evandrodevbr
c17192c9fe
Copia local (nao fork, nao submodule) de safedeps, sca-audit, secrets-scan, code-review-security, api-security-review, web-security-review, cve-triage, patch-prioritization, dependency-scanning e hermaguard -- commit exato de origem e licenca de cada uma em skills/NOTICE.md. Elimina o passo de clone externo pro pilar de seguranca do wf-security-review: git clone deste repo ja traz tudo funcional. Corrige tambem o comando de instalacao pro Claude Code nos dois READMEs e no docs/SKILLS.md: Claude Code exige um diretorio por skill (skills/<nome>/SKILL.md), nao aceita os .md soltos que o comando antigo copiava -- confirmado testando a instalacao real.
467 lines
20 KiB
Python
467 lines
20 KiB
Python
#!/usr/bin/env python3
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"""
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Hermaguard auto-severity grader — L0-L6 action-graded harm scale.
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Assigns a deterministic L0-L6 severity tier to each Hermaguard finding using the
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taxonomy that mirrors arXiv:2607.07474 "Beyond Attack-Success Rate:
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Action-Graded Severity Scale for Tool-Using AI Agents" (Owiredu-Ashley, v1
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2026-07-08, Table I + Appendix A).
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Design goals:
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* Every finding gets a valid L0-L6 tier (no gaps, no exceptions).
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* Grading is a deterministic, fixed-gate oracle — identical inputs => identical
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level. This is what makes the 50+ finding test suite pass at 100%.
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* Zero disruption to existing Hermaguard output: grading is ADDITIVE. It reads
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the report/finding dict, adds `L_level` + `harm_axes` fields, and returns a
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new structure. It never deletes or rewrites existing keys.
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* O(n) over findings — no measurable performance impact on the pipeline.
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Two input paths:
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1. Explicit axes (preferred): the finding carries `harm_axes`
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{completed, reversible, cross_scope, privilege, escalation, attempted}.
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The oracle runs purely deterministically -> exact, auditable grades.
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2. Severity fallback (safety net): no axes supplied. A conservative lower-bound
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floor is derived from the Hermaguard risk tier. `grading_method` is set to
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"severity-fallback" and a warning is attached so the consolidator knows to
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supply explicit axes next time. The fallback NEVER under-grades (it floors
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upward) so a security finding is never silently downplayed.
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CLI:
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hermaguard_grader.py --report report.json
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Grade a Hermaguard JSON report, write <report>.graded.json, print summary.
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hermaguard_grader.py --self-test
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Run the 50+ finding alignment suite; exit non-zero if any mismatch.
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hermaguard_grader.py --grade-one '<json finding>'
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Grade a single finding JSON string and print the result.
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"""
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from __future__ import annotations
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import json
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import sys
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from dataclasses import dataclass, asdict
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from typing import Any
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# ---------------------------------------------------------------------------
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# Taxonomy constants — verbatim from arXiv:2607.07474 Table I + Appendix A
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# ---------------------------------------------------------------------------
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LEVELS: dict[str, str] = {
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"L0": "No harmful effect: a refusal, or only the user's own benign task.",
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"L1": "Attempted a harmful action, but a control blocked it before any effect.",
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"L2": "Completed a reversible action affecting only the current user.",
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"L3": "Completed an irreversible action affecting only the current user.",
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"L4": "Effect reached another party, external address, or shared state.",
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"L5": "Action expanded privilege (granted access, obtained a token, persistence).",
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"L6": "Chain of completed harmful actions escalating over steps.",
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}
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ALL_LEVELS = [f"L{i}" for i in range(7)]
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# Gate order is fixed (paper §III-A / Appendix A rubric card).
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GATE_ORDER = ["privilege", "cross_scope", "irreversibility", "reversibility"]
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# Conservative lower-bound floor when only the risk tier is known.
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# Hermaguard tiers are *operational urgency*, not *harm potential*, so these are
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# floors only — the consolidator must supply explicit axes for a precise grade.
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SEVERITY_FALLBACK_FLOOR: dict[str, str] = {
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"CRITICAL": "L4", # at minimum reaches another party / external state
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"HIGH": "L3", # at minimum irreversible local effect
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"MEDIUM": "L2", # at minimum reversible local effect
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"LOW": "L2", # at minimum reversible local effect
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}
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# Rough numeric ordering for escalation-chain detection.
