Files
Open_Ultra_SAST/src/openultrasast/ruleset/store.py
T
Marius CiepluchandClaude Opus 4.8 444d8c9490 feat(ruleset): Phase 2 (task 3) — rules as governed data, severity from policy
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]>
2026-06-22 15:27:16 +02:00

143 lines
5.1 KiB
Python

from __future__ import annotations
import json
import tomllib
from collections.abc import Iterable
from dataclasses import dataclass, replace
from pathlib import Path
DEFAULT_RULESET_DIR = Path(__file__).parent
VALID_STATUS = ("enabled", "shadow", "disabled")
class RulesetError(ValueError):
"""Raised when a ruleset data file is malformed."""
@dataclass(frozen=True)
class PatternRule:
"""A detection rule expressed as governed data (no rule-local severity).
``pattern`` is human-PR-only; the self-improvement loop may only change
``status``/``min_evidence_level``/``precision_estimate`` via the ledger.
"""
rule_id: str
title: str
languages: tuple[str, ...]
cwe: str
tags: tuple[str, ...]
pattern: str
status: str = "enabled"
min_evidence_level: str = "static_corroboration"
precision_estimate: float = 0.0
version: str = "1"
def load_ruleset(directory: Path = DEFAULT_RULESET_DIR, ledger: Path | None = None) -> tuple[PatternRule, ...]:
"""Load every ``*.toml`` rule file under ``directory`` and apply the loop ledger.
Rules are returned sorted by ``rule_id`` for determinism. The optional ledger
(``rule_policy.json``) overlays loop-owned ``status``/``min_evidence_level``/
``precision_estimate`` per ``rule_id``.
"""
rules: dict[str, PatternRule] = {}
for path in sorted(directory.rglob("*.toml")):
payload = tomllib.loads(path.read_text())
for item in payload.get("rule", []):
rule = _rule_from_dict(item, path)
if rule.rule_id in rules:
raise RulesetError(f"duplicate rule_id {rule.rule_id!r} in {path}")
rules[rule.rule_id] = rule
overlay = _load_ledger(ledger)
resolved = [_apply_overlay(rule, overlay.get(rule.rule_id)) for rule in rules.values()]
return tuple(sorted(resolved, key=lambda rule: rule.rule_id))
def write_ruleset(path: Path, rules: Iterable[PatternRule]) -> None:
"""Write rules as TOML. Patterns use literal multi-line strings (no escaping)."""
path.parent.mkdir(parents=True, exist_ok=True)
blocks: list[str] = []
for rule in rules:
if "'''" in rule.pattern:
raise RulesetError(f"rule {rule.rule_id} pattern cannot contain a triple single-quote")
blocks.append(
"\n".join(
[
"[[rule]]",
f"rule_id = {_s(rule.rule_id)}",
f"title = {_s(rule.title)}",
f"languages = {_arr(rule.languages)}",
f"cwe = {_s(rule.cwe)}",
f"tags = {_arr(rule.tags)}",
f"status = {_s(rule.status)}",
f"min_evidence_level = {_s(rule.min_evidence_level)}",
f"precision_estimate = {float(rule.precision_estimate)}",
f"version = {_s(rule.version)}",
f"pattern = '''{rule.pattern}'''",
]
)
)
path.write_text("\n\n".join(blocks) + "\n")
def _rule_from_dict(item: dict[str, object], path: Path) -> PatternRule:
try:
rule_id = str(item["rule_id"])
cwe = str(item["cwe"])
pattern = str(item["pattern"])
except KeyError as exc:
raise RulesetError(f"rule in {path} missing required field {exc}") from exc
status = str(item.get("status", "enabled"))
if status not in VALID_STATUS:
raise RulesetError(f"rule {rule_id} has invalid status {status!r}")
if not pattern:
raise RulesetError(f"rule {rule_id} has an empty pattern")
return PatternRule(
rule_id=rule_id,
title=str(item.get("title", rule_id)),
languages=_str_tuple(item.get("languages")),
cwe=cwe,
tags=_str_tuple(item.get("tags")),
pattern=pattern,
status=status,
min_evidence_level=str(item.get("min_evidence_level", "static_corroboration")),
precision_estimate=float(item.get("precision_estimate", 0.0)), # type: ignore[arg-type]
version=str(item.get("version", "1")),
)
def _load_ledger(ledger: Path | None) -> dict[str, dict[str, object]]:
if ledger is None or not ledger.exists():
return {}
payload = json.loads(ledger.read_text())
return {str(key): value for key, value in payload.items() if isinstance(value, dict)}
def _apply_overlay(rule: PatternRule, overlay: dict[str, object] | None) -> PatternRule:
if not overlay:
return rule
status = str(overlay.get("status", rule.status))
if status not in VALID_STATUS:
status = rule.status
return replace(
rule,
status=status,
min_evidence_level=str(overlay.get("min_evidence_level", rule.min_evidence_level)),
precision_estimate=float(overlay.get("precision_estimate", rule.precision_estimate)), # type: ignore[arg-type]
)
def _str_tuple(value: object) -> tuple[str, ...]:
if isinstance(value, list):
return tuple(str(item) for item in value)
return ()
def _s(value: str) -> str:
return '"' + value.replace("\\", "\\\\").replace('"', '\\"') + '"'
def _arr(values: tuple[str, ...]) -> str:
return "[" + ", ".join(_s(value) for value in values) + "]"