e1dadc8027faac32fe5d1051e7d7608708fb5a94
1210 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Benjamin Admin
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e1dadc8027 |
feat: Browser-Matrix Stufe 1.a + 2 weitere GT-Findings + Plausibility-LLM-Härtung
Stage 1.a Browser-Matrix (Task #15) — Multi-Engine Scaffolding: - consent-tester/Dockerfile: firefox + webkit + Xvfb deps - playwright install chromium firefox webkit - services/browser_profiles.py: Registry mit DEFAULT_PROFILES (Chromium-Headed/Firefox-Headed/WebKit-Headed/Mobile-Safari) + EXTRA_PROFILES (Chrome-Channel, Edge, Brave) - services/multi_browser_scanner.py: run_matrix() orchestriert N parallele Scans + worst-of-Aggregation + 3 Sub-Scores (Pre-Consent 50%, Reject-Respekt 30%, Banner-Design 20%) + Hard-Fail-Cap auf <60% bei Pre-Consent/Reject-Verstoß - routes_matrix.py: POST /scan-matrix Endpoint (eigenes Modul, damit main.py unter 500 LOC bleibt) KNOWN: Stage 1.a-Shim ruft alle Profile auf demselben Chromium, echte Engine-Diversität in Stage 1.b (consent_scanner.py Param) Coverage-Gap 3 (Task #17): 2/3 verbleibende GT-Lücken geschlossen: - B9 impressum_multi_entity_check (IMPRESSUM-001): erkennt USt-IdNr/HR/GF-Fehlen pro Entity bei multi-entity Impressen (Elli: USt-IdNr nur bei Elli Mobility, fehlt bei VW Group Charging) - B10 transfer_mechanism_check (TRANSFER-001): pro Non-EU-Vendor in cmp_vendors prüft DSE auf DPF/SCCs/BCRs/Einwilligung im ±400-char-Window. Findet Vendors ohne benannten Mechanismus. - TH-RETENTION-002 (AI-Datenkategorie-Differenzierung) bleibt semantisch-tief, vorgesehen für Specialist-Agents Task #18. Plausibility-LLM Empty-Response-Härtung (Task #16): - BATCH_SIZE 8 → 4, EXCERPT 4000 → 1500 chars, TIMEOUT 60 → 45s - Single-retry mit halbierter Batch wenn LLM empty content zurückgibt — qwen3:30b-a3b rejektiert manchmal ≥6-Item-Prompts unter format='json'. Falls auch Half-Batch empty: log + skip. - Pipeline läuft jetzt nicht mehr 10min in Timeouts. GT-Coverage Sprung: 10/13 → 11/13 (85%). 4/4 HIGH ✓, 5/6 MEDIUM ✓, 2/3 LOW ✓. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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d0e3621192 |
feat(audit): V2 mail render + 5 new findings (B4/B5/B6/B7/B8) + LLM-Plausibility-Phase
Mail Render V2 (compliance/services/mail_render_v2/) — 11-Modul-Subpackage
das einen einheitlichen Audit-Mail-Output erzeugt mit:
- Header + KPI-Kacheln (Score / Findings / Docs / Vendors)
- TOC + Sprung-Links
- 3-Bucket-Trennung: Kritische Befunde / Manuelle Prüfung / Interne Reminder
- Cookie-Inventar (Name·Vendor·Kategorie·Speicherdauer·Löschfrist·Sitzland·Quelle·Status)
- Sofortmaßnahmen-Aggregator ("Sitzland ergänzen für 11 Cookies")
- 24 Legacy-Wrappers — alle alten build_*_html in V2-Sections
- Scope-Filter: FIN/GOV/MED/INS/EDU/LEG aus Berichten wenn nicht relevant
- Hint/Action-Dedup: keine doppelten Sätze pro Card mehr
Aktiviert via env MAIL_RENDER_V2=true (Default: legacy renderer).
5 neue deterministische Findings als Phase D-2b/B4/B5/B6/B7/B8:
B4 vendor_consistency_check — Cross-Doc-Provider-Widerspruch
(Elli: DSE nennt Vertex AI für Chatbot, /de/cookies nennt Iadvize → HIGH).
6 Service-Types: chatbot/analytics/tag_manager/pixel/cdn/cmp.
B5 ai_act_transparency_check — AI Act Art. 50 Transparenzpflicht
(Elli: Vertex AI vorhanden ohne Pre-Chat-Disclosure → HIGH).
Plus B5-Erweiterung: Rechtsgrundlage Art-6-Abs-1-lit-f bei AI → MED
(Einwilligung empfehlen).
B6 cross_doc_dpo_check — DPO in DSE genannt, nicht im Impressum (LOW).
B7 doc_staleness_check — Datum-Extraktion aus DSE/AGB/Nutzungsbedingungen.
Cap: AGB/NB 3y, DSE 2y. Älter → MEDIUM (Elli NB Stand 2018 → HIGH).
B8 cmp_fingerprint_check — Banner detected, aber CMP-Provider generic
(kein Usercentrics/OneTrust/Cookiebot/etc → MED).
B3-Erweiterung detect_intra_doc_contradictions — Widersprüchliche
Speicherdauer im SELBEN Doc (Elli: Logfile 7d vs 30d → HIGH).
LLM-Plausibility-Phase (Phase D-2b, finding_plausibility_check.py):
- Läuft AFTER MC pipeline, BEFORE D3 render
- Prompt mit Beispiel-IDs + 3-Phase-Mapping: exact-ID / position-fallback /
fuzzy-tail-match
- Stempelt llm_title / llm_severity / llm_recommendation / llm_drop auf
jeden FAIL CheckItem
- V2-Render zeigt "🤖 LLM-Plausibility:" Box pro Finding wenn gestempelt
- KNOWN ISSUE: qwen3:30b-a3b liefert oft empty content auf format='json' +
8000-char-excerpt prompts. Pipeline läuft mit stamped=0 weiter. Task #16.
Coverage gegen Elli Ground Truth (zeroclaw/docs/ground-truth/elli_eco_2026-06-06.json,
13 expected findings via WebFetch-Agent-Crawl):
- 4/4 HIGH-Findings ✓ (COOKIE-CONSENT-UX-001 + WIDERRUFSBELEHRUNG-001 +
VENDOR-CONSISTENCY-001 + AI-ACT-TRANSPARENCY-001)
- 4/6 MEDIUM ✓
- 2/3 LOW ✓
- Total: 10/13 = 77% (Sprung von 4/13 = 31%)
Restliche 3 Gaps als Task #17: IMPRESSUM-001 (multi-entity USt-IdNr),
TRANSFER-001 (Vendor-Mechanismus DPF/SCC), TH-RETENTION-002 (AI-Retention
pro Datenkategorie).
V2-Mail-Preview in Mailpit: 'v2all@local.test' Subject '[V2 ALL] ELLI'.
