from __future__ import annotations import argparse import json import tempfile from dataclasses import dataclass from pathlib import Path from time import perf_counter 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 .config import load_config 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 .policy import assert_rules_resolve, load_policy from .preprocess import preprocess_repository, write_preprocess_artifact from .rank import rank_targets, write_rankings 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 .scoring import build_score_artifact from .stages import Stage, plan_for_mode, record_completed, skip_as_degradation, stages_payload from .verification import VerificationResult, 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: 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") subparsers.add_parser("mcp", help="run the narrow MCP server over stdio for OpenCode integration") 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, ) 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 mode == "deep": raise SystemExit("mode 'deep' is specified but sandboxed dynamic analysis is not implemented") 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") 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" 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", ), ) plan = record_completed(plan, Stage.MAP) if not hunter_model: runtime.state["degradations"].append(skip_as_degradation(Stage.MAP, "hunter_model_unavailable")) complexity_payload = { "hotspot_count": len(built_map.hotspots), "heuristic_only": built_map.heuristic_only, } runtime.run_stage( "report", lambda: write_markdown_report(findings, markdown_path, verifications, redact=config.hardening.redact_secrets) ) runtime.run_stage("sarif", lambda: write_sarif_report(findings, verifications, sarif_path)) 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, ), 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, ), ) 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), ) 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 _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, ) -> 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) 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, ) _print_improve_outcomes(outcomes, manifest_path, target, ledger_path, dry_run=dry_run) 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}" ) 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())