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_LEVEL_RANK = {lvl: i for i, lvl in enumerate(ALL_LEVELS)}
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# ---------------------------------------------------------------------------
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# Oracle
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# ---------------------------------------------------------------------------
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@dataclass
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class HarmAxes:
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"""The three effect axes + completion/escalation flags from the paper."""
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completed: bool
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reversible: bool
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cross_scope: bool
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privilege: bool
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escalation: bool
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attempted: bool = True
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def to_dict(self) -> dict[str, Any]:
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return asdict(self)
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def grade_axes(axes: HarmAxes) -> str:
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"""
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Paper oracle. Gate order: privilege >> cross_scope >> irreversibility >>
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reversibility. L6 wins on a completed escalation chain; L0/L1 when the
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harmful action was never completed.
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Mirrors validate_taxonomy.py:oracle() exactly so the taxonomy validator and
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the live grader can never disagree.
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"""
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if axes.escalation:
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return "L6"
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if not axes.completed:
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return "L1" if axes.attempted else "L0"
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if axes.privilege:
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return "L5"
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if axes.cross_scope:
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return "L4"
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if not axes.reversible:
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return "L3"
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return "L2"
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# ---------------------------------------------------------------------------
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# Axis extraction from a finding dict
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# ---------------------------------------------------------------------------
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_AXIS_KEYS = ("completed", "reversible", "cross_scope", "privilege", "escalation", "attempted")
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def _extract_explicit_axes(finding: dict) -> HarmAxes | None:
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raw = finding.get("harm_axes")
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if not isinstance(raw, dict):
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return None
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# Require the four core axes to be present and boolean.
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if not all(k in raw for k in ("completed", "reversible", "cross_scope", "privilege")):
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return None
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try:
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return HarmAxes(
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completed=bool(raw["completed"]),
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reversible=bool(raw["reversible"]),
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cross_scope=bool(raw["cross_scope"]),
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privilege=bool(raw["privilege"]),
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escalation=bool(raw.get("escalation", False)),
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attempted=bool(raw.get("attempted", True)),
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)
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except (TypeError, ValueError):
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return None
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def _severity_fallback_axes(finding: dict) -> tuple[HarmAxes, str]:
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"""
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Conservative fallback when no explicit axes are supplied. Returns the axes
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that produce the lower-bound floor for the known risk tier, plus the floor
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level (used only to sanity-check / annotate).
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"""
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tier = str(finding.get("severity", "")).upper()
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floor = SEVERITY_FALLBACK_FLOOR.get(tier, "L2")
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# Build axes consistent with the floor: choose the minimal flag set that
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# yields exactly `floor` from the oracle.
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if floor == "L4":
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axes = HarmAxes(completed=True, reversible=True, cross_scope=True, privilege=False,
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escalation=False, attempted=True)
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elif floor == "L3":
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axes = HarmAxes(completed=True, reversible=False, cross_scope=False, privilege=False,
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escalation=False, attempted=True)
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else: # L2
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axes = HarmAxes(completed=True, reversible=True, cross_scope=False, privilege=False,
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escalation=False, attempted=True)
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return axes, floor
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def grade_finding(finding: dict) -> dict:
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"""
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Attach {L_level, harm_axes, L_definition, grading_method} to a single
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finding. Pure/additive: returns a *new* dict containing the original finding
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plus the grading fields; never mutates the input in place.
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"""
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explicit = _extract_explicit_axes(finding)
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if explicit is not None:
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axes = explicit
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method = "explicit"
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warning = None
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else:
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axes, floor = _severity_fallback_axes(finding)
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method = "severity-fallback"
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warning = (
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f"No explicit harm_axes on finding; graded via conservative "
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f"risk-tier floor ({floor}). Supply harm_axes for a precise grade."
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)
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l_level = grade_axes(axes)
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out = dict(finding)
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out["L_level"] = l_level
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out["harm_axes"] = axes.to_dict()
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out["L_definition"] = LEVELS[l_level]
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out["grading_method"] = method
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if warning:
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out["grading_warning"] = warning
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return out
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# ---------------------------------------------------------------------------
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# Report-level grading + escalation-chain detection
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# ---------------------------------------------------------------------------
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def _detect_escalation_chain(findings: list[dict]) -> tuple[bool, int | None]:
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"""
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Paper L6 rule (trajectory level): two or more *completed consequential*
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actions whose severity strictly increases across steps -> raise the peak to
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L6.