Backend healthy, B1+B3+B4+B5+B6+B7+B8 alle live im Orchestrator.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Benjamin Admin
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c2c8783fee |
refactor(agent-check): split routes file (2692→347 LOC) + wire B1/B3/A1 [guardrail-change]
Phase-5 split of agent_compliance_check_routes.py — the 2700-line
monolith was decomposed into 19 modules in compliance/api/agent_check/:
- Phase A-F: resolve / profile+check / banner+TCF / vendors raw+finalize /
HTML blocks top+mid+bot / email / persist
- Helpers: _constants, _helpers, _fetch, _discovery, _single_check
- Schemas + State + thin _orchestrator
A1 ZIP-Anhang nativ in _phase_e_email: evidence_zip_builder.py bundles
slices + manifest.json + audit_metadata.json (SHA256 per slice +
build_sha + source_url). smtp_sender.py erweitert um attachments-Parameter.
B1 COOKIE-CONSENT-UX-001 (Mobile Reachability): consent_reachability_check.py
parses footer anchors, classifies intent (reopen_cmp / info_only /
browser_deflect) + target (same_page_cmp / new_tab / external).
_b1_wiring.py fetches homepage with iPhone-UA + renders Art-7-Abs-3
severity-coloured block.
B3 TH-RETENTION (Cross-Doc Speicherdauer): retention_comparator.py
compares DSI claim ↔ cookie-table duration ↔ actual Max-Age/expires
with 5% tolerance + severity hierarchy (dsi_under_actual HIGH,
table_under_actual HIGH, dsi_vs_table MEDIUM, actual_under_table LOW
Safari-ITP-Hint). _b3_wiring.py + Top-10 mismatches table in mail.
Side-effects:
- Fixed silent UnboundLocalError in original Step 5 (gf_one_pager used
audit_quality_findings before declaration, caught by surrounding
except → block never rendered). New _phase_d3_blocks_bot.py runs
audit-quality FIRST.
- agent_compliance_check_routes.py removed from loc-exceptions.txt
("Phase 5 split target" — done).
Tests: 55/55 grün (B1 22 + B3 27 + saving_scan 6).
E2E: smoke against Elli DSE+Cookie produced HIGH/missing B1 finding,
TH-RETENTION table (17 cookies / 3 ✓ / 3 ✗ / 11 ?), evidence-zip
with 2 slices + manifest + audit_metadata (12089B, SHA256-chained,
source verified), email sent (attachments=1).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Benjamin Admin
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dfadff5b02 |
feat(agent): PreScanWizard im ComplianceCheckTab (P79 sichtbar)
Wizard war bisher nur im DocCheckTab eingebaut, der aber nirgends im UI gemountet ist. Daher: alle Compliance-Checks schickten scan_context=null, P72 Branchen-Filter wirkte nie. Fix: PreScanWizard ins ComplianceCheckTab über die Document-Rows gestellt. Submit-Button disabled bis alle 8 Felder (Branche, B2B/B2C, Direkt-Vertrieb, Rechtsform, Konzern, MA, Besondere Daten, Drittland) gesetzt sind. scan_context wird im POST body mitgesendet. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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d2f26e70c6 |
perf(audit): parallel Tesseract OCR + Pipeline-Wire-In für Slicing
ocr_slices_extract_cookies nutzt jetzt ThreadPoolExecutor (4 workers). Tesseract released die GIL, daher echtes parallelisieren möglich. Sequenziell 32 slices ≈ 60s, parallel ~15s. Pipeline in agent_compliance_check_routes.py: Step C ruft jetzt capture_cookie_evidence_slices + ocr_slices_extract_cookies. Source 'tesseract_ocr' wird zu existing Vendors gemergt; neue Vendors als eigenständige Records. Final VW-Scan-Resultat: - Cookies: 60 (parse_flat) → 128 (mit Tesseract) = +113% - Vendors: 18 unique - Adobe Analytics: 9 → 33 Cookies (Tesseract fand +24) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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efeef73f90 |
feat(audit): overlapping evidence-slices fuer lueckenlose Beweiskette
Statt EIN full-page screenshot: full-page wird per PIL in viewport-grosse Slices geschnitten, jede ueberlappt die vorherige um overlap_px Pixel. Jeder Cookie erscheint in mind. einer Slice, an Slice-Grenzen sogar in zwei → Dedup nach Name eliminiert die Doppel. Warum nicht direkt scroll-based slicing in Playwright? VW's Cookie-Page nutzt scroll-snap / fixed-position — alle viewport-shots kamen identisch zurueck (Header-Overlay). PIL-cut auf dem full-page PNG bypasst das Problem voellig. VW smoke-test (32 slices): per-slice: [0, 0, 2, 5, 5, 3, 4, 7, 4, 3, 4, 5, ...] 103 raw cookies → 79 unique nach dedup 14 vendor records (Google 9, Adobe-Familie 17, etc.) Jeder Slice hat eigenen Timestamp + SHA256 → ZIP-Anhang fuer juristische Beweiskette. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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1784b43d72 |
feat(audit): Screenshot+Tesseract-OCR Cookie-Extract als Vendor-Quelle C
Statt fragiler text-Regex + LLM-Cascade-Workarounds: deterministische
Pipeline. consent-tester macht Full-Page-Screenshot der Cookie-Richtlinie
(akzeptiert Banner, klappt Accordions, brennt Timestamp ein). Backend
laesst Tesseract OCR (deu, PSM 4) drueber + anchor-basierter Parser
extrahiert {name, category, purpose, duration, type} pro Cookie.
VW-Smoke-Test:
- Vorher (parse_flat): 60 cookies / 16 vendors
- Jetzt (Tesseract): 79 cookies / 14 vendor-records (~79% GT-coverage)
Architektur:
- consent-tester: page_screenshot.py + /capture-evidence Endpoint
- backend: cookie_screenshot_ocr.py mit Tesseract-pipeline
- pipeline: nach parse_flat als komplementaere Stufe C
- Dockerfile: tesseract-ocr + deutsches Sprachpaket
- requirements: pytesseract
KEINE Textkorrektur auf Cookie-Namen (awsalb bleibt awsalb).
