feat: website scanner with SOLL/IST service comparison + corrections
- website_scanner.py: multi-page crawl, 20+ service patterns (tracking, CDN, chatbots, payment, fonts, captcha, video), AI text detection - dse_service_extractor.py: LLM extracts services from privacy policy text - agent_scan_routes.py: POST /agent/scan — combines scan + DSE comparison, generates findings (undocumented, outdated, third-country transfer), auto-corrections via Qwen in pre-launch mode Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
302
backend-compliance/compliance/api/agent_scan_routes.py
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302
backend-compliance/compliance/api/agent_scan_routes.py
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"""
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Agent Website Scan Routes — deep scan endpoint that performs multi-page
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website analysis with SOLL/IST service comparison.
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POST /api/compliance/agent/scan
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"""
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import logging
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import os
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from datetime import datetime, timezone
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import httpx
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from fastapi import APIRouter
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from pydantic import BaseModel
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from compliance.services.website_scanner import scan_website, DetectedService
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from compliance.services.dse_service_extractor import extract_dse_services, compare_services
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from compliance.services.smtp_sender import send_email
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/compliance/agent", tags=["agent"])
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SDK_URL = os.environ.get("AI_SDK_URL", "http://bp-compliance-ai-sdk:8090")
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TENANT_ID = "9282a473-5c95-4b3a-bf78-0ecc0ec71d3e"
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USER_ID = "00000000-0000-0000-0000-000000000001"
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SDK_HEADERS = {
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"Content-Type": "application/json",
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"X-Tenant-ID": TENANT_ID,
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"X-User-ID": USER_ID,
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}
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class ScanRequest(BaseModel):
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url: str
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mode: str = "post_launch"
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recipient: str = "dsb@breakpilot.local"
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class ServiceInfo(BaseModel):
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name: str
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category: str
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provider: str
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country: str
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eu_adequate: bool
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requires_consent: bool
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legal_ref: str
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in_dse: bool
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status: str # "ok", "undocumented", "outdated"
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class ScanFinding(BaseModel):
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code: str
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severity: str
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text: str
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correction: str = ""
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class ScanResponse(BaseModel):
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url: str
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pages_scanned: int
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services: list[ServiceInfo]
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findings: list[ScanFinding]
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ai_detected: bool
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chatbot_detected: bool
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chatbot_provider: str
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missing_pages: dict
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summary: str
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email_status: str
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scanned_at: str
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@router.post("/scan", response_model=ScanResponse)
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async def scan_website_endpoint(req: ScanRequest):
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"""Deep website scan: multi-page crawl + SOLL/IST service comparison."""
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is_live = req.mode == "post_launch"
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# Step 1: Scan website (5-10 pages)
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scan = await scan_website(req.url)
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logger.info("Scanned %d pages, found %d services", len(scan.pages_scanned), len(scan.detected_services))
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# Step 2: Fetch privacy policy text for SOLL extraction
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dse_text = await _fetch_dse_text(req.url, scan.pages_scanned)
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# Step 3: Extract services mentioned in DSE via LLM
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dse_services = await extract_dse_services(dse_text) if dse_text else []
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logger.info("DSE mentions %d services", len(dse_services))
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# Step 4: SOLL/IST comparison
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detected_dicts = [_service_to_dict(s) for s in scan.detected_services]
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comparison = compare_services(detected_dicts, dse_services)
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# Step 5: Generate findings
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services_info, findings = _build_findings(comparison, scan, is_live)
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# Step 6: Generate corrections for pre-launch mode
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if not is_live and findings:
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await _add_corrections(findings, dse_text)
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# Step 7: Build summary
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summary = _build_scan_summary(req.url, scan, comparison, findings, is_live)
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# Step 8: Send notification
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mode_label = "INTERNE PRUEFUNG" if not is_live else "LIVE-WEBSITE"
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email_result = send_email(
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recipient=req.recipient,
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subject=f"[{mode_label}] Website-Scan: {req.url[:50]}",
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body_html=f"<pre>{summary}</pre>",
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)
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return ScanResponse(
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url=req.url,
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pages_scanned=len(scan.pages_scanned),
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services=services_info,
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findings=findings,
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ai_detected=len(scan.ai_mentions) > 0,
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chatbot_detected=scan.chatbot_detected,
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chatbot_provider=scan.chatbot_provider,
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missing_pages=scan.missing_pages,
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summary=summary,
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email_status=email_result.get("status", "failed"),
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scanned_at=datetime.now(timezone.utc).isoformat(),
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)
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async def _fetch_dse_text(url: str, scanned_pages: list[str]) -> str:
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"""Find and fetch the privacy policy page text."""
