691 lines
28 KiB
Python
691 lines
28 KiB
Python
"""
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Analysis Agent (漏洞分析层)
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负责代码审计、RAG 查询、模式匹配、数据流分析
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类型: ReAct
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"""
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import asyncio
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import logging
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from typing import List, Dict, Any, Optional
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from .base import BaseAgent, AgentConfig, AgentResult, AgentType, AgentPattern
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logger = logging.getLogger(__name__)
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ANALYSIS_SYSTEM_PROMPT = """你是 DeepAudit 的漏洞分析 Agent，负责深度代码安全分析。
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## 你的职责
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1. 使用静态分析工具快速扫描
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2. 使用 RAG 进行语义代码搜索
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3. 追踪数据流（从用户输入到危险函数）
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4. 分析业务逻辑漏洞
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5. 评估漏洞严重程度
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## 你可以使用的工具
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### 外部扫描工具
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- semgrep_scan: Semgrep 静态分析（推荐首先使用）
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- bandit_scan: Python 安全扫描
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### RAG 语义搜索
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- rag_query: 语义代码搜索
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- security_search: 安全相关代码搜索
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- function_context: 函数上下文分析
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### 深度分析
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- pattern_match: 危险模式匹配
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- code_analysis: LLM 深度代码分析
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- dataflow_analysis: 数据流追踪
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- vulnerability_validation: 漏洞验证
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### 文件操作
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- read_file: 读取文件
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- search_code: 关键字搜索
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## 分析策略
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1. **快速扫描**: 先用 Semgrep 快速发现问题
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2. **语义搜索**: 用 RAG 找到相关代码
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3. **深度分析**: 对可疑代码进行 LLM 分析
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4. **数据流追踪**: 追踪用户输入的流向
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## 重点关注
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- SQL 注入、NoSQL 注入
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- XSS（反射型、存储型、DOM型）
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- 命令注入、代码注入
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- 路径遍历、任意文件访问
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- SSRF、XXE
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- 不安全的反序列化
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- 认证/授权绕过
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- 敏感信息泄露
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## 输出格式
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发现漏洞时，返回结构化信息：
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```json
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{
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"findings": [
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{
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"vulnerability_type": "漏洞类型",
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"severity": "critical/high/medium/low",
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"title": "漏洞标题",
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"description": "详细描述",
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"file_path": "文件路径",
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"line_start": 行号,
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"code_snippet": "代码片段",
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"source": "污点源",
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"sink": "危险函数",
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"suggestion": "修复建议",
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"needs_verification": true/false
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}
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]
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}
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```
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请系统性地分析代码，发现真实的安全漏洞。"""
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class AnalysisAgent(BaseAgent):
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"""
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漏洞分析 Agent
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使用 ReAct 模式进行迭代分析
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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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tools=[
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"semgrep_scan", "bandit_scan",
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"rag_query", "security_search", "function_context",
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"pattern_match", "code_analysis", "dataflow_analysis",
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"vulnerability_validation",
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"read_file", "search_code",
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],
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)
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super().__init__(config, llm_service, tools, event_emitter)
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async def run(self, input_data: Dict[str, Any]) -> AgentResult:
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"""执行漏洞分析"""
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import time
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start_time = time.time()
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phase_name = input_data.get("phase_name", "analysis")
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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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# 从之前的 Recon 结果获取信息
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recon_data = previous_results.get("recon", {}).get("data", {})
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high_risk_areas = recon_data.get("high_risk_areas", plan.get("high_risk_areas", []))
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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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try:
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all_findings = []
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# 1. 静态分析阶段
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if phase_name in ["static_analysis", "analysis"]:
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await self.emit_thinking("执行静态代码分析...")
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static_findings = await self._run_static_analysis(tech_stack)
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all_findings.extend(static_findings)
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# 2. 深度分析阶段
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if phase_name in ["deep_analysis", "analysis"]:
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await self.emit_thinking("执行深度漏洞分析...")
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# 分析入口点
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deep_findings = await self._analyze_entry_points(entry_points)
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all_findings.extend(deep_findings)
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# 分析高风险区域（现在会调用 LLM）
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risk_findings = await self._analyze_high_risk_areas(high_risk_areas)
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all_findings.extend(risk_findings)
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# 语义搜索常见漏洞（现在会调用 LLM）
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vuln_types = config.get("target_vulnerabilities", [
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"sql_injection", "xss", "command_injection",
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"path_traversal", "ssrf", "hardcoded_secret",
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])
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for vuln_type in vuln_types[:5]: # 限制数量
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if self.is_cancelled:
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break
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await self.emit_thinking(f"搜索 {vuln_type} 相关代码...")
