Per user request: BMW (and others) put their own services AND external
vendors in the same cookie-policy widget. The VVT-Tabelle now groups
them by Art. 30(1)(d) DSGVO recipient category so the DSB can act on
the right buckets:
- INTERNAL — owner processing for itself ('BMW AG — XYZ')
- GROUP_COMPANY — same brand family, different legal entity ('BMW Bank')
- PROCESSOR — Auftragsverarbeiter, AVV-pflichtig (Adobe, Akamai)
- CONTROLLER — independent / joint controller (Meta Pixel, Google
Ads, LinkedIn — they run their own profiles)
- AUTHORITY — government bodies (rare in cookies)
- OTHER — fallback
New module vendor_classifier.py:
- owner_from_url(url) — derive site-owner token (bmw.de -> 'BMW',
mercedes-benz.de -> 'Mercedes-Benz')
- classify(name, category, owner) — strict 5-tier heuristic:
* INTERNAL: vendor name first-token is '<Owner>' / '<Owner> AG' /
'<Owner> SE' / '<Owner> GmbH' / '<Owner> AG & Co. KG'
* GROUP_COMPANY: starts with '<Owner> ' but isn't '<Owner> AG'
* CONTROLLER: matches a known joint-controller list (Meta, Google
Ads, YouTube, LinkedIn Insight, TikTok, Pinterest, Taboola,
Outbrain, Criteo, Twitter, Reddit, ...)
* PROCESSOR: legal-form suffix in name (GmbH, AG, Inc., A/S,
B.V., S.A., Ltd., LLC, ...)
* OTHER: anything else
vendor_extractor.extract_vendors_from_payloads now takes owner_name:
- Passes it through to classify() for every extracted vendor record
- The route derives owner_name via _company_name_from_url(doc_entries)
- LLM-extracted vendors are classified the same way (so V3 fallback
also produces tagged records)
agent_doc_check_extras.build_vvt_table_html rewritten:
- Buckets vendors by recipient_type
- Renders one section per non-empty bucket, in canonical order
(RECIPIENT_TYPE_SECTIONS), each with section header + count + bad
count + nested table
- Within each section: sorted by compliance_score ascending
- Response JSON cmp_vendors includes recipient_type so the frontend
can later import per-category into the VVT module
Expected BMW result: ~60 INTERNAL rows (BMW AG own services),
~25 PROCESSOR rows (Adobe, Adform, Akamai, AWS, ...), ~5 CONTROLLER
rows (Meta Pixel, Google, LinkedIn, Pinterest, Outbrain, Taboola).
When a known CMP (ePaaS, OneTrust) renders the cookie policy, we now
extract structured vendor records, probe their opt-out + privacy URLs,
score each vendor (0-100), and append a 'VVT-Vorschlag' table to the
compliance email — one row per vendor, sortable by compliance score.
consent-tester:
- DSIDiscoveryResult.cmp_payloads: surfaces raw CMP JSON to callers
- DSIDiscoveryResponse: new cmp_payloads field
- discover_dsi_documents sets cmp_payloads from cmp_capture
- cmp_library/{epaas,onetrust}.py: new extract_vendors(d) returning
list[VendorRecord]
backend:
- _fetch_text() now returns (text, cmp_payloads) tuple
- doc_entries store cmp_payloads per doc (mostly cookie)
- _autodiscover_missing forwards homepage payloads to the cookie entry
- New module vendor_extractor.py: dispatches ePaaS/OneTrust/generic
schemas; dedupes vendors across multiple payloads
- cookie_link_validator.py extended with validate_vendor_urls(vendors)
and score_vendors(vendors) — 0-100 score per vendor based on name,
purpose, country, opt-out reachable, privacy URL reachable, cookies
with names + expiry
- agent_doc_check_extras.build_vvt_table_html: renders the table
- Route appends VVT HTML after the provider list, before the
document-by-document report
- Response JSON gains cmp_vendors for future frontend rendering
Example for BMW: ~30 ePaaS providers → table with Name | Kategorie |
Sitz | Cookies | Opt-Out (✓/✗) | Privacy (✓/✗) | Score. Sorted by
score ascending so the worst-compliant vendors are at the top.
When the user leaves some doc-type rows empty, the tool now actively
searches the website for them — only marks 'not found' as last resort.
Flow:
1. User submits N URLs (e.g. just DSI)
2. For each canonical doc_type with no submitted URL/text, the route
identifies the most-common base (scheme://netloc) from submitted URLs
3. Calls consent-tester /dsi-discovery on the homepage with
max_documents=15 (180s timeout)
4. Classifies every discovered doc into a canonical doc_type via
title/URL keyword rules (_DISCOVERY_RULES — covers cookie/widerruf/
social_media/agb/nutzungsbedingungen/dsb/impressum/dse)
5. Fills matching empty entries with the discovered text, marks
auto_discovered=True and discovery_attempted=True
Padding now differentiates:
- 'Auf der Website nicht gefunden' — discovery was attempted, no doc
matched. Amber badge, friendly hint to add URL manually.
- 'Nicht eingereicht — Quelle nicht angegeben' — user gave NO URLs at
all, nothing to crawl from. Grey badge.
Email + frontend:
- Status labels: NICHT GEFUNDEN (amber) vs NICHT EINGEREICHT (grey)
- 'Gepruefte Quellen' table tags auto-discovered URLs with a small blue
'auto-entdeckt' badge so GF sees what tool found vs user submitted.
Implementation only runs when ≥1 URL was submitted (no base to crawl
from otherwise). Adds 30-90s for unsubmitted types but avoids the
'just say nicht gefunden' anti-pattern.
Benjamin Admin