92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
"""LLM analysis client for GuardDog findings.
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Supports any OpenAI-compatible API endpoint with configurable model.
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"""
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import json
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import httpx
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from guarddog_nexus.config import config
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from guarddog_nexus.constants import LLM_ANALYSIS_SYSTEM_PROMPT
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from guarddog_nexus.logging_setup import log
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def _build_user_message(finding: dict) -> str:
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"""Build a concise prompt from a finding's data."""
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rule = finding.get("rule", "unknown")
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severity = finding.get("severity", "unknown")
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message = finding.get("message", "")
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location = finding.get("location", "")
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code = finding.get("code", "")
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prompt = (
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f"Rule: {rule}\n"
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f"Severity: {severity}\n"
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f"Message: {message}\n"
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)
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if location:
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prompt += f"Location: {location}\n"
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if code:
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prompt += f"Code snippet:\n```\n{code}\n```\n"
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prompt += (
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"\nAnalyse this finding and return JSON with keys: "
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"verdict, summary, analysis, severity_rating."
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)
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return prompt
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async def analyze_finding(finding_data: dict) -> dict | None:
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"""Send a finding to the LLM for security analysis.
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Returns parsed JSON dict on success, or None on failure.
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"""
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if not config.llm_api_key:
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log.warning("LLM_API_KEY not set — skipping LLM analysis")
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return None
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url = f"{config.llm_api_base.rstrip('/')}/chat/completions"
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headers = {
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"Authorization": f"Bearer {config.llm_api_key}",
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"Content-Type": "application/json",
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}
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payload = {
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"model": config.llm_model,
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"messages": [
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{"role": "system", "content": LLM_ANALYSIS_SYSTEM_PROMPT},
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{"role": "user", "content": _build_user_message(finding_data)},
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],
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"temperature": 0.3,
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"response_format": {"type": "json_object"},
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}
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try:
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async with httpx.AsyncClient(
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timeout=config.llm_timeout, headers=headers
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) as client:
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resp = await client.post(url, json=payload)
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resp.raise_for_status()
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body = resp.json()
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except httpx.TimeoutException:
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log.error(
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"LLM analysis timed out after %ds for rule=%s",
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config.llm_timeout,
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finding_data.get("rule"),
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)
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return None
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except Exception as e:
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log.warning("LLM analysis failed for rule=%s: %s", finding_data.get("rule"), e)
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return None
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try:
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content = body["choices"][0]["message"]["content"]
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return json.loads(content)
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except (KeyError, IndexError, json.JSONDecodeError) as e:
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log.warning(
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"LLM response parse error for rule=%s: %s",
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finding_data.get("rule"),
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e,
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)
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return None
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