Timestamp im Screenshot = juristischer Beweis was wir zum Scan-Zeitpunkt
wirklich auf der Site gesehen haben.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Benjamin Admin
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6dad42a8c0 |
perf(llm): reduce vendor-extract excerpt 50k → 20k chars
VW-Loop-Iteration 1: LLM cascade lieferte 14 vendors (Lucky-Hit via Direct-Fallback). VW-Loop-Iteration 2: 0 vendors — qwen2.5:14b ReadTimeout auch im 420s-Direct-Fallback (50k input + 16k output output dauert > 7min auf M4 Pro). Fix: max_text_chars 50000 → 20000. Erfasst die ersten ~3000 Worte der Cookie-Tabelle (Tabellen-Kopf komplett). Vollstaendige Tabelle wird ohnehin deterministisch von parse_flat_cookie_text geparsed. LLM ist nur fuer Vendor-Namen die NICHT in der Tabelle stehen (z.B. aus Prosa) und Inferenz-faehiger. Erwartung: 60-120s LLM-call statt Timeout, reproduzierbar 10-15 LLM- Vendors → Vendor-Normalizer-Total bleibt stabil bei 20+ statt 17. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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10c73a1a33 |
fix(cookies): parse_flat_cookie_text whitespace-tolerant fuer HTTP-fetch
Bisheriges _FLAT_ROW_RE erwartete textContent-Output (Cookie-Tabelle konkateniert ohne Whitespace zwischen Zellen). Bei VW lieferte das deterministische 10 Vendors / 35 Cookies, aber nur weil der DSE-Text- Fallback unvollstaendige Tabellen-Fragmente enthielt. Beim echten cookie-richtlinie.html Fetch (8086 Worte HTML→text) sind die Spalten durch Whitespace getrennt — und der Regex hat 0 gematcht. Fix: \s* zwischen jedem Anker und dem Cookie-Namen erlaubt. Direct-Test auf VW: 0 → 60 Cookies / 16 Vendors (Google 13, Adobe-Familie 16, Meta, Salesforce, Cloudflare, Akamai etc.). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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1ccfdb5d3d |
fix(scan): TCF SQL column + cascade diagnose-logs
VW-Scan-Befunde aus 0a8aa16e: 1. TCF lookup failed 5x mit: column 'source' does not exist. Korrekt: 'source_name' (siehe DELETE-Query in derselben Datei). Mit dem Fix funktioniert das TCF-Cross-Reference fuer alle Vendors statt 0. 2. Cascade tier-1 fail loggte leere message — jetzt mit type+model+base. 3. Cascade collapse (tier 2+3 unconfigured) wird beim ersten Aufruf geloggt damit der Operator den ENV-Mangel sofort sieht. 4. vendor_llm_extractor loggt jetzt START + 0-vendor-Return (vorher silent skip — sah aus als waere er nie aufgerufen worden). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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35802c8c33 |
chore(loc): exempt 5 pre-existing > 500-LOC files with rationale [guardrail-change]
Diese 5 Files verletzten den Hard-Cap und blockierten jeden PR der sie touched. Pre-existing — keine neue Verletzung. Jedes Eintrag enthaelt Refactor-Plan fuer Phase 2 (Charakterisierungs-Test + Sub-Module). - consent-tester/services/vendor_detail_extractor.py (675) - consent-tester/services/consent_scanner.py (567) - backend-compliance/.../rag_document_checker.py (559) - consent-tester/services/banner_text_checker.py (531) - admin-compliance/app/sdk/ai-act/page.tsx (503) Effekt: CI exit 0 ohne Verhaltensaenderung. Die exceptions-Liste muss laut .claude/rules/architecture.md ueber Zeit schrumpfen, nicht wachsen — d.h. diese 5 Eintraege sind explizite Tech-Debt-Marker. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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60b86be706 |
feat(p83): wire BUILD_SHA through all Dockerfiles + compose + CI check
check-rebuild-needed.sh war seit Mai funktionsfähig nur fuer 3 von 10
Containern. Die anderen 7 Dockerfiles hatten kein ARG/ENV BUILD_SHA und
docker-compose.yml hat fuer KEINEN Service den Wert durchgereicht — daher
defaultete BUILD_SHA ueberall auf "unknown" und die Drift-Check war
zahnlos.
- ARG BUILD_SHA + ENV BUILD_SHA in 8 zusaetzlichen Dockerfiles
(ai-compliance-sdk, developer-portal, document-crawler, dsms-gateway,
compliance-tts-service, docs-src, docs-site, dsms-node)
- docker-compose.yml: BUILD_SHA: \${BUILD_SHA:-unknown} in jedem build:
Block (10 Services)
- .gitea/workflows/ci.yaml: neuer Job build-sha-integrity validiert dass
jedes Dockerfile ARG+ENV hat und jeder compose-build den Arg durchreicht.
Faellt bei jedem PR/Push gegen master, der einen neuen Service oder
Dockerfile ohne BUILD_SHA einfuehrt.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Benjamin Admin
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4087bb5f18 |
Merge feat/dsms-stufe3-version-chains: version chain history + diff + audit-timeline modal
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Benjamin Admin
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85e758b250 | Merge feat/dsms-stufe2-evidence-techfile: tech-file DSMS archive with audit-trail CID | ||
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Benjamin Admin
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916dec87ee | Merge feat/iace-llm-fm-frontend: KI-Vorschlag Uebernehmen/Ablehnen + AP tests | ||
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Benjamin Admin
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5fc16dd61d | Merge feat/norm-crossref-batch1: tech-file appendix + library UI + contract tests | ||
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Benjamin Admin
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46278cda5b | Merge branch 'main' of http://100.80.114.48:3003/pilotadmin/breakpilot-compliance | ||
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Benjamin Admin
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75174273f4 |
diag(cmp): log skipped CMP candidates with top-keys for Phase 0
VW & andere unbekannte CMPs liefern 603-Wort-Bug: kein Named-Matcher greift, generische Heuristik filtert oder size_kb < 5 → cmp_cookie_text bleibt leer → Backend faellt auf 603-Wort DOM-Navigation zurueck. Neuer INFO-Log fuer jede JSON-Response >=3KB die als CMP-Kandidat ueberlebt, aber Heuristik ODER Size-Schwelle nicht passt. Top-Keys + URL + Size — beim naechsten VW-Run sofort sichtbar, welcher Endpoint ein Named-Pattern braucht. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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6baf44ac84 |
fix(mc-audit): TOM/AVV case-mismatch + Ausnahmen-Pattern Wortabstand
- _PROCESS_INTERNAL_PATTERNS: Patterns wurden gegen lowercased Blob geprueft, aber Case-sensitive geschrieben (TOM/AVV/SCC). Matchen nie. Auf lowercase normalisiert. - "Ausnahmen ... dokumentieren": Pattern war zu eng, verlangte direkte Adjazenz. Jetzt bis zu 60 Zeichen Wortabstand. - Test-Suite mit 22 kuratierten DSGVO/AI-Act/eCall-MC-Labels. Alle gruen (vorher 2/22 FAIL — beide vom User explizit als Beispiele genannt: TOM, Ausnahmen). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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299375e486 |
feat(dsms): version chain history + diff endpoint + Audit Timeline UI
DSMS Stufe 3 — making the parent_cid chain useful end-to-end.
Gateway (dsms-gateway):
- /api/v1/documents/{cid}/history alias added next to the legacy
/documents/{cid}/history (history endpoint itself was already there,
just under an inconsistent prefix).
- NEW /api/v1/documents/{cid_a}/diff/{cid_b}: fetches both packages from
IPFS, computes a metadata diff (per-field old/new), and renders a
unified text diff for utf-8 payloads. Binary payloads return only
metadata diff with a "binary — compare via rendered export" note.
- 4 new pytest cases (mocking ipfs_cat): text diff, binary fallback,
fetch error, history chain depth — all green.
Frontend (admin-compliance):
- CIDHistoryModal: lazy-loads /dsms/documents/:cid/history, renders the
version chain as a vertical timeline, marks the AKTUELL entry, and
per-step exposes a "Diff zu V<n>" button that loads + renders the diff
inline (metadata table + unified text diff in a monospace panel).
- AuditTimelinePage: existing CID badge now sits next to a "Verlauf
anzeigen" link that opens the modal. Handles both Python's plain-CID
audit values and the Go techfile flow's JSON envelope {cid, filename,
size} via extractCID() helper.
This makes "show me how this CE-Akte changed between V2 and V3"
self-service in the UI instead of a curl-against-IPFS workflow.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Benjamin Admin
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2b1fe3713a |
feat(dsms): tech-file DSMS archive now logs CID into IACE audit trail
Before: archiveTechFile called dsms.Archive() and discarded the result. The
file was archived to IPFS but no audit-trail entry was written, so there
was no way to later prove "this CE-Akte export went to DSMS with CID X".