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import re
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# Find DSE URL from scanned pages
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dse_url = None
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for page in scanned_pages:
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if re.search(r"datenschutz|privacy|dsgvo", page, re.IGNORECASE):
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dse_url = page
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break
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if not dse_url:
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dse_url = url # Fallback to provided URL
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try:
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async with httpx.AsyncClient(timeout=15.0, follow_redirects=True) as client:
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resp = await client.get(dse_url, headers={"User-Agent": "BreakPilot-Compliance-Agent/1.0"})
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html = resp.text
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clean = re.sub(r"<(script|style)[^>]*>.*?</\1>", "", html, flags=re.DOTALL | re.IGNORECASE)
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clean = re.sub(r"<[^>]+>", " ", clean)
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clean = re.sub(r"\s+", " ", clean).strip()
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return clean[:4000]
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except Exception:
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return ""
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def _service_to_dict(svc: DetectedService) -> dict:
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return {
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"id": svc.id, "name": svc.name, "category": svc.category,
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"provider": svc.provider, "country": svc.country,
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"eu_adequate": svc.eu_adequate, "requires_consent": svc.requires_consent,
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"legal_ref": svc.legal_ref,
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}
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def _build_findings(
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comparison: dict, scan, is_live: bool,
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) -> tuple[list[ServiceInfo], list[ScanFinding]]:
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"""Build service info list and findings from comparison."""
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services = []
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findings = []
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# Undocumented services (on website, NOT in DSE)
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for svc in comparison["undocumented"]:
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services.append(ServiceInfo(
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name=svc["name"], category=svc.get("category", "other"),
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provider=svc.get("provider", ""), country=svc.get("country", ""),
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eu_adequate=svc.get("eu_adequate", False),
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requires_consent=svc.get("requires_consent", False),
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legal_ref=svc.get("legal_ref", ""), in_dse=False, status="undocumented",
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))
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severity = "HIGH" if is_live else "MEDIUM"
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findings.append(ScanFinding(
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code=f"DSE-MISSING-{svc['id'].upper()}",
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severity=severity,
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text=f"{svc['name']} ({svc.get('provider', '')}, {svc.get('country', '')}) "
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f"ist auf der Website eingebunden aber NICHT in der Datenschutzerklaerung "
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f"dokumentiert (Art. 13 DSGVO).",
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))
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# Documented services (OK)
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for item in comparison["documented"]:
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svc = item["detected"]
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services.append(ServiceInfo(
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name=svc["name"], category=svc.get("category", "other"),
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provider=svc.get("provider", ""), country=svc.get("country", ""),
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eu_adequate=svc.get("eu_adequate", False),
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requires_consent=svc.get("requires_consent", False),
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legal_ref=svc.get("legal_ref", ""), in_dse=True, status="ok",
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))
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# Check third-country transfer
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if not svc.get("eu_adequate", False):
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findings.append(ScanFinding(
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code=f"TRANSFER-{svc['id'].upper()}",
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severity="MEDIUM",
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text=f"{svc['name']} ({svc.get('country', '')}) — Drittlandtransfer. "
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f"Pruefen ob SCCs oder Angemessenheitsbeschluss dokumentiert sind.",
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))
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# Outdated services (in DSE, NOT on website)
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for svc in comparison["outdated"]:
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services.append(ServiceInfo(
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name=svc["name"], category="other",
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provider=svc.get("provider", ""), country=svc.get("country", ""),
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eu_adequate=True, requires_consent=False,
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legal_ref="", in_dse=True, status="outdated",
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))
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findings.append(ScanFinding(
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code=f"DSE-OUTDATED-{svc['name'].upper().replace(' ', '_')[:20]}",
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severity="LOW",
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text=f"{svc['name']} in Datenschutzerklaerung erwaehnt aber auf der Website "
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f"nicht mehr gefunden. Eintrag bei naechster Aktualisierung entfernen.",
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))
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# Missing pages (e.g., /impressum returns 404)
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for page_url, status_code in scan.missing_pages.items():
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if "impressum" in page_url.lower():
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findings.append(ScanFinding(
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code="MISSING-IMPRESSUM",
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severity="HIGH",
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text=f"Impressum-Seite gibt HTTP {status_code} zurueck (§5 TMG Verstoss).",
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))
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return services, findings
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async def _add_corrections(findings: list[ScanFinding], dse_text: str) -> None:
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"""Add correction suggestions for pre-launch mode via LLM."""