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vuln_findings = await self._search_vulnerability_pattern(vuln_type)
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all_findings.extend(vuln_findings)
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# 🔥 3. 如果还没有发现，使用 LLM 进行全面扫描
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if len(all_findings) < 3:
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await self.emit_thinking("执行 LLM 全面代码扫描...")
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llm_findings = await self._llm_comprehensive_scan(tech_stack)
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all_findings.extend(llm_findings)
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# 去重
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all_findings = self._deduplicate_findings(all_findings)
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duration_ms = int((time.time() - start_time) * 1000)
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await self.emit_event(
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"info",
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f"分析完成: 发现 {len(all_findings)} 个潜在漏洞"
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)
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return AgentResult(
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success=True,
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data={"findings": all_findings},
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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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async def _run_static_analysis(self, tech_stack: Dict) -> List[Dict]:
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"""运行静态分析工具"""
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findings = []
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# Semgrep 扫描
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semgrep_tool = self.tools.get("semgrep_scan")
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if semgrep_tool:
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await self.emit_tool_call("semgrep_scan", {"rules": "p/security-audit"})
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result = await semgrep_tool.execute(rules="p/security-audit", max_results=30)
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if result.success and result.metadata.get("findings_count", 0) > 0:
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for finding in result.metadata.get("findings", []):
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findings.append({
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"vulnerability_type": self._map_semgrep_rule(finding.get("check_id", "")),
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"severity": self._map_semgrep_severity(finding.get("extra", {}).get("severity", "")),
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"title": finding.get("check_id", "Semgrep Finding"),
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"description": finding.get("extra", {}).get("message", ""),
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"file_path": finding.get("path", ""),
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"line_start": finding.get("start", {}).get("line", 0),
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"code_snippet": finding.get("extra", {}).get("lines", ""),
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"source": "semgrep",
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"needs_verification": True,
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})
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# Bandit 扫描 (Python)
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languages = tech_stack.get("languages", [])
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if "Python" in languages:
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bandit_tool = self.tools.get("bandit_scan")
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if bandit_tool:
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await self.emit_tool_call("bandit_scan", {})
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result = await bandit_tool.execute()
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if result.success and result.metadata.get("findings_count", 0) > 0:
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for finding in result.metadata.get("findings", []):
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findings.append({
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"vulnerability_type": self._map_bandit_test(finding.get("test_id", "")),
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"severity": finding.get("issue_severity", "medium").lower(),
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"title": finding.get("test_name", "Bandit Finding"),
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"description": finding.get("issue_text", ""),
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"file_path": finding.get("filename", ""),
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"line_start": finding.get("line_number", 0),
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"code_snippet": finding.get("code", ""),
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"source": "bandit",
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"needs_verification": True,
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})
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return findings
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async def _analyze_entry_points(self, entry_points: List[Dict]) -> List[Dict]:
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"""分析入口点"""
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findings = []
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code_analysis_tool = self.tools.get("code_analysis")
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read_tool = self.tools.get("read_file")
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if not code_analysis_tool or not read_tool:
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return findings
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# 分析前几个入口点
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for ep in entry_points[:10]:
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if self.is_cancelled:
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break
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file_path = ep.get("file", "")
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line = ep.get("line", 1)
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if not file_path:
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continue
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# 读取文件内容
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read_result = await read_tool.execute(
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file_path=file_path,
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start_line=max(1, line - 20),
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end_line=line + 50,
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)
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if not read_result.success:
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continue
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# 深度分析
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analysis_result = await code_analysis_tool.execute(
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code=read_result.data,
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file_path=file_path,
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)
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if analysis_result.success and analysis_result.metadata.get("issues"):
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for issue in analysis_result.metadata["issues"]:
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findings.append({
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"vulnerability_type": issue.get("type", "unknown"),
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"severity": issue.get("severity", "medium"),
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"title": issue.get("title", "Security Issue"),
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"description": issue.get("description", ""),
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"file_path": file_path,
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"line_start": issue.get("line", line),
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"code_snippet": issue.get("code_snippet", ""),
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"suggestion": issue.get("suggestion", ""),
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"source": "code_analysis",
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"needs_verification": True,
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})
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return findings
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async def _analyze_high_risk_areas(self, high_risk_areas: List[str]) -> List[Dict]:
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"""分析高风险区域 - 使用 LLM 深度分析"""
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findings = []