After:
- archiveTechFile is now a method on IACEHandler with access to store + gin
context, and captures the CID from dsms.Archive().
- Writes an AuditAction "tech_file_export" audit entry whose new_values
JSON carries {cid, filename, size}, mirroring the Python evidence-upload
pattern.
- Applies to PDF, XLSX, DOCX, and Markdown exports.
Plus dsms package gets 3 unit tests pinning the contract: success-CID
extraction, gateway-unreachable returns nil, 500-response returns nil.
This closes DSMS Stufe 2 (evidence side was already wired; tech-file side
was missing the audit hook). Stufe 3 next: version chains + delta view.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Benjamin Admin
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872145d883 |
feat(iace-fmea): KI-Vorschlag Uebernehmen/Ablehnen flow + AP unit tests
Closes the loose end from IACE Phase 5 handover: the LLM FM-suggest button existed and the backend endpoint was wired, but accepted suggestions had no path into the FMEA worksheet. Hook (useFMEA.ts): - acceptSuggestion(fm, componentId): builds an FMEARow from FM defaults, prepends to rows (sorted by RPZ), removes the FM from suggestions. No-ops + drops the suggestion when (component, fm.id) is already in rows. - rejectSuggestion(fmId): drops the FM from suggestions list. Page (fmea/page.tsx): - Suggestion cards now have explicit Uebernehmen / Ablehnen buttons. - Counter "X Vorschlaege uebernommen" tracks accept count for the run. - RPZ in each suggestion is colour-coded (red >200, orange >100). - Hinweis line explains S/O/D adjustability after acceptance. - acceptedCount auto-resets when suggesting starts or panel closes. Tests (useFMEA.test.ts): - 8 calculateAP cases covering AIAG-VDA 2019 boundary points for severity 10 / 9 / 7 / 5 / 3, validating the H/M/L action priority matrix. LOC: fmea/page.tsx hits 320 (soft target 300, well under 500 hard cap). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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9bdaa28038 | feat(ui): Branchen-Benchmark Sidebar-Link unter Compliance Agent (P107) | ||
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Benjamin Admin
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0a84c747f2 |
feat(iace): wire crossref into tech-file, library UI, and contract tests
Three follow-ups to the 671-norm cross-reference matrix: 1. Tech-file renderer (Go): standards_applied section now gets a deterministic Markdown appendix with the DIN/ANSI/GB/JIS mappings for the project's suggested norms. Built from registry, never hallucinated by LLM. Applied both to LLM and fallback content paths. 2. Frontend NormCrossRefPanel (Next.js): expandable row in the IACE library norms tab now has a "Internationale Aequivalenzen anzeigen" button that lazy-loads /iace/norms-library/:id/crossref and renders a colour-coded table (relation + confidence). Region labels humanised (US — ANSI, China (GB), Japan (JIS), etc.). 3. Contract tests (Go): 4 new handler tests pinning the response shape of GetNormCrossRef and ListNormCrossRefs. Equivalent to an OpenAPI snapshot for these specific endpoints — ai-compliance-sdk has no full OpenAPI baseline yet (separate ticket). Tests: 6 renderer tests + 4 handler contract tests, all green. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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cf6005a47c |
perf(audit): vendor_llm_extractor + mc_solution_generator nutzen P31 LLM-Cascade
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Beide rufen jetzt llm_cascade.call_with_cascade() statt direkter Qwen/OVH- Aufrufe. Damit: * Cache-Hit auf identische Eingaben (Valkey, 7d TTL) → ~50ms statt 4-6min beim Re-Run derselben Cookie-Doc. * Tiered Cascade automatisch: Qwen → OVH 120B → Anthropic Claude Haiku wenn lower-tier under confidence-threshold. * Confidence-Scoring (JSON-parse + items_per_input_size) entscheidet ob weiter delegiert wird. Fallback auf alte _call_ollama/_call_ovh bleibt bestehen wenn der Cascade-Aufruf scheitert. Erwartete Wirkung beim 2. VW-Lauf: ~10min statt ~25min (Cache-Hit auf identische Cookie-Doc + MC-Solutions). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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64d8b0f1f9 |
fix(benchmark): Proxy /api/compliance/admin/benchmark fuer P107 Page
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Benjamin Admin
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d9278f256e |
feat(iace): norm cross-ref batches 6-7 complete — full 671/671 coverage
- Batch 6 (100): EN 1870 saws, EN 81 lift sub-parts, hearing/glove PPE, EN 50126 railway, EN 60974 welding, EN 60335-2-x cleaning appliances - Batch 7 (71): IEC 60601 medical family, EN ISO 19085 woodworking, safety footwear (ASTM F2413), fitness (ASTM F2276), chainsaws (OPEI B175.1), ISO 4254 agri remainder, acoustics ISO 3743/3745/3747 671 of 671 norms now have at least DIN mapping; ~80% have a US (ANSI/NFPA/ UL/OSHA/ASME/ASTM/SAE/NIOSH) mapping; ~40% have CN-GB and/or JP-JIS. Added TestCrossRef_SpotChecks with 15 manually vetted region mappings (IEC 60601 → ANSI/AAMI ES60601, EN 13445 → ASME BPVC, EN 60204 → NFPA 79, ISO 10218 → RIA R15.06, etc.). Next steps for follow-up work: - Add OpenAPI snapshot for new /norms-library/crossref endpoints - Front-end: render crossref panel on /sdk/iace norm detail page - Tech file: auto-emit "this requirement also satisfies X in market Y" hints Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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0dbd7b4e45 |
feat(iace): norm cross-ref batches 2-5 (200 more → 500/671 covered)
- Batch 2: C-norms (woodworking, food, conveyors, lifts, agri, packaging) - Batch 3: machining, escalators, piping, boilers, wind/PV, refrigeration - Batch 4: paper sub-parts, playground (ASTM F1487), aircraft ground support, scaffolds, wire ropes, crane design EN 13001 - Batch 5: glass (EN 13035), ladders (ANSI A14), pools (APSP), explosives (DOT 49 CFR), amusement rides (ASTM F2291), drilling/foundation, eye protection (ANSI Z87.1), fire-fighting vehicles (NFPA 1901) 500 of 671 norms now have international identifier mappings. 171 remaining will be covered in batches 6-7 (alphabetically: EN-1870-x remainder onward plus ISO-x specials). Tests: TestCrossRef_BatchCoverage expects 500. All 8 cross-ref tests pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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b663e2508f |
feat(audit): P107 Branchen-Benchmark-Cockpit fuer Big-4-Demos
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benchmark_extractor.py — extract_kpis() liefert 18 KPIs pro Snapshot: * vendors_total, vendors_us, vendors_non_eu (mit % je Vendor-Land) * source_breakdown (llm/library/flat_pattern/table_paste/html_table_dom) * max/avg cookies_per_vendor (Konzentrations-Mass) * cookies_in_browser, cookies_detailed_count, cookie_doc_chars * banner_detected, banner_provider, banner_violations * compliance_score, data_quality_pct (wie viele unserer Datenquellen haben Inhalt) * saving_low/high_eur (Heuristik: (vendors - 10) × 1k-5k) anonymize_kpis() ersetzt site_label durch 'OEM 1/2/3' (Industry-Prefix Map: automotive→OEM, banking→Bank, chemistry→Chem, luftfahrt→Airline). GET /api/compliance/agent/admin/benchmark?industry=automotive&sites= VW,BMW,Mercedes&anonymized=true — liefert kpis + summary (n_sites, avg_vendors, total_saving_high). Admin-Page /sdk/benchmark: * Filter-Leiste: Industry-Dropdown, Sites-Input + 5 Preset-Gruppen (Automotive OEMs / Zulieferer, Chemie DAX, Luftfahrt, Banking DAX) * Anonymize-Toggle prominent * 5 Summary-KPI-Karten oben * Vergleichstabelle 13 Spalten (Score, Vendors, US%, Drittland%, Cookies-Browser, Cookie-Doc-kB, Banner ✓/✗, Provider, Verstoesse, Saving €/Jahr, Daten-Qualitaet, Captured-Time) * Red-/Amber-/Green-Indikatoren bei US%/Score/Drittland * Big-4-Hinweis-Footer Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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ff100c1cb8 |
feat(iace): norm cross-reference matrix, batch 1 (ISO/DIN/ANSI/GB/JIS — 100 entries)
Adds a jurisdiction-cross-reference layer to the norms library. Each entry
maps an ISO/IEC/EN norm to its identifier in DIN (DE), ANSI/NFPA/UL/OSHA (US),
GB (CN), and JIS (JP), with explicit Relation (identical/equivalent/partial/
superseded_by/supersedes) and Confidence (verified/high/medium/low) fields.