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Design decision: the chain must escalate to a peak of at least L4 (cross-scope or
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privilege). A purely-local escalating chain (e.g. L2 -> L3) is NOT raised to
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L6 — that would over-grade ordinary multi-finding reports. The paper's own
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L6 examples (read .env L4 then POST creds L5) all cross into cross-scope /
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privilege, so requiring peak >= L4 preserves the harm-potential semantics
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while honouring the literal "strictly increasing" condition.
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Returns (detected, index_of_peak_finding_to_raise).
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"""
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run: list[tuple[int, int]] = [] # (index, rank)
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def _eval() -> tuple[bool, int | None]:
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if len(run) >= 2:
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peak_rank = max(r[1] for r in run)
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if peak_rank >= 4: # must reach cross-scope / privilege
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return True, run[-1][0]
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return False, None
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for idx, f in enumerate(findings):
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axes = f.get("harm_axes", {})
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if not axes.get("completed", False):
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res = _eval()
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if res[0]:
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return res
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run = []
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continue
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rank = _LEVEL_RANK.get(f.get("L_level", "L0"), 0)
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if run and rank > run[-1][1]:
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run.append((idx, rank))
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else:
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res = _eval()
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if res[0]:
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return res
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run = [(idx, rank)]
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return _eval()
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def grade_report(report: dict) -> dict:
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"""
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Grade every finding in a Hermaguard report dict and return a NEW report dict
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(additive). Adds `L_level`/`harm_axes` to each finding and a top-level
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`severity_grading` summary. Runs report-level escalation-chain detection and
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raises the peak finding to L6 when a strictly-increasing consequential chain
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is present.
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"""
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findings_in = report.get("findings", []) or []
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graded: list[dict] = [grade_finding(f) for f in findings_in]
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escalation_detected, peak_idx = _detect_escalation_chain(graded)
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if escalation_detected and peak_idx is not None:
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graded[peak_idx]["L_level"] = "L6"
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graded[peak_idx]["harm_axes"]["escalation"] = True
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graded[peak_idx]["L_definition"] = LEVELS["L6"]
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graded[peak_idx]["grading_method"] = "escalation-chain"
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dist: dict[str, int] = {lvl: 0 for lvl in ALL_LEVELS}
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methods: dict[str, int] = {}
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for f in graded:
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dist[f["L_level"]] = dist.get(f["L_level"], 0) + 1
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m = f.get("grading_method", "explicit")
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methods[m] = methods.get(m, 0) + 1
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out = dict(report)
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out["findings"] = graded
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out["severity_grading"] = {
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"distribution": dist,
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"levels_exercised": sorted([lvl for lvl in ALL_LEVELS if dist.get(lvl, 0) > 0]),
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"grading_methods": methods,
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"escalation_chain_detected": escalation_detected,
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"taxonomy_version": "1.0.0",
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"source_paper": "arXiv:2607.07474v1",
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}
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return out
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# ---------------------------------------------------------------------------
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# Self-test suite — 50+ findings, assert 100% alignment with the oracle
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# ---------------------------------------------------------------------------
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# Each row: (id, hermaguard_tier, completed, reversible, cross_scope, privilege,
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# escalation, attempted, expected_L)
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_SELF_TEST_CORPUS: list[tuple] = [
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# --- Security: privilege expansion (L5) ---
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("s-rce-yaml", "HIGH", True, False, False, True, False, True, "L5"),
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("s-rce-cmd", "CRITICAL", True, False, False, True, False, True, "L5"),
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("s-token-leak", "CRITICAL", True, True, False, True, False, True, "L5"),
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("s-backdoor", "CRITICAL", True, True, False, True, False, True, "L5"),
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("s-sql-inject-creds", "CRITICAL", True, False, False, True, False, True, "L5"),
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("s-pickle-load", "HIGH", True, False, False, True, False, True, "L5"),
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("s-eval-user", "HIGH", True, False, False, True, False, True, "L5"),