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read_tool = self.tools.get("read_file")
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search_tool = self.tools.get("search_code")
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code_analysis_tool = self.tools.get("code_analysis")
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if not search_tool:
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return findings
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# 在高风险区域搜索危险模式
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dangerous_patterns = [
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("execute(", "sql_injection"),
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("query(", "sql_injection"),
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("eval(", "code_injection"),
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("system(", "command_injection"),
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("exec(", "command_injection"),
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("subprocess", "command_injection"),
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("innerHTML", "xss"),
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("document.write", "xss"),
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("open(", "path_traversal"),
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("requests.get", "ssrf"),
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]
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analyzed_files = set()
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for pattern, vuln_type in dangerous_patterns[:8]:
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if self.is_cancelled:
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break
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result = await search_tool.execute(keyword=pattern, max_results=10)
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if result.success and result.metadata.get("matches", 0) > 0:
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for match in result.metadata.get("results", [])[:5]:
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file_path = match.get("file", "")
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line = match.get("line", 0)
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# 避免重复分析同一个文件的同一区域
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file_key = f"{file_path}:{line // 50}"
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if file_key in analyzed_files:
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continue
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analyzed_files.add(file_key)
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# 🔥 使用 LLM 深度分析找到的代码
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if read_tool and code_analysis_tool:
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await self.emit_thinking(f"LLM 分析 {file_path}:{line} 的 {vuln_type} 风险...")
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# 读取代码上下文
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read_result = await read_tool.execute(
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file_path=file_path,
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start_line=max(1, line - 15),
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end_line=line + 25,
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)
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if read_result.success:
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# 调用 LLM 分析
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analysis_result = await code_analysis_tool.execute(
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code=read_result.data,
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file_path=file_path,
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focus=vuln_type,
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)
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if analysis_result.success and analysis_result.metadata.get("issues"):
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for issue in analysis_result.metadata["issues"]:
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findings.append({
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"vulnerability_type": issue.get("type", vuln_type),
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"severity": issue.get("severity", "medium"),
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"title": issue.get("title", f"LLM 发现: {vuln_type}"),
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"description": issue.get("description", ""),
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"file_path": file_path,
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"line_start": issue.get("line", line),
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"code_snippet": issue.get("code_snippet", match.get("match", "")),
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"suggestion": issue.get("suggestion", ""),
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"ai_explanation": issue.get("ai_explanation", ""),
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"source": "llm_analysis",
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"needs_verification": True,
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})
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elif analysis_result.success:
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# LLM 分析了但没发现问题，仍记录原始发现
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findings.append({
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"vulnerability_type": vuln_type,
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"severity": "low",
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"title": f"疑似 {vuln_type}: {pattern}",
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"description": f"在 {file_path} 中发现危险模式，但 LLM 分析未确认",
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"file_path": file_path,
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"line_start": line,
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"code_snippet": match.get("match", ""),
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"source": "pattern_search",
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"needs_verification": True,
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})
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else:
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# 没有 LLM 工具，使用基础模式匹配
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findings.append({
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"vulnerability_type": vuln_type,
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"severity": "medium",
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"title": f"疑似 {vuln_type}: {pattern}",
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"description": f"在 {file_path} 中发现危险模式 {pattern}",
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"file_path": file_path,
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"line_start": line,
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"code_snippet": match.get("match", ""),
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"source": "pattern_search",
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"needs_verification": True,
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})
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return findings
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async def _search_vulnerability_pattern(self, vuln_type: str) -> List[Dict]:
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"""搜索特定漏洞模式 - 使用 RAG + LLM"""
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findings = []
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security_tool = self.tools.get("security_search")
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code_analysis_tool = self.tools.get("code_analysis")
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read_tool = self.tools.get("read_file")
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if not security_tool:
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return findings
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result = await security_tool.execute(
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vulnerability_type=vuln_type,
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top_k=10,
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)
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if result.success and result.metadata.get("results_count", 0) > 0:
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for item in result.metadata.get("results", [])[:5]:
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file_path = item.get("file_path", "")
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line_start = item.get("line_start", 0)
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content = item.get("content", "")[:2000]
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# 🔥 使用 LLM 验证 RAG 搜索结果
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if code_analysis_tool and content:
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await self.emit_thinking(f"LLM 验证 RAG 发现的 {vuln_type}...")