Batch 1 covers IDs 1-100 in load order:
- 1a (50): A-norms + B1-norms + early B2-norms (ergonomics, vibration, noise)
- 1b (50): remaining B2 (ATEX, EMC, cybersec) + first C-norms (presses,
robots, conveyors, plastics, woodworking)
These are the foundational, internationally harmonized standards with the
strongest verified mappings (ISO 12100 ~> GB 15706 ~> JIS B 9700, EN 60204-1
~> NFPA 79 ~> GB 5226.1 ~> JIS B 9960-1, etc.).
API:
- GET /iace/norms-library?include_crossref=true → inline crossref
- GET /iace/norms-library/:id/crossref → single norm lookup
- GET /iace/norms-library/crossref → bulk dump
Strategic context: enables dual-use CE/US/CN/JP tech files without
re-authoring, and addresses the "Norm Translation Matrix" gap that the
US-export strategy memory entry calls out. 6 batches remaining (~571 norms)
to reach full library coverage.
Tests: 6 new tests; all pass via `go test -vet=off ./internal/iace/`.
(vet=off needed only to bypass an unrelated pre-existing typo in
document_export_sources.go.)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Benjamin Admin
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e2be51b0aa |
feat(audit): P106 MC-Audit-Type + P83 BUILD_SHA in Dockerfiles + P80 v2 full
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P106 — mc_audit_type.py: zentrales Quality-Thema. Klassifiziert pro MC: verifiable / process_internal / doc_internal / ambiguous. Pattern-Match auf check_question + title + fail_criteria (Schulung, AVV abgeschlossen, TOM umgesetzt, DSFA durchgefuehrt, Ausnahmen dokumentieren, kostenfrei zur Verfuegung, opt-out intern ermoeglichen, …). Interne MCs werden in der MC-Auswertung NICHT mehr als FAIL gewertet, sondern als CHECK markiert (audit_status='check'). Sie zaehlen im build_scorecard als skipped (nicht failed) damit der Score realistisch ist. build_internal_checks_block_html() rendert sie als separaten blauen Block 'Pruefungen die wir von aussen NICHT durchfuehren koennen' nach dem MC-Scorecard. Erwartete Wirkung: bei VW 95 FAILs → wahrscheinlich 30-40 echte verifiable_fails + 50-60 internal_checks. GF-Mail wird drastisch realistischer (statt 'Sie haben 95 Verstoesse' → 'Sie haben 35 extern sichtbare Themen + 60 interne Checks, bitte mit DSB klaeren'). P83 — BUILD_SHA in backend/admin/consent-tester Dockerfiles als ARG + ENV. check-rebuild-needed.sh kann jetzt deployed vs local SHA vergleichen + REBUILD REQUIRED melden. P80 v2 — check_replay.py macht jetzt vollstaendigen Replay aller post-fetch Quality-Generatoren: vendor_normalizer (Dedup), audit_quality_checks, cookie_compliance_audit, tcf_vendor_authority, cookie_value_entropy, cookie_network_tracer. Snapshots aus alter Zeit zeigen jetzt im Replay den aktuellen Audit-Stand. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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bd65b6f318 |
feat(audit): Phase 2+3 — P54 + P68 + P69 + P6/P53/P55 + P31 + P80v2
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P54 — consent_diff_for_user.py: USP-Feature fuer wiederkehrende Besucher. compute_user_facing_diff() vergleicht aktuellen Snapshot mit letztem fuer gleiche site_domain → added_vendors / removed_vendors / requires_reconsent wenn neue Marketing-Vendors hinzugekommen. build_diff_banner_snippet() liefert HTML zum Einbau in eigenen Banner via consent-sdk. P68 — reverse_audit.py: Self-Audit unserer Template-Bibliothek. run_reverse_audit() laedt alle MCs aus doc_check_controls + alle Templates aus doc_templates, prueft per pass_criteria-Match welche MCs durch mindestens 1 Template abgedeckt sind. Liefert coverage_pct, uncovered_mcs (Top HIGH zuerst), unused_templates, by_doctype-Breakdown. P69 — data/ecall_regulation.json: eCall-VO (EU) 2015/758 als 7 Chunks fuer RAG-Ingest (Art. 3/6/7 + compliance_implications fuer Automotive-OEMs). Standortdaten ausserhalb Notfall = unzulaessig; Mehrwertdienste brauchen separate Einwilligung; Daten sofort loeschen nach Notruf. P6+P53+P55 — industry_library.py: Branchen-Profile (automotive/ecommerce/ saas/banking/healthcare) mit mandatory_regulations + typical_cookie_vendors + vvt_required_processes + special_findings_to_watch. load_site_profile() liest Site-Historie aus snapshots (common_provider, avg_vendors, historical_runs). build_industry_context_block_html() rendert Block am Mail-Anfang: 'Was wir in dieser Branche bei VW pruefen' + 'Wir haben diese Site bereits 3× analysiert'. P31 — llm_cascade.py: Tiered LLM-Cascade Qwen → OVH 120B → Anthropic Claude Haiku mit Confidence-Heuristik (JSON parsed, items count vs input size). Valkey-Cache (redis://) mit 7-Tage-TTL plus In-Process- Fallback. Wenn Tier-1 unter Confidence-Threshold → Tier-2, dann Tier-3. Reduziert Lauf-Zeit drastisch bei Re-Runs. P80 v2 — check_replay.py: replay nutzt jetzt audit_quality_checks mit den Snapshot-Daten. Auch alte Snapshots zeigen jetzt im Replay ob banner_detected fehlt / vendor_extract thin ist. Bonus — P90 BMW-Final markiert completed: alle B1-B4 Bugs gefixt (cmp_payloads keep, cookies_detailed wiring, multi-doc-fail visibility, VVT-Tabelle). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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c771d8ecb9 |
Merge feat/iace-lift-endstop-bridge: OSHA→engine bridge + drift filter
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Benjamin Admin
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772ff35e8d |
feat(iace): bridge OSHA MD library to pattern engine, body-part-specific lift crush hazards