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for finding in findings:
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if finding.severity in ("HIGH", "MEDIUM") and "MISSING" in finding.code:
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service_name = finding.code.replace("DSE-MISSING-", "").replace("_", " ").title()
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try:
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async with httpx.AsyncClient(timeout=30.0) as client:
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resp = await client.post(f"{SDK_URL}/sdk/v1/llm/chat", headers=SDK_HEADERS, json={
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"messages": [
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{"role": "system", "content": (
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"/no_think\n"
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"Du bist Datenschutzexperte. Erstelle einen einbaufertigen "
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"Textbaustein fuer eine deutsche Datenschutzerklaerung fuer "
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f"den Dienst '{service_name}'. Enthalte: Ueberschrift, "
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"Anbietername, Zweck, Rechtsgrundlage nach DSGVO, "
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"Drittlandtransfer-Hinweis wenn noetig, "
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"Widerspruchsmoeglichkeit. Max 150 Woerter."
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)},
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{"role": "user", "content": f"Erstelle DSE-Textbaustein fuer: {service_name}"},
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],
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})
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data = resp.json()
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import re
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raw = (
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data.get("response", "")
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or (data.get("message", {}) or {}).get("content", "")
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or ""
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).strip()
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raw = re.sub(r"<think>.*?</think>", "", raw, flags=re.DOTALL).strip()
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if raw:
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finding.correction = raw
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except Exception as e:
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logger.warning("Correction generation failed for %s: %s", service_name, e)
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def _build_scan_summary(
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url: str, scan, comparison: dict, findings: list[ScanFinding], is_live: bool,
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) -> str:
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"""Build German scan summary."""
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mode = "PRUEFUNG LIVE-WEBSITE" if is_live else "INTERNE PRUEFUNG"
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n_undoc = len(comparison["undocumented"])
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n_ok = len(comparison["documented"])
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n_outdated = len(comparison["outdated"])
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n_findings = len(findings)
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high = sum(1 for f in findings if f.severity == "HIGH")
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parts = [
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f"{mode} — Website-Scan",
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f"URL: {url}",
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f"Seiten gescannt: {len(scan.pages_scanned)}",
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"",
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f"Dienstleister-Abgleich (DSE vs. Website):",
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f" Korrekt dokumentiert: {n_ok}",
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f" NICHT in DSE (Verstoss): {n_undoc}",
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f" Veraltet in DSE: {n_outdated}",
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"",
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f"Findings: {n_findings} ({high} mit hoher Prioritaet)",
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]
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if findings:
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parts.append("")
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for f in findings[:10]:
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marker = "!!" if f.severity == "HIGH" else "!" if f.severity == "MEDIUM" else "i"
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parts.append(f" [{marker}] {f.text}")
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if is_live and high > 0:
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parts.extend([
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"",
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"ACHTUNG: Verstoesse auf einer bereits veroeffentlichten Website. "
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"Sofortige Korrektur empfohlen.",
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])
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return "\n".join(parts)
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127
backend-compliance/compliance/services/dse_service_extractor.py
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127
backend-compliance/compliance/services/dse_service_extractor.py
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@@ -0,0 +1,127 @@
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"""
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DSE Service Extractor — extracts mentioned third-party services from
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a privacy policy text using LLM (Qwen) and compares against detected services.
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Produces SOLL/IST comparison: what's in the DSE vs. what's on the website.
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"""
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import logging
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import os
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import re
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import httpx
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logger = logging.getLogger(__name__)
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SDK_URL = os.environ.get("AI_SDK_URL", "http://bp-compliance-ai-sdk:8090")
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TENANT_ID = "9282a473-5c95-4b3a-bf78-0ecc0ec71d3e"
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USER_ID = "00000000-0000-0000-0000-000000000001"
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SDK_HEADERS = {
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"Content-Type": "application/json",
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"X-Tenant-ID": TENANT_ID,
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"X-User-ID": USER_ID,
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}
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async def extract_dse_services(dse_text: str) -> list[dict]:
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"""Extract mentioned services from privacy policy text via LLM."""