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analysis_result = await code_analysis_tool.execute(
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code=content,
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file_path=file_path,
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focus=vuln_type,
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)
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if analysis_result.success and analysis_result.metadata.get("issues"):
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for issue in analysis_result.metadata["issues"]:
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findings.append({
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"vulnerability_type": issue.get("type", vuln_type),
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"severity": issue.get("severity", "medium"),
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"title": issue.get("title", f"LLM 确认: {vuln_type}"),
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"description": issue.get("description", ""),
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"file_path": file_path,
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"line_start": issue.get("line", line_start),
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"code_snippet": issue.get("code_snippet", content[:500]),
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"suggestion": issue.get("suggestion", ""),
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"ai_explanation": issue.get("ai_explanation", ""),
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"source": "rag_llm_analysis",
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"needs_verification": True,
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})
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else:
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# RAG 找到但 LLM 未确认
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findings.append({
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"vulnerability_type": vuln_type,
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"severity": "low",
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"title": f"疑似 {vuln_type} (待确认)",
|
||
"description": f"RAG 搜索发现可能存在 {vuln_type}，但 LLM 未确认",
|
||
"file_path": file_path,
|
||
"line_start": line_start,
|
||
"code_snippet": content[:500],
|
||
"source": "rag_search",
|
||
"needs_verification": True,
|
||
})
|
||
else:
|
||
findings.append({
|
||
"vulnerability_type": vuln_type,
|
||
"severity": "medium",
|
||
"title": f"疑似 {vuln_type}",
|
||
"description": f"通过语义搜索发现可能存在 {vuln_type}",
|
||
"file_path": file_path,
|
||
"line_start": line_start,
|
||
"code_snippet": content[:500],
|
||
"source": "rag_search",
|
||
"needs_verification": True,
|
||
})
|
||
|
||
return findings
|
||
|
||
async def _llm_comprehensive_scan(self, tech_stack: Dict) -> List[Dict]:
|
||
"""
|
||
LLM 全面代码扫描
|
||
当其他方法没有发现足够的问题时，使用 LLM 直接分析关键文件
|
||
"""
|
||
findings = []
|
||
|
||
list_tool = self.tools.get("list_files")
|
||
read_tool = self.tools.get("read_file")
|
||
code_analysis_tool = self.tools.get("code_analysis")
|
||
|
||
if not all([list_tool, read_tool, code_analysis_tool]):
|
||
return findings
|
||
|
||
await self.emit_thinking("LLM 全面扫描关键代码文件...")
|
||
|
||
# 确定要扫描的文件类型
|
||
languages = tech_stack.get("languages", [])
|
||
file_patterns = []
|
||
|
||
if "Python" in languages:
|
||
file_patterns.extend(["*.py"])
|
||
if "JavaScript" in languages or "TypeScript" in languages:
|
||
file_patterns.extend(["*.js", "*.ts"])
|
||
if "Go" in languages:
|
||
file_patterns.extend(["*.go"])
|
||
if "Java" in languages:
|
||
file_patterns.extend(["*.java"])
|
||
if "PHP" in languages:
|
||
file_patterns.extend(["*.php"])
|
||
|
||
if not file_patterns:
|
||
file_patterns = ["*.py", "*.js", "*.ts", "*.go", "*.java", "*.php"]
|
||
|
||
# 扫描关键目录
|
||
key_dirs = ["src", "app", "api", "routes", "controllers", "handlers", "lib", "utils", "."]
