anti
f211d394e6
Browser-payload derivations per Appendix A.9: navigator.webdriver flag, canvas/audio/WebGL automation hash matches (Puppeteer/Playwright/ Selenium/curl-impersonate), WebRTC IP leak, TZ/language vs source-IP geo mismatch, navigator.platform vs userAgent vs WebGL renderer inconsistency. Evidence shape pinned to CanaryFingerprintEvidence (metric + matched_signature) — raw fingerprint blobs (canvas hashes, full UAs, navigator.platform values) explicitly NOT carried into TTPTag.evidence per TTP_TAGGING.md §'Hard parts §7' (enrichment vs tag boundary). The identity-merge guard rail is preserved: composite fp.id matches across IPs are NOT a TTP, so no rule fires on the bare hash. Tests: tests/ttp/test_canary_fingerprint_lifter.py per-rule positive + negative + evidence-shape guard + state modulation. tests/ttp/rule_precision/test_canary_rules.py xfail flipped to real precision (R0049/R0050/R0051/R0053 H-band ≥95%; R0052 M-band ≥80%).
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
"""R0049-R0053 — canary fingerprint cohort.
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CanaryFingerprintLifter (E.3.11) parses the fingerprint payload
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(navigator/webdriver flag, canvas/audio/WebGL hashes, WebRTC IPs,
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TZ/language/geo composite) and emits per Appendix A.9. The v0
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:class:`RuleEngine` cannot navigate a structured fingerprint blob —
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these rules are inert under the regex matcher.
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"""
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from __future__ import annotations
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from collections.abc import Callable
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from pathlib import Path
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import pytest
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from decnet.ttp.impl.rule_engine import RuleEngine
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from decnet.ttp.store.base import RuleState
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from decnet.ttp.store.impl.filesystem import _parse_and_compile
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from tests.ttp.rule_precision.conftest import CorpusRow, make_event
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CohortLoader = Callable[[str], list[CorpusRow]]
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_RULE_IDS = [f"R{n:04d}" for n in range(49, 54)]
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@pytest.mark.parametrize("rule_id", _RULE_IDS)
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def test_rule_yaml_present(rule_id: str) -> None:
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path = Path("rules/ttp") / f"{rule_id}.yaml"
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assert path.exists(), f"missing YAML: {path}"
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compiled = _parse_and_compile(path, RuleState())
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assert compiled.rule_id == rule_id
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@pytest.mark.parametrize("rule_id", _RULE_IDS)
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async def test_lifter_bound_inert_in_v0(
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rule_id: str,
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precision_engine: RuleEngine,
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corpus_loader: CohortLoader,
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) -> None:
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fired: set[str] = set()
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for row in corpus_loader("canary"):
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tags = await precision_engine.evaluate(make_event(row))
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fired.update(tag.rule_id for tag in tags)
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assert rule_id not in fired
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def _build_lifter() -> "CanaryFingerprintLifter":
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from decnet.ttp.impl.canary_fingerprint_lifter import (
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CanaryFingerprintLifter,
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)
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from tests.ttp._stub_store import StubRuleStore
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rules = [
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_parse_and_compile(Path("rules/ttp") / f"{rid}.yaml", RuleState())
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for rid in _RULE_IDS
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]
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lifter = CanaryFingerprintLifter(StubRuleStore(compiled=rules))
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for rule in rules:
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lifter._index.install(rule)
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return lifter
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@pytest.mark.parametrize("rule_id", _RULE_IDS)
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def test_canary_rule_precision(
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rule_id: str,
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corpus_loader: CohortLoader,
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) -> None:
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"""E.3.11 — drive CanaryFingerprintLifter over the labelled corpus
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and assert per-rule precision (H-band rules → ≥95%, M-band → ≥80%).
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R0052 is M-band (0.7 confidence); the rest are H-band.
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"""
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import asyncio
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from tests.ttp.rule_precision.conftest import precision_for
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rows = corpus_loader("canary")
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if not rows:
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pytest.skip("no canary corpus available")
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lifter = _build_lifter()
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fired: dict[str, list[str]] = {}
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for row in rows:
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tags = asyncio.run(lifter.tag(make_event(row)))
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fired[row.label] = [tag.rule_id for tag in tags]
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precision, _tp, _fp = precision_for(rule_id, rows, fired)
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threshold = 0.80 if rule_id == "R0052" else 0.95
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assert precision >= threshold, (
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f"{rule_id} precision {precision:.2f} < {threshold} on canary corpus"
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)
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