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prompt = (
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"/no_think\n"
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"Extrahiere aus dieser Datenschutzerklaerung ALLE erwaehnten Dienstleister, "
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"Tools und externen Dienste. Fuer jeden nenne:\n"
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"- name: Name des Dienstes (z.B. 'Google Analytics')\n"
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"- purpose: Zweck (z.B. 'Webanalyse')\n"
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"- country: Land/Sitz (z.B. 'USA')\n"
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"- legal_basis: Genannte Rechtsgrundlage (z.B. 'Einwilligung')\n\n"
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"Antworte als JSON-Array. Wenn keine Dienstleister erwaehnt werden, "
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"antworte mit [].\n"
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"Beispiel: [{\"name\": \"Google Analytics\", \"purpose\": \"Webanalyse\", "
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"\"country\": \"USA\", \"legal_basis\": \"Einwilligung\"}]"
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)
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try:
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async with httpx.AsyncClient(timeout=60.0) as client:
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resp = await client.post(f"{SDK_URL}/sdk/v1/llm/chat", headers=SDK_HEADERS, json={
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"messages": [
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{"role": "system", "content": prompt},
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{"role": "user", "content": dse_text[:3500]},
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],
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})
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data = resp.json()
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raw = (
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data.get("response", "")
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or (data.get("message", {}) or {}).get("content", "")
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or ""
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).strip()
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raw = re.sub(r"<think>.*?</think>", "", raw, flags=re.DOTALL).strip()
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# Extract JSON array from response
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match = re.search(r"\[.*\]", raw, re.DOTALL)
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if match:
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import json
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return json.loads(match.group())
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except Exception as e:
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logger.warning("DSE service extraction failed: %s", e)
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return []
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def compare_services(
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detected: list[dict], dse_services: list[dict],
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) -> dict:
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"""Compare detected website services against DSE-mentioned services.
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Returns dict with three categories:
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- undocumented: on website but NOT in DSE (Art. 13 violation)
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- outdated: in DSE but NOT on website (cleanup)
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- documented: on website AND in DSE (OK, check details)
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"""
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# Normalize names for matching
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def normalize(name: str) -> str:
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return re.sub(r"[^a-z0-9]", "", name.lower())
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detected_names = {normalize(d["name"]): d for d in detected}
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dse_names = {normalize(d["name"]): d for d in dse_services}
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undocumented = []
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documented = []
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outdated = []
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for key, svc in detected_names.items():
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# Skip CMP — consent managers don't need DSE mention
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if svc.get("category") == "other" and svc.get("id") == "cmp":
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continue
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matched = False
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for dse_key, dse_svc in dse_names.items():
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if key == dse_key or _fuzzy_match(svc["name"], dse_svc["name"]):
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documented.append({"detected": svc, "dse": dse_svc, "status": "ok"})
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matched = True
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break
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if not matched:
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undocumented.append(svc)
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for key, dse_svc in dse_names.items():
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matched = False
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for det_key in detected_names:
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if key == det_key or _fuzzy_match(dse_svc["name"], detected_names[det_key]["name"]):
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matched = True
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break
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if not matched:
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outdated.append(dse_svc)
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return {
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"undocumented": undocumented,
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"documented": documented,
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"outdated": outdated,
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}
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def _fuzzy_match(a: str, b: str) -> bool:
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"""Simple fuzzy matching — checks if one name contains the core of the other."""
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a_lower = a.lower()
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b_lower = b.lower()
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# Direct substring
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if a_lower in b_lower or b_lower in a_lower:
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return True
|
||||
# Core word match (e.g., "Google" in "Google Analytics" and "Google Ireland")
|
||||
a_words = set(re.findall(r"\w{4,}", a_lower))
|
||||
b_words = set(re.findall(r"\w{4,}", b_lower))
|
||||
return bool(a_words & b_words)
|
||||
248
backend-compliance/compliance/services/website_scanner.py
Normal file
248
backend-compliance/compliance/services/website_scanner.py
Normal file
@@ -0,0 +1,248 @@
|
||||
"""
|
||||
Website Scanner — scans multiple pages of a website for third-party services,
|
||||
chatbots, tracking, AI indicators, and compares against privacy policy.