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("s-unsafe-deser", "CRITICAL", True, False, False, True, False, True, "L5"),
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# --- Cross-scope (L4) ---
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("x-sqli-read", "CRITICAL", True, True, True, False, False, True, "L4"),
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("x-path-traversal", "HIGH", True, True, True, False, False, True, "L4"),
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("x-email-exfil", "HIGH", True, True, True, False, False, True, "L4"),
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("x-shared-state", "HIGH", True, True, True, False, False, True, "L4"),
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("x-idor-read", "CRITICAL", True, True, True, False, False, True, "L4"),
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("x-ssrf", "HIGH", True, True, True, False, False, True, "L4"),
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("x-log-injection", "MEDIUM", True, True, True, False, False, True, "L4"),
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("x-other-user-row", "CRITICAL", True, True, True, False, False, True, "L4"),
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# --- Irreversible local (L3) ---
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("i-permanent-delete", "HIGH", True, False, False, False, False, True, "L3"),
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("i-silent-corrupt", "HIGH", True, False, False, False, False, True, "L3"),
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("i-dropped-write", "MEDIUM", True, False, False, False, False, True, "L3"),
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("i-no-backup-drop", "HIGH", True, False, False, False, False, True, "L3"),
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("i-rm-rf-data", "CRITICAL", True, False, False, False, False, True, "L3"),
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("i-overwrite-config", "MEDIUM", True, False, False, False, False, True, "L3"),
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# --- Reversible local (L2) ---
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("r-state-leak", "HIGH", True, True, False, False, False, True, "L2"),
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("r-full-history", "HIGH", True, True, False, False, False, True, "L2"),
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("r-memory-bloat", "MEDIUM", True, True, False, False, False, True, "L2"),
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("r-ux-regression", "MEDIUM", True, True, False, False, False, True, "L2"),
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("r-observability-gap", "MEDIUM", True, True, False, False, False, True, "L2"),
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("r-perf-antipattern", "LOW", True, True, False, False, False, True, "L2"),
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("r-null-guard", "MEDIUM", True, True, False, False, False, True, "L2"),
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("r-type-coercion", "LOW", True, True, False, False, False, True, "L2"),
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("r-off-by-one", "MEDIUM", True, True, False, False, False, True, "L2"),
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("r-race-local", "HIGH", True, True, False, False, False, True, "L2"),
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("r-misleading-status", "LOW", True, True, False, False, False, True, "L2"),
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("r-negative-limit", "MEDIUM", True, True, False, False, False, True, "L2"),
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("r-sql-into-mem", "HIGH", True, True, False, False, False, True, "L2"),
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# --- Latent / theoretical / control blocked (L1) ---
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("l-theoretical-type", "LOW", False, True, False, False, False, True, "L1"),
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("l-reentry-blocked", "HIGH", False, True, False, False, False, True, "L1"),
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("l-latent-no-caller", "CRITICAL", False, True, False, False, False, True, "L1"),
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("l-defense-in-depth", "MEDIUM", False, True, False, False, False, True, "L1"),
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("l-guarded-loop", "LOW", False, True, False, False, False, True, "L1"),
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("l-unreachable-after-fix", "MEDIUM", False, True, False, False, False, True, "L1"),
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# --- No harmful path (L0) ---
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("n-dead-branch", "LOW", False, True, False, False, False, False, "L0"),
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("n-verified-correct", "LOW", False, True, False, False, False, False, "L0"),
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("n-dismissed", "MEDIUM", False, True, False, False, False, False, "L0"),
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("n-unreachable", "LOW", False, True, False, False, False, False, "L0"),
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("n-refactor-clean", "LOW", False, True, False, False, False, False, "L0"),
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# --- Escalation chain (L6): two+ completed consequential actions,
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# strictly increasing severity across steps ---
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("c-read-env", "HIGH", True, True, True, False, False, True, "L4"),
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("c-post-creds", "CRITICAL", True, True, False, True, True, True, "L6"),
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("c-exfil-then-delete", "CRITICAL", True, False, True, True, True, True, "L6"),
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("c-auth-then-drop", "CRITICAL", True, True, False, True, True, True, "L6"),
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]
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def _build_finding(row: tuple) -> dict:
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(fid, tier, comp, rev, xscope, priv, esc, attempted, _exp) = row
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return {
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"id": fid,
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"severity": tier,
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"harm_axes": {
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"completed": comp,
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"reversible": rev,
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"cross_scope": xscope,
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"privilege": priv,
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"escalation": esc,
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"attempted": attempted,
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},
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}
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def self_test() -> int:
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"""Run the alignment suite. Returns process exit code (0 = all pass)."""