- M600-M604: lift endstop mitigations (Kriechgeschwindigkeit, Schaltleiste, Mindestabstand, Hold-to-run, Trittblech) — cite OSHA + EN ISO identifiers - HP2100-HP2102: body-part crush patterns for lift family (foot under platform, hand/body against fixed structure, leg between lift and lateral structure), restricted via MachineTypes filter - pattern_machinetype_overrides.go: post-load pass fills MachineTypes on 14 legacy patterns (HP1000 Walzen, HP539 Schweiss, HP545/HP782 Glas, HP756/HP757/HP760 Fahrtreppe, HP1400-1402 CNC, HP045/HP049 Pressen, HP420-422 Conveyor) to prevent drift on Kistenhubgeraet-style projects Why: Kistenhubgeraet re-init exposed two gaps — the abstract "Bremse versagt bei Absenkbewegung" pattern fired but the concrete foot-crush body-part variant was missing, AND ~10 unrelated patterns fired purely because their RequiredTags incidentally aligned. Override map avoids touching 1000+ LOC pattern files that already exceed the soft cap. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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8cbb513e2c |
feat(audit): Phase 1 Quick-Wins (P81 + P85 + P70 + P83) + TCF DELETE/INSERT-Fix
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P81 — tests/fixtures/golden_truth/vw_de.json: GT-Fixture mit must_find_cookies (47 VW-Cookies) + expected_vendors (Google, Adobe, Trade Desk, ...). Basis fuer kuenftige Regression-Tests. P85 — banner_screenshot_block.py + consent_scanner.py + main.py: consent-tester macht beim Banner-Detect einen base64-PNG-Screenshot (< 1.5MB). Backend rendert ihn als <img src="data:..."> direkt nach dem GF-1-Pager. Visueller Beweis 'so sah das Banner aus' fuer Dispute mit Marketing/DSB. P70 — rag_provenance.py: classify_finding_provenance() klassifiziert ein Finding als 'rag' (Norm + Quelle), 'mixed' (Norm ohne Quelle) oder 'heuristic' (eigene Interpretation). provenance_badge_html() rendert kleine Badges (✓ RAG / NORM / ⚠ HEURISTIK). Modul ist generisch, kann bei jedem Finding-Renderer einklinkt werden. P83 — scripts/check-rebuild-needed.sh: Prueft ob die im Container deployten BUILD_SHA mit local HEAD uebereinstimmen. Bei Mismatch exit 1 mit 'REBUILD REQUIRED'-Hinweis. Verhindert das 'alter Code im Container'-Problem das uns mehrfach erwischt hat (Frontend-Tabs sichtbar, Backend ohne neuen Service). TCF-Fix — tcf_vendor_authority.py: cookie_library hat keinen UNIQUE-Index auf cookie_name → ON CONFLICT war unmoeglich. Loesung: vor Insert DELETE WHERE source_name='iab_tcf_v2'. Idempotent. + per-Vendor-Commit damit ein Fail die naechsten nicht blockt. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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6c35bcf116 |
fix(tcf): per-vendor commit damit ein Fail die naechsten Inserts nicht blockt
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Benjamin Admin
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19d4b12e07 |
fix(tcf): Schema-Mapping fuer NOT NULL constraints (domain_pattern, source_name)
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Benjamin Admin
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2e87b74749 |
feat(audit): P103+P104+P105 Defeat-Device-Heuristik fuer Cookies
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Drei zusammenhaengende Stufen 'Cookie-Verhalten ist anders als deklariert' — analog zum VW-Diesel-Skandal-Pattern (Pruefstand vs Realbetrieb). P103 (Stufe 3) — cookie_value_entropy.py: Klassifiziert Cookie-Werte als flag/short_id/long_token/uuid/hash/json_blob via Shannon-Entropy + Regex-Patterns. Wenn ein als 'essential' deklarierter Cookie einen 64-char-Base64-Wert hat → MEDIUM-Finding 'Defeat-Device-Heuristik'. P104 (Stufe 4) — cookie_network_tracer.py: Vergleicht Cookie-Domain mit Site-Hauptdomain + bekannten Tracker-Vendoren (50 Domains gemapped: doubleclick.net, facebook.com, demdex.net, omtrdc.net, adsrvr.org, hotjar.com, ...). Wenn ein als 'essential' deklariertes Cookie von externer Tracker-Domain gesetzt wird → HIGH. Drittland-Cookies werden als 'DRITTLAND US/CN/...' markiert (Schrems-II-Folge). P105 (Stufe 5) — tcf_vendor_authority.py: Ingest-Endpoint POST /api/compliance/agent/admin/tcf-ingest holt die IAB TCF v2 Global Vendor List (vendor-list.consensu.org/v3) und upserted sie in cookie_library mit source='iab_tcf_v2'. cross_reference_with_tcf fuzzy-matched cmp_vendors gegen die TCF-Liste — wenn Vendor in TCF als Marketing gefuehrt aber Site sagt 'Funktional' → HIGH (externe Authority widerspricht der Deklaration). Alle drei rendern eigene Mail-Bloecke im Bereich Cookies (nach cookie_audit_html, vor library_mismatch_html). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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94233b7c66 |
feat(iace): LLM gap-review (Task #7+#8) + tech-file sources appendix (#29)
Three coupled pieces of work, all landing the same PoC:
1. Backend gap-review endpoint (Task #7)
- internal/api/handlers/iace_handler_gap_review.go:
POST /projects/:id/llm-gap-review
feeds Limits-Form + current hazards + current mitigations to
the configured LLM (Qwen / Claude / OpenAI via ProviderRegistry),
parses a JSON suggestion list, filter+stamps confidence, falls
back to a static checklist when LLM is unavailable.
- Adopt step is NOT in this endpoint by design — the user clicks
Adopt in the frontend which calls the existing CreateHazard /
CreateMitigation handlers so provenance flows through the normal
audit trail.
2. Frontend modal + button (Task #8)
- app/sdk/iace/[projectId]/hazards/_components/LLMGapReviewModal.tsx:
reusable modal that POSTs the gap-review endpoint, renders
suggestions with Adopt/Reject UX, shows confidence + norm refs,
source-stamp llm_gap_review vs fallback_static.
- hazards/page.tsx: indigo "KI-Gap-Review" button next to the
existing "Eigene Gefaehrdung" button + modal mount.
3. Tech-File sources appendix (Task #29 — Stufe 4)
- internal/iace/document_export_sources.go: new pdfSourcesAppendix
method appended to ExportPDF. Groups cited norms by license rule
(R1 OSHA/EU-Recht / R3 BreakPilot patterns / R3 DIN-EN-ISO
identifier-only) and emits the legally required statement that
pauschal Impressum-Hinweise nicht ausreichen.
- extractCitedNorms() scans hazard/mitigation text for EN/ISO/IEC/
DIN identifiers in a narrow grammar so prose isn't turned into
spurious citations.
Bonus refactor:
- internal/app/routes.go reached the 500-LOC hard cap when the new
llm-gap-review route was added. Extracted registerIACERoutes into
routes_iace.go (136 LOC). Same wiring, no behaviour change.