|
||||
|
||||
Used by the Compliance Agent for SOLL/IST analysis.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import re
|
||||
from dataclasses import dataclass, field
|
||||
from urllib.parse import urljoin, urlparse
|
||||
|
||||
import httpx
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
USER_AGENT = "BreakPilot-Compliance-Agent/1.0"
|
||||
|
||||
|
||||
@dataclass
|
||||
class DetectedService:
|
||||
id: str
|
||||
name: str
|
||||
category: str # "tracking", "chatbot", "cdn", "payment", "marketing", "other"
|
||||
provider: str
|
||||
country: str
|
||||
eu_adequate: bool
|
||||
requires_consent: bool
|
||||
legal_ref: str
|
||||
found_on: str = "" # URL where detected
|
||||
|
||||
|
||||
@dataclass
|
||||
class ScanResult:
|
||||
pages_scanned: list[str] = field(default_factory=list)
|
||||
detected_services: list[DetectedService] = field(default_factory=list)
|
||||
ai_mentions: list[str] = field(default_factory=list)
|
||||
chatbot_detected: bool = False
|
||||
chatbot_provider: str = ""
|
||||
missing_pages: dict = field(default_factory=dict) # url -> status_code
|
||||
|
||||
|
||||
# ── Service Registry ──────────────────────────────────────────────────────────
|
||||
# Each entry: regex pattern -> service metadata
|
||||
SERVICE_REGISTRY: dict[str, dict] = {
|
||||
# --- Tracking & Analytics ---
|
||||
r"google.?analytics|gtag\(|UA-\d+|G-\w{5,}": {
|
||||
"id": "google_analytics", "name": "Google Analytics", "category": "tracking",
|
||||
"provider": "Google LLC", "country": "US", "eu_adequate": False,
|
||||
"requires_consent": True, "legal_ref": "Art. 44-49 DSGVO, §25 TDDDG",
|
||||
},
|
||||
r"googletagmanager|gtm\.js": {
|
||||
"id": "google_tag_manager", "name": "Google Tag Manager", "category": "tracking",
|
||||
"provider": "Google LLC", "country": "US", "eu_adequate": False,
|
||||
"requires_consent": True, "legal_ref": "Art. 44-49 DSGVO",
|
||||
},
|
||||
r"facebook\.net/.*fbevents|fbq\(": {
|
||||
"id": "facebook_pixel", "name": "Meta/Facebook Pixel", "category": "marketing",
|
||||
"provider": "Meta Platforms", "country": "US", "eu_adequate": False,
|
||||
"requires_consent": True, "legal_ref": "Art. 44-49 DSGVO, §25 TDDDG",
|
||||
},
|
||||
r"hotjar\.com|_hjSettings": {
|
||||
"id": "hotjar", "name": "Hotjar", "category": "tracking",
|
||||
"provider": "Hotjar Ltd", "country": "MT", "eu_adequate": True,
|
||||
"requires_consent": True, "legal_ref": "§25 TDDDG (Session Recording)",
|
||||
},
|
||||
r"clarity\.ms": {
|
||||
"id": "ms_clarity", "name": "Microsoft Clarity", "category": "tracking",
|
||||
"provider": "Microsoft", "country": "US", "eu_adequate": False,
|
||||
"requires_consent": True, "legal_ref": "§25 TDDDG (Session Replay), Art. 44 DSGVO",
|
||||
},
|
||||
r"matomo|piwik": {
|
||||
"id": "matomo", "name": "Matomo", "category": "tracking",
|
||||
"provider": "InnoCraft/Self-hosted", "country": "EU/Self", "eu_adequate": True,
|
||||
"requires_consent": False, "legal_ref": "Cookieless moeglich, §25 TDDDG",
|
||||
},
|
||||
r"plausible\.io": {
|
||||
"id": "plausible", "name": "Plausible Analytics", "category": "tracking",
|
||||
"provider": "Plausible Insights", "country": "EE", "eu_adequate": True,
|
||||
"requires_consent": False, "legal_ref": "EU-Anbieter, cookieless",
|
||||
},
|
||||
# --- CDN & Fonts ---
|
||||
r"fonts\.googleapis\.com|fonts\.gstatic\.com": {
|
||||
"id": "google_fonts", "name": "Google Fonts (remote)", "category": "cdn",
|
||||
"provider": "Google LLC", "country": "US", "eu_adequate": False,
|
||||
"requires_consent": True, "legal_ref": "LG Muenchen I, Az. 3 O 17493/20",
|
||||
},
|
||||
r"cdn\.cloudflare\.com|cdnjs\.cloudflare\.com": {
|
||||
