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Externalize detection rules to versioned TOML and remove rule-local severity, so rules detect and the central CWE policy decides severity — fixing "rules too influential / not centrally managed". Zero-dependency; detection is byte-identical (benchmark recall 44/47 = 93.6%, FP 0% unchanged) and the 90/10 gate holds. - RulesetStore (ruleset/store.py + ruleset/<lang>/rules.toml): PatternRule as data (rule_id, title, languages, cwe, tags, pattern, status, min_evidence_level, precision_estimate, version) with NO severity; load_ruleset (sorted, with optional rule_policy.json ledger overlay) and a stdlib TOML write_ruleset. The 32 rules were generated from the prior PATTERN_RULES with a verified exact round-trip (pattern/cwe/languages/tags/title). - findings.py: drop the hardcoded PatternRule class + PATTERN_RULES tuple; load the default ruleset from TOML; quick_scan_findings(root, targets, rankings, ruleset, policy) resolves each finding's severity from policy via resolve_severity (SEVERITY_LABEL maps 0-5 -> string) and tags findings with their rule status. - Three-status volume control: StaticFinding gains status (default "enabled"); disabled rules never fire; shadow findings are partitioned out of the report and score in the CLI (written to shadow_findings.json) — no shadow rules by default. - Emission floors (RulesetConfig.min_emit_priority / min_emit_precision, default 0.0 = emit all) replace "every match is a finding". - CLI loads the ruleset as a stage and uses it for the fail-loud policy check, detection, hunter pool, and the score CWE map. Wheel ships the ruleset TOML. 85 tests pass; ruff + mypy clean. Spec tasks 3.1-3.4 checked off. Co-Authored-By: Claude Opus 4.8 <[email protected]>
143 lines
5.1 KiB
Python
143 lines
5.1 KiB
Python
from __future__ import annotations
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import json
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import tomllib
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from collections.abc import Iterable
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from dataclasses import dataclass, replace
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from pathlib import Path
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DEFAULT_RULESET_DIR = Path(__file__).parent
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VALID_STATUS = ("enabled", "shadow", "disabled")
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class RulesetError(ValueError):
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"""Raised when a ruleset data file is malformed."""
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@dataclass(frozen=True)
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class PatternRule:
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"""A detection rule expressed as governed data (no rule-local severity).
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``pattern`` is human-PR-only; the self-improvement loop may only change
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``status``/``min_evidence_level``/``precision_estimate`` via the ledger.
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"""
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rule_id: str
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title: str
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languages: tuple[str, ...]
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cwe: str
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tags: tuple[str, ...]
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pattern: str
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status: str = "enabled"
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min_evidence_level: str = "static_corroboration"
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precision_estimate: float = 0.0
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version: str = "1"
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def load_ruleset(directory: Path = DEFAULT_RULESET_DIR, ledger: Path | None = None) -> tuple[PatternRule, ...]:
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"""Load every ``*.toml`` rule file under ``directory`` and apply the loop ledger.
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Rules are returned sorted by ``rule_id`` for determinism. The optional ledger
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(``rule_policy.json``) overlays loop-owned ``status``/``min_evidence_level``/
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``precision_estimate`` per ``rule_id``.
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"""
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rules: dict[str, PatternRule] = {}
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for path in sorted(directory.rglob("*.toml")):
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payload = tomllib.loads(path.read_text())
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for item in payload.get("rule", []):
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rule = _rule_from_dict(item, path)
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if rule.rule_id in rules:
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raise RulesetError(f"duplicate rule_id {rule.rule_id!r} in {path}")
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rules[rule.rule_id] = rule
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overlay = _load_ledger(ledger)
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resolved = [_apply_overlay(rule, overlay.get(rule.rule_id)) for rule in rules.values()]
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return tuple(sorted(resolved, key=lambda rule: rule.rule_id))
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def write_ruleset(path: Path, rules: Iterable[PatternRule]) -> None:
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"""Write rules as TOML. Patterns use literal multi-line strings (no escaping)."""
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path.parent.mkdir(parents=True, exist_ok=True)
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blocks: list[str] = []
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for rule in rules:
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if "'''" in rule.pattern:
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raise RulesetError(f"rule {rule.rule_id} pattern cannot contain a triple single-quote")
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blocks.append(
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"\n".join(
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[
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"[[rule]]",
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f"rule_id = {_s(rule.rule_id)}",
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f"title = {_s(rule.title)}",
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f"languages = {_arr(rule.languages)}",
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f"cwe = {_s(rule.cwe)}",
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f"tags = {_arr(rule.tags)}",
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f"status = {_s(rule.status)}",
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f"min_evidence_level = {_s(rule.min_evidence_level)}",
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f"precision_estimate = {float(rule.precision_estimate)}",
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f"version = {_s(rule.version)}",
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f"pattern = '''{rule.pattern}'''",
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]
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)
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)
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path.write_text("\n\n".join(blocks) + "\n")
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def _rule_from_dict(item: dict[str, object], path: Path) -> PatternRule:
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try:
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rule_id = str(item["rule_id"])
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cwe = str(item["cwe"])
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pattern = str(item["pattern"])
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except KeyError as exc:
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raise RulesetError(f"rule in {path} missing required field {exc}") from exc
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status = str(item.get("status", "enabled"))
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if status not in VALID_STATUS:
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raise RulesetError(f"rule {rule_id} has invalid status {status!r}")
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if not pattern:
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raise RulesetError(f"rule {rule_id} has an empty pattern")
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return PatternRule(
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rule_id=rule_id,
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title=str(item.get("title", rule_id)),
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languages=_str_tuple(item.get("languages")),
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cwe=cwe,
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tags=_str_tuple(item.get("tags")),
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pattern=pattern,
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status=status,
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min_evidence_level=str(item.get("min_evidence_level", "static_corroboration")),
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precision_estimate=float(item.get("precision_estimate", 0.0)), # type: ignore[arg-type]
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version=str(item.get("version", "1")),
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)
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def _load_ledger(ledger: Path | None) -> dict[str, dict[str, object]]:
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if ledger is None or not ledger.exists():
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return {}
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payload = json.loads(ledger.read_text())
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return {str(key): value for key, value in payload.items() if isinstance(value, dict)}
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def _apply_overlay(rule: PatternRule, overlay: dict[str, object] | None) -> PatternRule:
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if not overlay:
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return rule
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status = str(overlay.get("status", rule.status))
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if status not in VALID_STATUS:
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status = rule.status
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return replace(
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rule,
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status=status,
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min_evidence_level=str(overlay.get("min_evidence_level", rule.min_evidence_level)),
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precision_estimate=float(overlay.get("precision_estimate", rule.precision_estimate)), # type: ignore[arg-type]
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)
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def _str_tuple(value: object) -> tuple[str, ...]:
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if isinstance(value, list):
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return tuple(str(item) for item in value)
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return ()
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def _s(value: str) -> str:
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return '"' + value.replace("\\", "\\\\").replace('"', '\\"') + '"'
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def _arr(values: tuple[str, ...]) -> str:
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return "[" + ", ".join(_s(value) for value in values) + "]"
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