fix(audit): VW-Cookie-Tabelle — Library-Fallback + Pattern-Extract verstaerkt
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VW-Lehre: cmp_vendors=6 (alle LLM-grob) wurde als ausreichend gewertet,
obwohl die echte Cookie-Tabelle 30+ Eintraege hat. 3 Fixes:

1. fallback_vendors_for_run skip-Schwelle: existing_vendor_count >= 3
   war zu niedrig. Jetzt nur skip wenn < 5 Cookies UND >= 5 Vendors
   schon vorhanden.

2. Library-Fallback wird jetzt aufgerufen bei < 20 cmp_vendors (statt
   < 3). VW-typische Setups (6 LLM-grob + 30 aus Library) bekommen
   damit eine vollstaendige Vendor-Liste.

3. _extract_cookie_names_from_doc: regex-Pattern-Extract aus dem
   Cookie-Doc-Text selbst — sucht nach 'NAME Tracking Cookies (Marketing)'
   etc. Findet Cookie-Namen die NICHT im Browser-Jar landen (z.B. nur
   nach Consent geladen werden). Diese werden zusaetzlich durch die
   Library matched.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
2026-05-21 18:32:07 +02:00
parent c281464071
commit 138d9068c4
2 changed files with 58 additions and 7 deletions
@@ -769,7 +769,10 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
# Cookie-Library-Fallback (P52 Lite): wenn weiterhin wenige
# Vendors aber viele after_accept-Cookies, aus Library auflösen.
if banner_result and len(cmp_vendors) < 3:
# VW-Lehre: 6 LLM-Grob-Vendors reichen NICHT — die Library
# holt 30+ weitere aus den Cookie-Namen + Cookie-Doc-Pattern.
# Schwelle: immer probieren wenn < 20 Vendors.
if banner_result and len(cmp_vendors) < 20:
try:
from compliance.services.cookie_to_vendor_fallback import (
fallback_vendors_for_run,
@@ -779,6 +782,7 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
try:
extra = fallback_vendors_for_run(
_fb_db, banner_result, len(cmp_vendors),
cookie_doc_text=cookie_text,
)
if extra:
existing_names = {(v.get("name") or "").strip().lower()
@@ -14,6 +14,7 @@ hat 28 Eintraege. Diese 28 Cookies sind in der Library = ~15-20 Vendors.
from __future__ import annotations
import logging
import re
from typing import Iterable
from sqlalchemy import text
@@ -80,21 +81,67 @@ def fallback_vendors_for_run(
db: Session,
banner_result: dict | None,
existing_vendor_count: int,
cookie_doc_text: str | None = None,
) -> list[dict]:
"""Returns extra vendor records to merge with the run's cmp_vendors.
Only fires when existing_vendor_count is suspiciously low (< 3) AND
we have enough cookies to look up (>= 5). Otherwise skip.
VW-Lehre: cmp_vendors=6 (alle LLM-grob) reicht NICHT die echte
Cookie-Tabelle hat 30+ Eintraege. Wir fuehren den Lookup jetzt auch
bei mid-tier-Counts aus, solange after_accept >= 15 Cookies hat
ODER der Cookie-Doc-Text Cookie-Tabellen-Signale enthaelt.
"""
if existing_vendor_count >= 3:
return []
names = _collect_cookie_names(banner_result)
if len(names) < 5:
# Erweitere names um Cookie-Namen die im Cookie-Doc-Text als
# Tabellen-Eintraege auftauchen (Pattern: NAME gefolgt von
# "Tracking Cookies"/"Session Cookies"/"Funktional"/...).
if cookie_doc_text:
names |= _extract_cookie_names_from_doc(cookie_doc_text)
# Skip-Bedingungen ueberarbeitet:
# - sehr wenige Cookies UND >= 5 Vendors schon vorhanden → skip
# - sonst IMMER versuchen
if len(names) < 5 and existing_vendor_count >= 5:
return []
if not names:
return []
vendors = lookup_vendors_from_library(db, names)
if vendors:
logger.info(
"Cookie-Library-Fallback: %d Vendors aus %d Cookies (vorher %d)",
"Cookie-Library-Fallback: %d Vendors aus %d Cookies "
"(existing cmp_vendors=%d)",
len(vendors), len(names), existing_vendor_count,
)
return vendors
_TABLE_ROW_RE = re.compile(
r"\b([A-Za-z_][A-Za-z0-9_\-\.]{2,40})\s+"
r"(?:Tracking Cookies|Session Cookies|Funktional|Marketing|"
r"Analytics|Performance|Notwendig|Strictly\s+Necessary|"
r"Statistik|Werbung|Targeting|Personalisierung)",
re.I,
)
def _extract_cookie_names_from_doc(text: str) -> set[str]:
"""Pattern-basiertes Erkennen von Cookie-Tabellen-Zeilen.
VW-Cookie-Tabelle hat Form:
'IDE Tracking Cookies (Marketing) Dieser Cookie ... 13 Monate'
Das fangen wir mit einem Cookie-Name-vor-Category-Pattern.
"""
out: set[str] = set()
for m in _TABLE_ROW_RE.finditer(text):
name = m.group(1).strip()
# Filter offensichtliche Noise (Pronomen, Verben)
nl = name.lower()
if nl in ("dieser", "diese", "ein", "der", "die", "das",
"session", "permanent", "funktional", "notwendig",
"marketing", "analytics", "werbung", "anbieter",
"google", "facebook", "tracking", "cookie", "cookies"):
continue
if len(name) >= 3:
out.add(name)
return out