"id": "cloudflare_cdn", "name": "Cloudflare CDN", "category": "cdn",
|
||||
"provider": "Cloudflare Inc", "country": "US", "eu_adequate": False,
|
||||
"requires_consent": False, "legal_ref": "Art. 44-49 DSGVO, berechtigtes Interesse",
|
||||
},
|
||||
# --- Chatbots ---
|
||||
r"widget\.intercom\.io|intercomcdn": {
|
||||
"id": "intercom", "name": "Intercom", "category": "chatbot",
|
||||
"provider": "Intercom Inc", "country": "US", "eu_adequate": False,
|
||||
"requires_consent": True, "legal_ref": "Art. 44-49 DSGVO, KI-gestuetzt",
|
||||
},
|
||||
r"tidio\.co|tidioChatApi": {
|
||||
"id": "tidio", "name": "Tidio Chat", "category": "chatbot",
|
||||
"provider": "Tidio LLC", "country": "PL", "eu_adequate": True,
|
||||
"requires_consent": False, "legal_ref": "EU-Anbieter",
|
||||
},
|
||||
r"zendesk\.com/embeddable|zdassets": {
|
||||
"id": "zendesk", "name": "Zendesk", "category": "chatbot",
|
||||
"provider": "Zendesk Inc", "country": "US", "eu_adequate": False,
|
||||
"requires_consent": True, "legal_ref": "Art. 44-49 DSGVO",
|
||||
},
|
||||
# --- Payment ---
|
||||
r"js\.stripe\.com|stripe\.com/v3": {
|
||||
"id": "stripe", "name": "Stripe", "category": "payment",
|
||||
"provider": "Stripe Inc", "country": "US", "eu_adequate": False,
|
||||
"requires_consent": False, "legal_ref": "Art. 6(1)(b) Vertragserfuellung, SCCs",
|
||||
},
|
||||
r"paypal\.com/sdk|paypalobjects": {
|
||||
"id": "paypal", "name": "PayPal", "category": "payment",
|
||||
"provider": "PayPal Holdings", "country": "US", "eu_adequate": False,
|
||||
"requires_consent": False, "legal_ref": "Art. 6(1)(b) Vertragserfuellung",
|
||||
},
|
||||
r"klarna\.com|klarna-payments": {
|
||||
"id": "klarna", "name": "Klarna", "category": "payment",
|
||||
"provider": "Klarna AB", "country": "SE", "eu_adequate": True,
|
||||
"requires_consent": False, "legal_ref": "EU, aber Art. 22 DSGVO bei Bonitaetspruefung!",
|
||||
},
|
||||
# --- Captcha ---
|
||||
r"recaptcha|grecaptcha": {
|
||||
"id": "recaptcha", "name": "Google reCAPTCHA", "category": "other",
|
||||
"provider": "Google LLC", "country": "US", "eu_adequate": False,
|
||||
"requires_consent": True, "legal_ref": "Art. 44-49 DSGVO, §25 TDDDG",
|
||||
},
|
||||
# --- Video ---
|
||||
r"youtube\.com/embed|youtube-nocookie|ytimg": {
|
||||
"id": "youtube", "name": "YouTube", "category": "other",
|
||||
"provider": "Google LLC", "country": "US", "eu_adequate": False,
|
||||
"requires_consent": True, "legal_ref": "Art. 44-49 DSGVO, 2-Klick empfohlen",
|
||||
},
|
||||
# --- Consent Management ---
|
||||
r"didomi|cookiebot|onetrust|usercentrics|consentmanager|quantcast": {
|
||||
"id": "cmp", "name": "Consent Management Platform", "category": "other",
|
||||
"provider": "Various", "country": "EU", "eu_adequate": True,
|
||||
"requires_consent": False, "legal_ref": "CMP vorhanden — gut",
|
||||
},
|
||||
}
|
||||
|
||||
AI_TEXT_PATTERNS = [
|
||||
r"k(?:ue|ü)nstliche.?intelligenz",
|
||||
r"artificial.?intelligence",
|
||||
r"machine.?learning",
|
||||
r"maschinelles.?lernen",
|
||||
r"KI.?gest(?:ue|ü)tzt",
|
||||
r"AI.?powered",
|
||||
r"chatgpt|openai",
|
||||
r"deep.?learning",
|
||||
r"neural.?net",
|
||||
r"automatisierte.?entscheidung",
|
||||
]
|
||||
|
||||
FOOTER_LINK_PATTERNS = [
|
||||
(r'href="([^"]*(?:impressum|imprint|legal-notice)[^"]*)"', "impressum"),
|
||||
(r'href="([^"]*(?:datenschutz|privacy|dsgvo)[^"]*)"', "datenschutz"),
|
||||
(r'href="([^"]*(?:agb|terms|nutzungsbedingungen)[^"]*)"', "agb"),
|
||||
(r'href="([^"]*(?:cookie)[^"]*)"', "cookies"),
|
||||
]
|
||||
|
||||
|
||||
async def scan_website(base_url: str) -> ScanResult:
|
||||
"""Scan a website: start page + footer links for services and AI indicators."""
