461 lines
17 KiB
Python
461 lines
17 KiB
Python
"""
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Analysis Agent (漏洞分析层) - LLM 驱动版
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LLM 是真正的安全分析大脑！
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- LLM 决定分析策略
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- LLM 选择使用什么工具
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- LLM 决定深入分析哪些代码
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- LLM 判断发现的问题是否是真实漏洞
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类型: ReAct (真正的!)
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"""
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import asyncio
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import json
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import logging
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import re
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from typing import List, Dict, Any, Optional
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from dataclasses import dataclass
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from .base import BaseAgent, AgentConfig, AgentResult, AgentType, AgentPattern
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from ..json_parser import AgentJsonParser
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logger = logging.getLogger(__name__)
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ANALYSIS_SYSTEM_PROMPT = """你是 DeepAudit 的漏洞分析 Agent，一个**自主**的安全专家。
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## 你的角色
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你是安全审计的**核心大脑**，不是工具执行器。你需要：
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1. 自主制定分析策略
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2. 选择最有效的工具和方法
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3. 深入分析可疑代码
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4. 判断是否是真实漏洞
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5. 动态调整分析方向
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## 你可以使用的工具
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### 文件操作
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- **read_file**: 读取文件内容
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参数: file_path (str), start_line (int), end_line (int)
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- **list_files**: 列出目录文件
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参数: directory (str), pattern (str)
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- **search_code**: 代码关键字搜索
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参数: keyword (str), max_results (int)
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### 深度分析
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- **pattern_match**: 危险模式匹配
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参数: pattern (str), file_types (list)
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- **code_analysis**: LLM 深度代码分析 ⭐
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参数: code (str), file_path (str), focus (str)
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- **dataflow_analysis**: 数据流追踪
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参数: source (str), sink (str)
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### 外部静态分析工具
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- **semgrep_scan**: Semgrep 静态分析（推荐首先使用）
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参数: rules (str), max_results (int)
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- **bandit_scan**: Python 安全扫描
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参数: target (str)
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- **gitleaks_scan**: Git 密钥泄露扫描
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参数: target (str)
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- **trufflehog_scan**: 敏感信息扫描
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参数: target (str)
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- **npm_audit**: NPM 依赖漏洞扫描
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参数: target (str)
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- **safety_scan**: Python 依赖安全扫描
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参数: target (str)
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- **osv_scan**: OSV 漏洞数据库扫描
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参数: target (str)
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### RAG 语义搜索
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- **security_search**: 安全相关代码搜索
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参数: vulnerability_type (str), top_k (int)
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- **function_context**: 函数上下文分析
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参数: function_name (str)
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## 工作方式
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每一步，你需要输出：
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```
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Thought: [分析当前情况，思考下一步应该做什么]
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Action: [工具名称]
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Action Input: [JSON 格式的参数]
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```
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当你完成分析后，输出：
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```
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Thought: [总结所有发现]
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Final Answer: [JSON 格式的漏洞报告]
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```
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## Final Answer 格式
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```json
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{
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"findings": [
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{
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"vulnerability_type": "sql_injection",
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"severity": "high",
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"title": "SQL 注入漏洞",
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"description": "详细描述",
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"file_path": "path/to/file.py",
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"line_start": 42,
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"code_snippet": "危险代码片段",
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"source": "污点来源",
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"sink": "危险函数",
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"suggestion": "修复建议",
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"confidence": 0.9,
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"needs_verification": true
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}
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],
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"summary": "分析总结"
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}
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```
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## 分析策略建议
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1. **快速扫描**: 先用 semgrep_scan 获得概览
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2. **重点深入**: 对可疑文件使用 read_file + code_analysis
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3. **模式搜索**: 用 search_code 找危险模式 (eval, exec, query 等)
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4. **语义搜索**: 用 RAG 找相似的漏洞模式
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5. **数据流**: 用 dataflow_analysis 追踪用户输入
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## 重点关注的漏洞类型
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- SQL 注入 (query, execute, raw SQL)
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- XSS (innerHTML, document.write, v-html)
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- 命令注入 (exec, system, subprocess)
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- 路径遍历 (open, readFile, path 拼接)
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- SSRF (requests, fetch, http client)
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- 硬编码密钥 (password, secret, api_key)
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- 不安全的反序列化 (pickle, yaml.load, eval)
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## 重要原则
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1. **质量优先** - 宁可深入分析几个真实漏洞，不要浅尝辄止报告大量误报
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2. **上下文分析** - 看到可疑代码要读取上下文，理解完整逻辑
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3. **自主判断** - 不要机械相信工具输出，要用你的专业知识判断
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4. **持续探索** - 发现一个问题后，思考是否有相关问题
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现在开始你的安全分析！"""
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@dataclass
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class AnalysisStep:
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"""分析步骤"""
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thought: str
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action: Optional[str] = None
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action_input: Optional[Dict] = None
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observation: Optional[str] = None
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is_final: bool = False
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final_answer: Optional[Dict] = None
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class AnalysisAgent(BaseAgent):
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"""
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漏洞分析 Agent - LLM 驱动版
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LLM 全程参与，自主决定：
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1. 分析什么
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2. 使用什么工具
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3. 深入哪些代码
