pair catalog is not a file: {catalog}") cases = select_split(select_profile(select_slice(load_pair_catalog(catalog), slice_name), profile), split) if loo: return _pairs_loo(cases, loo_out or Path("reports") / "loo.json", json_out=json_out) scan = None if hunter: config_path = Path("openultrasast.toml") config = load_config(config_path if config_path.exists() else None) model = hunter_model or config.models.hunter or "" client = tool_hunter.resolve_hunter_client() if model and client is not None: scan = make_hunter_scan(client, model) result = evaluate_catalog(cases, hunter=scan, pointers=True if pointers else None) if json_out: print(json.dumps(result_payload(result), indent=2, sort_keys=True)) return 0 print_pair_metrics("pair eval", result) print(f"signals={len(result.signals)}") return 0 def _search_variants( *, root: Path, targets: list[FileTarget], overlay_records: list[OverlayRecord], facts: SemanticFacts | FactLoadError, max_mechanisms: int, runtime: HarnessRuntime, ) -> tuple[list[StaticFinding], list[OverlayRecord], dict[str, object]]: """Third MAP proposer (corpus-seeded-mechanisms Req 3): shapes from the mechanism store, merged with overlay flows.""" from .semantic.mechanisms import corpus_mechanisms from .semantic.variant_search import hits_to_findings, search_tree store = MechanismStore(root / CALIBRATION_DIR / "mechanisms.jsonl") empty: dict[str, object] = {"mechanisms_searched": 0, "files_searched": 0, "findings": 0, "merged_into_overlay": 0} if isinstance(facts, FactLoadError): return [], overlay_records, empty result = runtime.run_stage("variants", lambda: search_tree(root, targets, store, facts, max_mechanisms=max_mechanisms)) for degradation in result.degradations: runtime.state["degradations"].append(degradation) summaries = {record.id: (record.summary, record.pairs, record.cwe) for record in corpus_mechanisms(store.load())} findings, records = hits_to_findings(result.hits, overlay_records, summaries) merged = sum(1 for before, after in zip(overlay_records, records, strict=True) if before.mechanism_id is None and after.mechanism_id) payload: dict[str, object] = { "mechanisms_searched": result.mechanisms_searched, "files_searched": result.files_searched, "findings": len(findings), "merged_into_overlay": merged, } return findings, records, payload def _hotspot_from_variant(finding: StaticFinding) -> Hotspot: return hotspot_from_finding(finding, rationale="variant of a known mechanism (suspicion; sandbox may raise)") def _pairs_loo(cases: Sequence[PairCase], out: Path, *, json_out: bool) -> int: """Req 4: the corpus's own detection rate; written as an artifact, never a gate.""" from .semantic.loo import evaluate_loo result = evaluate_loo(select_vendored(cases)) payload = result.to_dict() out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n") if json_out: print(json.dumps(payload, indent=2, sort_keys=True)) return 0 for slice_name, metrics in result.per_slice.items(): print( f"loo {slice_name}: pairs={metrics['pairs']} detected={metrics['detected']} silent={metrics['silent']} " f"youden={metrics['youden']:+.3f} teaching={metrics['teaching']}" ) print(f"artifact={out}") return 0 def _mechanisms_export(catalog: Path, slice_name: str, store_path: Path, *, json_out: bool) -> int: from .semantic.mechanisms import MechanismStore from .semantic.seed import export_mechanisms if not catalog.is_file(): raise SystemExit(f"pair catalog is not a file: {catalog}") cases = select_vendored(select_slice(load_pair_catalog(catalog), slice_name)) report = export_mechanisms(cases, MechanismStore(store_path)) payload = {"slice": slice_name, "store": str(store_path), "pairs": len(cases), **report.to_dict()} if json_out: print(json.dumps(payload, indent=2, sort_keys=True)) return 0 print(f"mechanisms export slice={slice_name} pairs={len(cases)} seeded={report.seeded} records={report.records} -> {store_path}") for pair, reason in report.skipped: print(f" skip {pair}: {reason}") return 0 def _print_improve_outcomes(outcomes: list[RoundOutcome], manifest_path: Path, target: Path, ledger_path: Path, *, dry_run: bool) -> None: print(f"manifest={manifest_path}") print(f"target={target}") accepted = [o for o in outcomes if o.accepted] for outcome in outcomes: edits = ", ".join(f"{e.rule_id}:{e.from_status}->{e.to_status}" for e in outcome.edits) or "-" print( f"round {outcome.round}: {outcome.reason} | " f"recall {outcome.recall_before:.2%}->{outcome.recall_after:.2%} " f"fp {outcome.fp_before:.2%}->{outcome.fp_after:.2%} " f"score {outcome.score_before}->{outcome.score_after} | edits: {edits}" ) if outcome.profile_regressions or outcome.profiles_under_minimum: print(f" profiles: regressed={outcome.profile_regressions or '-'} under_minimum={outcome.profiles_under_minimum or '-'}") print(f"rounds={len(outcomes)} accepted={len(accepted)}") if dry_run: print("dry_run=true (no ledger written)") elif accepted: print(f"ledger={ledger_path}") else: print("ledger=unchanged (no round accepted)") def _load_scan_findings(run_dir: Path) -> list[StaticFinding]: return load_findings(run_dir / "findings.json") def _index(path: Path, config_path: Path, chunk_lines: int) -> int: if not path.exists() or not path.is_dir(): raise SystemExit(f"index path is not a directory: {path}") config = load_config(config_path if config_path.exists() else None) run = create_scan_run(path, config) snapshot, targets = preprocess_repository(run.target, run.root / "preprocess" / "file_targets.json") chunks = build_code_chunks(run.target, targets, max_lines=chunk_lines) payload = { "store": config.embeddings.store or "json-local", "embedding_model": config.embeddings.model, "repo_root": snapshot.root, "repo_commit": snapshot.commit, "chunk_count": len(chunks), "chunks": [chunk.__dict__ for chunk in chunks], } path_out = run.root / "index" / "chunks.json" path_out.parent.mkdir(parents=True, exist_ok=True) path_out.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n") print(f"scan_id={run.scan_id}") print(f"run_dir={run.root}") print(f"chunks={len(chunks)}") print(f"index_artifact={path_out}") return 0 if __name__ == "__main__": raise SystemExit(main())