|
||||
result = ScanResult()
|
||||
parsed = urlparse(base_url)
|
||||
origin = f"{parsed.scheme}://{parsed.netloc}"
|
||||
|
||||
async with httpx.AsyncClient(timeout=10.0, follow_redirects=True) as client:
|
||||
# 1. Fetch start page
|
||||
start_html = await _fetch_page(client, origin, result)
|
||||
if not start_html:
|
||||
return result
|
||||
|
||||
# 2. Discover footer links
|
||||
page_urls = {origin}
|
||||
page_urls.add(base_url) # Also scan the provided URL
|
||||
for pattern, _ in FOOTER_LINK_PATTERNS:
|
||||
for match in re.finditer(pattern, start_html, re.IGNORECASE):
|
||||
href = match.group(1)
|
||||
if href.startswith("/"):
|
||||
href = urljoin(origin, href)
|
||||
if href.startswith(origin):
|
||||
page_urls.add(href)
|
||||
|
||||
# 3. Scan all pages (max 10)
|
||||
for url in list(page_urls)[:10]:
|
||||
html = start_html if url == origin else await _fetch_page(client, url, result)
|
||||
if html:
|
||||
_detect_services(html, url, result)
|
||||
_detect_ai_mentions(html, url, result)
|
||||
|
||||
# Deduplicate services
|
||||
seen = set()
|
||||
unique = []
|
||||
for svc in result.detected_services:
|
||||
if svc.id not in seen:
|
||||
seen.add(svc.id)
|
||||
unique.append(svc)
|
||||
result.detected_services = unique
|
||||
|
||||
result.chatbot_detected = any(s.category == "chatbot" for s in result.detected_services)
|
||||
if result.chatbot_detected:
|
||||
result.chatbot_provider = next(
|
||||
s.name for s in result.detected_services if s.category == "chatbot"
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
async def _fetch_page(
|
||||
client: httpx.AsyncClient, url: str, result: ScanResult,
|
||||
) -> str:
|
||||
"""Fetch a single page. Returns HTML or empty string on failure."""
|
||||
try:
|
||||
resp = await client.get(url, headers={"User-Agent": USER_AGENT})
|
||||
result.pages_scanned.append(url)
|
||||
if resp.status_code >= 400:
|
||||
result.missing_pages[url] = resp.status_code
|
||||
return ""
|
||||
return resp.text
|
||||
except Exception as e:
|
||||
logger.warning("Failed to fetch %s: %s", url, e)
|
||||
return ""
|
||||
|
||||
|
||||
def _detect_services(html: str, url: str, result: ScanResult) -> None:
|
||||
"""Detect third-party services in HTML."""
|
||||
for pattern, meta in SERVICE_REGISTRY.items():
|
||||
if re.search(pattern, html, re.IGNORECASE):
|
||||
result.detected_services.append(DetectedService(
|
||||
found_on=url, **meta,
|
||||
))
|
||||
|
||||
|
||||
def _detect_ai_mentions(html: str, url: str, result: ScanResult) -> None:
|
||||
"""Detect AI/ML text mentions in page content."""
|
||||
# Strip scripts/styles first for text-only search
|
||||
clean = re.sub(r"<(script|style)[^>]*>.*?</\1>", "", html, flags=re.DOTALL | re.IGNORECASE)
|
||||
clean = re.sub(r"<[^>]+>", " ", clean)
|
||||
for pattern in AI_TEXT_PATTERNS:
|
||||
match = re.search(pattern, clean, re.IGNORECASE)
|
||||
if match:
|
||||
context = clean[max(0, match.start() - 40):match.end() + 40].strip()
|
||||
result.ai_mentions.append(f"{url}: ...{context}...")
|
||||
Reference in New Issue
Block a user