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4. 报告什么发现
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"""
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def __init__(
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self,
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llm_service,
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tools: Dict[str, Any],
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event_emitter=None,
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):
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config = AgentConfig(
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name="Analysis",
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agent_type=AgentType.ANALYSIS,
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pattern=AgentPattern.REACT,
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max_iterations=30,
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system_prompt=ANALYSIS_SYSTEM_PROMPT,
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)
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super().__init__(config, llm_service, tools, event_emitter)
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self._conversation_history: List[Dict[str, str]] = []
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self._steps: List[AnalysisStep] = []
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def _parse_llm_response(self, response: str) -> AnalysisStep:
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"""解析 LLM 响应"""
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step = AnalysisStep(thought="")
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# 提取 Thought
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thought_match = re.search(r'Thought:\s*(.*?)(?=Action:|Final Answer:|$)', response, re.DOTALL)
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if thought_match:
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step.thought = thought_match.group(1).strip()
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# 检查是否是最终答案
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final_match = re.search(r'Final Answer:\s*(.*?)$', response, re.DOTALL)
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if final_match:
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step.is_final = True
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answer_text = final_match.group(1).strip()
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answer_text = re.sub(r'```json\s*', '', answer_text)
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answer_text = re.sub(r'```\s*', '', answer_text)
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# 使用增强的 JSON 解析器
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step.final_answer = AgentJsonParser.parse(
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answer_text,
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default={"findings": [], "raw_answer": answer_text}
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)
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# 确保 findings 格式正确
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if "findings" in step.final_answer:
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step.final_answer["findings"] = [
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f for f in step.final_answer["findings"]
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if isinstance(f, dict)
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]
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return step
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# 提取 Action
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action_match = re.search(r'Action:\s*(\w+)', response)
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if action_match:
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step.action = action_match.group(1).strip()
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# 提取 Action Input
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input_match = re.search(r'Action Input:\s*(.*?)(?=Thought:|Action:|Observation:|$)', response, re.DOTALL)
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if input_match:
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input_text = input_match.group(1).strip()
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input_text = re.sub(r'```json\s*', '', input_text)
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input_text = re.sub(r'```\s*', '', input_text)
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# 使用增强的 JSON 解析器
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step.action_input = AgentJsonParser.parse(
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input_text,
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default={"raw_input": input_text}
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)
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return step
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async def run(self, input_data: Dict[str, Any]) -> AgentResult:
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"""
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执行漏洞分析 - LLM 全程参与！
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"""
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import time
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start_time = time.time()
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project_info = input_data.get("project_info", {})
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config = input_data.get("config", {})
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plan = input_data.get("plan", {})
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previous_results = input_data.get("previous_results", {})
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task = input_data.get("task", "")
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task_context = input_data.get("task_context", "")
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# 🔥 处理交接信息
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handoff = input_data.get("handoff")
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if handoff:
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from .base import TaskHandoff
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if isinstance(handoff, dict):
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handoff = TaskHandoff.from_dict(handoff)
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self.receive_handoff(handoff)
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# 从 Recon 结果获取上下文
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recon_data = previous_results.get("recon", {})
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if isinstance(recon_data, dict) and "data" in recon_data:
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recon_data = recon_data["data"]
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tech_stack = recon_data.get("tech_stack", {})
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entry_points = recon_data.get("entry_points", [])
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high_risk_areas = recon_data.get("high_risk_areas", plan.get("high_risk_areas", []))
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initial_findings = recon_data.get("initial_findings", [])
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# 🔥 构建包含交接上下文的初始消息
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handoff_context = self.get_handoff_context()
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initial_message = f"""请开始对项目进行安全漏洞分析。
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## 项目信息
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- 名称: {project_info.get('name', 'unknown')}
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- 语言: {tech_stack.get('languages', [])}
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- 框架: {tech_stack.get('frameworks', [])}
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{handoff_context if handoff_context else f'''## 上下文信息
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### 高风险区域
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{json.dumps(high_risk_areas[:20], ensure_ascii=False)}
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### 入口点 (前10个)
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{json.dumps(entry_points[:10], ensure_ascii=False, indent=2)}
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### 初步发现 (如果有)
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{json.dumps(initial_findings[:5], ensure_ascii=False, indent=2) if initial_findings else "无"}'''}
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## 任务
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{task_context or task or '进行全面的安全漏洞分析，发现代码中的安全问题。'}
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## 目标漏洞类型
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{config.get('target_vulnerabilities', ['all'])}
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## 可用工具
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{self.get_tools_description()}
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请开始你的安全分析。首先思考分析策略，然后选择合适的工具开始分析。"""
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# 🔥 记录工作开始
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self.record_work("开始安全漏洞分析")
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# 初始化对话历史
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self._conversation_history = [
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{"role": "system", "content": self.config.system_prompt},
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{"role": "user", "content": initial_message},
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]
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self._steps = []
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all_findings = []
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await self.emit_thinking("🔬 Analysis Agent 启动，LLM 开始自主安全分析...")