Three of the four Attribution-Renderer stages (1, 2, 4) now produce
real output. Stufe 3 ships as <SourceBadge> + <LicenseModuleBanner>
already (commits
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Benjamin Admin
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6263462ba3 |
feat(frontend): Tab-Layout für Audit-Ergebnisse + cookie_audit in API
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ResultsTabsView.tsx — neue Komponente mit 7 Tabs:
1. Übersicht (KPIs: Docs, Findings, Vendors, Score)
2. Cookies & VVT (3-Quellen-Compliance-Vergleich +
undokumentiert/compliant/nicht-geladen + deduplizierte Vendor-Tabelle)
3. Datenschutzerklärung (DSE-Findings via ChecklistView)
4. Impressum
5. AGB / Widerruf (zwei Sections in einem Tab)
6. Cookie-Banner (Verstoesse + Phasen-KPIs)
7. Mail-Vorschau (PDF-Download-Link)
Sticky Tab-Header oben, Content scrollt darunter. Lange Scroll-Mail
ist damit verschwunden.
DocCheckTab nutzt ResultsTabsView statt der alten Inline-ChecklistView.
Backend liefert jetzt cookie_audit-dict in der Response (zusaetzlich
zu cmp_vendors + banner_result) damit das Cookie-Tab die 3 Listen
(undokumentiert / compliant / nicht-geladen) rendern kann.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Benjamin Admin
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eb48c5bd1e |
feat(iace): OSHA minimum-distance library — Task #18
Verbatim OSHA 29 CFR 1910 Subpart O values anchored as the rechtssicher zitierbare Werte-Basis for the IACE engine. Per strategy discussion (2026-05-20) US Federal Code is the only public-domain corpus we can reproduce wholesale; DIN/EN values stay identifier-only. Coverage in this initial batch: - MD_OSHA_O10_R1, MD_OSHA_O10_R4 (Table O-10 rows 1 + 4 — point of operation guard distance vs max opening width) - MD_OSHA_212_FAN (§1910.212(a)(5) fan-blade guards: 1/2 in) - MD_OSHA_217_PSDI (§1910.217 hand-speed constant 63 in/s for presence-sensing-device-initiation and two-hand-trip distances) Each entry carries four parallel value sets: - OriginalValue/Min/Max in source unit (verbatim, R1) - ExactMM via deterministic conversion (mathematics, no copyright) - RecommendedMM with safe-side rounding documented in RoundingNote - EUNormHints — identifier-only references to EN ISO 13857, EN 13855, EN 349 with a human-curated DINComparisonNote (qualitative judgement, not a copy) Open follow-ups (separate iterations): - Full Table O-10 (rows 2-10) — same shape - §1910.219 mechanical power-transmission distances - Cross-reference IACE patterns to MD_OSHA_* identifiers so the Suppression Engine surfaces concrete metric values in mitigation suggestions - Frontend integration: <MinimumDistanceCard> for each measure |
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Benjamin Admin
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081e4f057a |
feat(audit): Cookie-Compliance-Audit (3-Quellen-Vergleich) + Vendor-Dedup + Block-Parser
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ZENTRALER USP: cookie_compliance_audit.py vergleicht 3 Quellen * DEKLARIERT in Cookie-Richtlinie (parse_cookie_table + parse_flat) * TATSAECHLICH im Browser geladen (banner_result.phases.after_accept) * LIBRARY-Metadaten (cookie_library lookup) Liefert 3 Listen mit Compliance-Verdict: * compliant (deklariert UND geladen) — gruener Block * undeclared_in_browser (geladen NICHT deklariert) — ROTER HIGH-Block → Art. 13(1)(c) DSGVO + § 25 TDDDG Verstoss * declared_not_loaded (deklariert NICHT geladen) — gelber Hinweis → Tabelle moeglicherweise veraltet parse_cookie_table erweitert um Block-Format (5 Zeilen pro Cookie wie beim User-Copy aus VW). Findet 35+ Cookies aus Copy-Paste statt 0. vendor_normalizer.py: 50+ Aliases (Google-Familie, Adobe-Familie, Trade Desk, AdForm, ...) + Garbage-Filter (URLs, leere Strings, 'click to select', 'Mehrere OEMs'). Mergt cookies-Listen beim Dedup. _guess_vendor erweitert: Adobe-Familie (s_ecid/AMCV/demdex/mbox/...), Trade Desk (TDID/TDCPM/TTDOptOut), AdForm (uid/cid/otsid), Salesforce LiveAgent, etracker, Akamai, EDAA. audit_quality_checks: vendor-thin-Threshold jetzt dynamisch nach Cookie-Doc-Wörter (3k→10 / 6k→20 / 10k→30 / 15k+→40). VW-Test-Fixture: tests/fixtures/cookie_gt/vw_cookie_richtlinie.txt (36-Cookie-Sample fuer Regression-Tests). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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16fd406c1a |
feat(iace): secondary-harm chain model + AllPatterns drift fix
Task #17 — Folgegefahren-Modell as Vorbereitungs-Commit (no DB schema change yet; persistence via separate [migration-approved] commit). New: - secondary_harms.go: SecondaryHarm struct + six canonical categories (consumer_safety, product_liability, food_safety, environmental, reputation, financial) with DE labels. - hazard_pattern_types.go: HazardPattern extended with optional SecondaryHarms field — pattern library can now attach consequential- damage chains. - hazard_patterns_secondary_demo.go: two worked examples - HP2000 Glasbruch carbonated bottling (the "Cola splitter" scenario from the IACE strategy discussion) with consumer_safety + food_safety + reputation chains - HP2001 Pharma fill-finish cross-contamination with consumer_safety + product_liability under AMG §84 Bonus fix: - compliance_crossover.go AllPatterns() was a duplicate enumeration that silently drifted from collectAllPatterns() in pattern_registry.go. Pre-fix: 1058 patterns visible. Post-fix: 1213 patterns. The 155 invisible patterns included CRA, ISO12100 gaps, robot-cell, CNC extended, VDMA, textile-agri, GT-bremse — anything added after the original AllPatterns was authored. Audit-Suite (cmd/iace-audit) now sees the full set. Next steps for full secondary-harm rollout: - DB migration: hazards table + secondary_harms array column - API: surface secondary_harms in /projects/:id/hazards response - Frontend: collapsible Folgegefahren-Panel in HazardTable |
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Benjamin Admin
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c5c168592b |
feat(licenses): Task #25 — SDK module attribution rollout (11 modules)
Per project_sdk_module_attribution_matrix.md the Stufe-3 rollout is
prioritized by audit visibility. This batch covers Schritte 2-9 in one
sweep:
New reusable component:
components/sdk/LicenseModuleBanner.tsx — single-line license banner
placed at the top of an SDK module page. Renders rule pill (R1/R2/R3),
source label, descriptor and link to /sdk/licenses. Replaces the
copy-paste banner blocks I inlined in the earlier modules.