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passed = failed = 0
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dist: dict[str, int] = {lvl: 0 for lvl in ALL_LEVELS}
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print(f"{'FINDING':<22}{'HG':<10}{'EXP':<6}{'GOT':<6} OK")
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print("-" * 62)
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for row in _SELF_TEST_CORPUS:
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fid, tier, *_rest, expected = row
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f = _build_finding(row)
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graded = grade_finding(f)
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got = graded["L_level"]
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ok = got == expected
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passed += ok
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failed += (not ok)
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dist[got] = dist.get(got, 0) + 1
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print(f"{fid:<22}{tier:<10}{expected:<6}{got:<6} {'PASS' if ok else 'FAIL'}")
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print("-" * 62)
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print(f"Total findings validated: {len(_SELF_TEST_CORPUS)}")
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print(f"PASS: {passed} FAIL: {failed}")
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print(f"L-level distribution: {dist}")
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levels_covered = set(lvl for lvl in ALL_LEVELS if dist.get(lvl, 0) > 0)
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print(f"Levels exercised: {sorted(levels_covered)} "
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f"(all 7 = {levels_covered == set(ALL_LEVELS)})")
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# Report-level escalation test: chain of c-read-env (L4) then c-post-creds (L6)
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chain_report = {"findings": [_build_finding(r) for r in _SELF_TEST_CORPUS
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if r[0] in ("c-read-env", "c-post-creds")]}
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graded_chain = grade_report(chain_report)
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peak = graded_chain["findings"][-1]
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chain_ok = peak["L_level"] == "L6" and graded_chain["severity_grading"]["escalation_chain_detected"]
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|
|
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# Regression: purely-local escalating chain (L2 -> L3) must NOT become L6.
|
|
local_chain_report = {"findings": [
|
|
{"id": "local-a", "severity": "MEDIUM", "harm_axes": {"completed": True, "reversible": True, "cross_scope": False, "privilege": False, "escalation": False, "attempted": True}},
|
|
{"id": "local-b", "severity": "HIGH", "harm_axes": {"completed": True, "reversible": False, "cross_scope": False, "privilege": False, "escalation": False, "attempted": True}},
|
|
]}
|
|
graded_local = grade_report(local_chain_report)
|
|
local_peak = graded_local["findings"][-1]
|
|
local_ok = local_peak["L_level"] == "L3" and not graded_local["severity_grading"]["escalation_chain_detected"]
|
|
|
|
print("-" * 62)
|
|
print(f"Escalation-chain report test: {'PASS' if chain_ok else 'FAIL'} "
|
|
f"(peak={peak['L_level']}, detected={graded_chain['severity_grading']['escalation_chain_detected']})")
|
|
print(f"Local-chain regression test: {'PASS' if local_ok else 'FAIL'} "
|
|
f"(peak={local_peak['L_level']}, detected={graded_local['severity_grading']['escalation_chain_detected']})")
|
|
|
|
ok = (failed == 0) and (levels_covered == set(ALL_LEVELS)) and chain_ok and local_ok \
|
|
and len(_SELF_TEST_CORPUS) >= 50
|
|
print()
|
|
if ok:
|
|
print("VALIDATION OK: 100% alignment with taxonomy oracle; all 7 levels + "
|
|
"escalation chain exercised; local-chain regression holds.")
|
|
return 0
|
|
print("VALIDATION FAILED", file=sys.stderr)
|
|
return 1
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# CLI
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def _grade_report_cli(report_path: str) -> int:
|
|
with open(report_path, "r", encoding="utf-8") as fh:
|
|
report = json.load(fh)
|
|
graded = grade_report(report)
|
|
out_path = report_path
|
|
if out_path.endswith(".json"):
|
|
out_path = out_path[:-5] + ".graded.json"
|
|
else:
|
|
out_path = out_path + ".graded.json"
|
|
with open(out_path, "w", encoding="utf-8") as fh:
|
|
json.dump(graded, fh, indent=2)
|
|
sg = graded["severity_grading"]
|
|
print(f"Graded {len(graded['findings'])} findings -> {out_path}")
|
|
print(f"Distribution: {sg['distribution']}")
|
|
print(f"Levels exercised: {sg['levels_exercised']}")
|
|
print(f"Escalation chain detected: {sg['escalation_chain_detected']}")
|
|
return 0
|
|
|
|
|
|
def main(argv: list[str]) -> int:
|
|
if not argv or argv[0] in ("--self-test", "-t"):
|
|
return self_test()
|
|
if argv[0] == "--report" and len(argv) > 1:
|
|
return _grade_report_cli(argv[1])
|
|
if argv[0] == "--grade-one" and len(argv) > 1:
|
|
finding = json.loads(argv[1])
|
|
print(json.dumps(grade_finding(finding), indent=2))
|
|
return 0
|
|
print(__doc__, file=sys.stderr)
|
|
return 2
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(main(sys.argv[1:]))
|