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try:
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for iteration in range(self.config.max_iterations):
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if self.is_cancelled:
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break
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self._iteration = iteration + 1
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# 🔥 再次检查取消标志（在LLM调用之前）
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if self.is_cancelled:
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await self.emit_thinking("🛑 任务已取消，停止执行")
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break
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# 调用 LLM 进行思考和决策（流式输出）
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try:
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llm_output, tokens_this_round = await self.stream_llm_call(
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self._conversation_history,
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temperature=0.1,
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max_tokens=2048,
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)
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except asyncio.CancelledError:
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logger.info(f"[{self.name}] LLM call cancelled")
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break
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self._total_tokens += tokens_this_round
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# 解析 LLM 响应
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step = self._parse_llm_response(llm_output)
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self._steps.append(step)
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# 🔥 发射 LLM 思考内容事件 - 展示安全分析的思考过程
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if step.thought:
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await self.emit_llm_thought(step.thought, iteration + 1)
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# 添加 LLM 响应到历史
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self._conversation_history.append({
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"role": "assistant",
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"content": llm_output,
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})
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# 检查是否完成
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if step.is_final:
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await self.emit_llm_decision("完成安全分析", "LLM 判断分析已充分")
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if step.final_answer and "findings" in step.final_answer:
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all_findings = step.final_answer["findings"]
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# 🔥 发射每个发现的事件
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for finding in all_findings[:5]: # 限制数量
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await self.emit_finding(
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finding.get("title", "Unknown"),
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finding.get("severity", "medium"),
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finding.get("vulnerability_type", "other"),
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finding.get("file_path", "")
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)
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# 🔥 记录洞察
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self.add_insight(
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f"发现 {finding.get('severity', 'medium')} 级别漏洞: {finding.get('title', 'Unknown')}"
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)
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# 🔥 记录工作完成
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self.record_work(f"完成安全分析，发现 {len(all_findings)} 个潜在漏洞")
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await self.emit_llm_complete(
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f"分析完成，发现 {len(all_findings)} 个潜在漏洞",
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self._total_tokens
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)
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break
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# 执行工具
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if step.action:
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# 🔥 发射 LLM 动作决策事件
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await self.emit_llm_action(step.action, step.action_input or {})
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observation = await self.execute_tool(
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step.action,
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step.action_input or {}
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)
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step.observation = observation
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# 🔥 发射 LLM 观察事件
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await self.emit_llm_observation(observation)
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# 添加观察结果到历史
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self._conversation_history.append({
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"role": "user",
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"content": f"Observation:\n{observation}",
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})
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else:
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# LLM 没有选择工具，提示它继续
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await self.emit_llm_decision("继续分析", "LLM 需要更多分析")
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self._conversation_history.append({
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"role": "user",
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"content": "请继续分析。选择一个工具执行，或者如果分析完成，输出 Final Answer 汇总所有发现。",
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})
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# 处理结果
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duration_ms = int((time.time() - start_time) * 1000)
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# 标准化发现
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standardized_findings = []
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for finding in all_findings:
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standardized = {
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"vulnerability_type": finding.get("vulnerability_type", "other"),
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"severity": finding.get("severity", "medium"),
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"title": finding.get("title", "Unknown Finding"),
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"description": finding.get("description", ""),
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"file_path": finding.get("file_path", ""),
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"line_start": finding.get("line_start") or finding.get("line", 0),
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"code_snippet": finding.get("code_snippet", ""),
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"source": finding.get("source", ""),
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"sink": finding.get("sink", ""),
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"suggestion": finding.get("suggestion", ""),
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"confidence": finding.get("confidence", 0.7),
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"needs_verification": finding.get("needs_verification", True),
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}
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standardized_findings.append(standardized)
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await self.emit_event(
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"info",
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f"Analysis Agent 完成: {len(standardized_findings)} 个发现, {self._iteration} 轮迭代, {self._tool_calls} 次工具调用"
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)
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return AgentResult(
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success=True,
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data={
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"findings": standardized_findings,
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"steps": [
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{
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"thought": s.thought,
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"action": s.action,
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"action_input": s.action_input,
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"observation": s.observation[:500] if s.observation else None,
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}
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for s in self._steps
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],
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},
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iterations=self._iteration,
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tool_calls=self._tool_calls,
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tokens_used=self._total_tokens,
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duration_ms=duration_ms,
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)
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except Exception as e:
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logger.error(f"Analysis Agent failed: {e}", exc_info=True)
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return AgentResult(success=False, error=str(e))
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def get_conversation_history(self) -> List[Dict[str, str]]:
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"""获取对话历史"""
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return self._conversation_history
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def get_steps(self) -> List[AnalysisStep]:
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"""获取执行步骤"""
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return self._steps
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