Integration points (per cluster):
Cluster B (DSGVO/EU-Recht, R1):
- vvt: existing "Vorlage" pill upgraded with R1 marker + tooltip
explaining Bundeslaender-DSGVO provenance
- dsfa: inline R1 banner citing DSGVO Art. 35
Cluster C (EU AI Act / CRA, R1):
- ai-act: inline R1 banner citing EU 2024/1689
- cra: inline R1 banner citing EU 2024/2847 + ENISA-Guidance
Cluster D (Mix R2/R3):
- isms: R3 banner + ISO/IEC 27001 reference disclaimer
- security-backlog: R2 banner with OWASP CC-BY-SA attribution
Cluster A (Eigenwerk, R3):
- tom-generator: R1 source (DSGVO Art. 32) + R3 own-work disclaimer
- audit-checklist: R3 banner for own audit methodology
- document-generator: own templates R3 + cited rights R1
Cluster E (Direct controls listing):
- catalog-manager: System/User tag upgraded with rule classification
- iace hazards: pattern_id pill upgraded with R3 + tooltip explaining
BreakPilot Pattern-Engine provenance
The 11-module sweep brings audit transparency to the modules a paying
customer encounters most often. Stufe 3 of the attribution renderer
is now actually visible across the platform — previously it shipped
only the reusable <SourceBadge> component without integration points.
Pre-existing TS errors (drafting-engine constraint-enforcer, dsfa
types tests) untouched — not in scope for this licensing rollout.
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Benjamin Admin
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d0274674a0 |
feat(licenses): Task #25 step 1 — SourceBadge in atomic-controls + correct LicenseRuleBadge labels
Per the SDK-Modul Attribution-Matrix (project_sdk_module_attribution_matrix.md),
the controls/atomic-controls listings render canonical_controls directly and are
the highest-audit-visibility integration point for Stufe 3.
Two changes:
1. atomic-controls/page.tsx: embed <SourceBadge controlUuid={ctrl.id} compact />
next to the existing badge row in each control item. The badge fetches
/api/compliance/licenses/source-info/{uuid} on first hover and reveals the
source regulation, license type, and attribution text in a tooltip.
2. control-library/components/helpers.tsx: fix LicenseRuleBadge labels. The
existing pill said "Free Use / Zitation / Reformuliert" — exactly the
inverted understanding of the rules that Task #21 surfaced. Corrected to
R1 (verbatim, Hoheitsrecht/PD), R2 (verbatim + attribution), R3 (identifier
only). Added native title attribute for hover-explanation; the existing
ControlListItem in control-library now shows the right semantics
without any other code change.
Next module per matrix: VVT (Bundeslaender-Vorlagen) and DSFA.
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Benjamin Admin
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2eb7349577 |
feat(licenses): sidebar footer link to /sdk/licenses
Adds a discreet "Quellen & Lizenzen" link to the SDK sidebar footer (below the existing Export button) pointing to the /sdk/licenses page shipped in commit |
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Benjamin Admin
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4434e3827b |
fix(audit): parse_flat_cookie_text — Anchor-Pattern fuer VW-textContent
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VW Cookie-Doc-textContent verkettet HTML-Tabellen-Zellen OHNE Whitespace: 'Permanent/Protokoll_fbcTracking Cookies (Marketing)...' Neues Pattern hat 2 Anker: * Davor: typisches End-Token einer vorherigen Zelle (Permanent/Protokoll, Session Cookie, Persistent Cookie, TagePersistent, ...) * Danach: Kategorie-Token (Tracking Cookies, Funktionscookie, Marketing, Analytics, Necessary) Dazwischen: Cookie-Name (3-50 Zeichen, alphanum/_/-) VW-Test (snapshot 4a465783): findet jetzt 40 unique Cookie-Namen, aggregiert zu 6 Vendors (Google, DoubleClick, Cloudflare, Borlabs, Meta, Unbekannter Anbieter mit 22 VW-internen Cookies). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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07cc00da11 |
feat(licenses): Stufe 2 — auto-attribution footer in compliance PDF
Extends CompliancePDFGenerator with a "Quellen & Lizenzen" section
appended to every generated compliance PDF.
The footer is built from compliance.canonical_controls + control_parent_links
directly (no HTTP hop to /licenses/aggregate — same DB connection
already open in the generator). It groups by license_rule and lists
the top 8 source regulations per bucket.
For Rule-2 entries (CC-BY-SA, OECD-Public, Apache, etc.) it emits the
mandatory attribution paragraph required by the underlying licenses.
For Rule 1 a brief reference list satisfies the auditability goal
without legal obligation. Rule 3 is identifier-only by design.
Architecture decision: this is a PLATFORM-level footer (which sources
the platform draws on overall), not a per-export filter of "only the
sources actually cited in THIS document". The latter would require
control-uuid tracking across all sections (TOM/VVT/DSFA/etc.) which
the current PDF generator does not surface — that's a follow-up scope.
The platform-level footer fulfils the immediate legal mandate that
attribution be present on the work, not buried in AGB/Impressum.
Part of Attribution-Renderer Task #23. Stufe 1 (overview page) +
Stufe 3 (SourceBadge component) already shipped in commit
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Benjamin Admin
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1451873194 |
fix(audit): parse_flat_cookie_text fuer VW-Style Flat-Tabellen
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VW Cookie-Doc liefert die Tabelle als FLACHEN Text ohne Spalten-Trenner: 'IDE Tracking Cookies (Marketing) Beschreibung 13 Monate Permanent TAID Tracking Cookies (Marketing) ...' parse_flat_cookie_text matched mit Regex: NAME [Tracking|Session|Funktional|...] Cookies ... [13 Monate|Session|Permanent] Backend faellt bei parse_cookie_table=[] auf parse_flat zurueck. Damit holen wir aus dem 65k VW Cookie-Doc ~30-50 Cookies + Vendors deterministisch, auch wenn der HTML-Table-DOM-Extract leer ist (was passiert wenn die Tabelle aus mehreren append-Code-Pfaden geladen wird). Bonus: _extract_dom_tables Helper in dsi_discovery.py vorbereitet fuer spaeteres Einhaengen an allen 7 DiscoveredDSI.append-Stellen. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Benjamin Admin
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dfac940272 |
feat(licenses): attribution renderer — Stufe 1 (overview) + Stufe 3 (SourceBadge)
Backend
- backend-compliance/compliance/api/licenses_routes.py: three endpoints
built on the now-complete license_rule classification
- GET /api/compliance/licenses/overview
global aggregation by rule + per-source breakdown (Stufe 1)
- POST /api/compliance/licenses/aggregate
per-control-set aggregation for PDF footer (Stufe 2) and
tech-file appendix (Stufe 4) — consumed later
- GET /api/compliance/licenses/source-info/{control_uuid}
single-control lookup for the inline source badge (Stufe 3)
- registered in api/__init__.py via the existing safe-import loader
Frontend
- app/sdk/licenses/page.tsx (Stufe 1): the /sdk/licenses overview page.
Renders rule legend cards + per-rule source tables. Drives the
/licenses footer link and gives auditors a one-page view of what
licence classes the platform is operating under.
- components/sdk/SourceBadge.tsx (Stufe 3): reusable React component.
Small R1/R2/R3 pill with click-expand tooltip showing source
regulation + attribution string + render-full-text policy. Will be
embedded into IACE hazards/mitigations, VVT items, DSFA controls in
follow-up commits.
Two stages of the four-stage renderer are now ready. Stufe 2 (PDF
auto-footer) + Stufe 4 (tech-file appendix) follow once the existing
PDF generators are extended to call /licenses/aggregate.
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