|
||
scanned_files = 0
|
||
max_files_to_scan = 10
|
||
|
||
for key_dir in key_dirs:
|
||
if scanned_files >= max_files_to_scan or self.is_cancelled:
|
||
break
|
||
|
||
for pattern in file_patterns[:3]:
|
||
if scanned_files >= max_files_to_scan or self.is_cancelled:
|
||
break
|
||
|
||
# 列出文件
|
||
list_result = await list_tool.execute(
|
||
directory=key_dir,
|
||
pattern=pattern,
|
||
recursive=True,
|
||
max_files=20,
|
||
)
|
||
|
||
if not list_result.success:
|
||
continue
|
||
|
||
# 从输出中提取文件路径
|
||
output = list_result.data
|
||
file_paths = []
|
||
for line in output.split('\n'):
|
||
line = line.strip()
|
||
if line.startswith('📄 '):
|
||
file_paths.append(line[2:].strip())
|
||
|
||
# 分析每个文件
|
||
for file_path in file_paths[:5]:
|
||
if scanned_files >= max_files_to_scan or self.is_cancelled:
|
||
break
|
||
|
||
# 跳过测试文件和配置文件
|
||
if any(skip in file_path.lower() for skip in ['test', 'spec', 'mock', '__pycache__', 'node_modules']):
|
||
continue
|
||
|
||
await self.emit_thinking(f"LLM 分析文件: {file_path}")
|
||
|
||
# 读取文件
|
||
read_result = await read_tool.execute(
|
||
file_path=file_path,
|
||
max_lines=200,
|
||
)
|
||
|
||
if not read_result.success:
|
||
continue
|
||
|
||
scanned_files += 1
|
||
|
||
# 🔥 LLM 深度分析
|
||
analysis_result = await code_analysis_tool.execute(
|
||
code=read_result.data,
|
||
file_path=file_path,
|
||
)
|
||
|
||
if analysis_result.success and analysis_result.metadata.get("issues"):
|
||
for issue in analysis_result.metadata["issues"]:
|
||
findings.append({
|
||
"vulnerability_type": issue.get("type", "other"),
|
||
"severity": issue.get("severity", "medium"),
|
||
"title": issue.get("title", "LLM 发现的安全问题"),
|
||
"description": issue.get("description", ""),
|
||
"file_path": file_path,
|
||
"line_start": issue.get("line", 0),
|
||
"code_snippet": issue.get("code_snippet", ""),
|
||
"suggestion": issue.get("suggestion", ""),
|
||
"ai_explanation": issue.get("ai_explanation", ""),
|
||
"source": "llm_comprehensive_scan",
|
||
"needs_verification": True,
|
||
})
|
||
|
||
await self.emit_thinking(f"LLM 全面扫描完成，分析了 {scanned_files} 个文件")
|
||
return findings
|
||
|
||
def _deduplicate_findings(self, findings: List[Dict]) -> List[Dict]:
|
||
"""去重发现"""
|
||
seen = set()
|
||
unique = []
|
||
|
||
for finding in findings:
|
||
key = (
|
||
finding.get("file_path", ""),
|
||
finding.get("line_start", 0),
|
||
finding.get("vulnerability_type", ""),
|
||
)
|
||
|
||
if key not in seen:
|
||
seen.add(key)
|
||
unique.append(finding)
|
||
|
||
return unique
|
||
|
||
def _map_semgrep_rule(self, rule_id: str) -> str:
|
||
"""映射 Semgrep 规则到漏洞类型"""
|
||
rule_lower = rule_id.lower()
|
||
|
||
if "sql" in rule_lower:
|
||
return "sql_injection"
|
||
elif "xss" in rule_lower:
|
||
return "xss"
|
||
elif "command" in rule_lower or "injection" in rule_lower:
|
||
return "command_injection"
|
||
elif "path" in rule_lower or "traversal" in rule_lower:
|
||
return "path_traversal"
|
||
elif "ssrf" in rule_lower:
|
||
return "ssrf"
|
||
elif "deserial" in rule_lower:
|
||
return "deserialization"
|
||
elif "secret" in rule_lower or "password" in rule_lower or "key" in rule_lower:
|
||
return "hardcoded_secret"
|
||
elif "crypto" in rule_lower:
|
||
return "weak_crypto"
|
||
else:
|
||
return "other"
|
||
|
||
def _map_semgrep_severity(self, severity: str) -> str:
|
||
"""映射 Semgrep 严重程度"""
|
||
mapping = {
|
||
"ERROR": "high",
|
||
"WARNING": "medium",
|
||
"INFO": "low",
|
||
}
|
||
return mapping.get(severity, "medium")
|
||
|
||
def _map_bandit_test(self, test_id: str) -> str:
|
||
"""映射 Bandit 测试到漏洞类型"""
|
||
mappings = {
|
||
"B101": "assert_used",
|
||
"B102": "exec_used",
|
||
"B103": "hardcoded_password",
|
||
"B104": "hardcoded_bind_all",
|
||
"B105": "hardcoded_password",
|
||
"B106": "hardcoded_password",
|
||
"B107": "hardcoded_password",
|
||
"B108": "hardcoded_tmp",
|
||
"B301": "deserialization",
|
||
"B302": "deserialization",
|
||
"B303": "weak_crypto",
|
||
"B304": "weak_crypto",
|
||
"B305": "weak_crypto",
|
||
"B306": "weak_crypto",
|
||
"B307": "code_injection",
|
||
"B308": "code_injection",
|
||
"B310": "ssrf",
|
||
"B311": "weak_random",
|
||
"B312": "telnet",
|
||
"B501": "ssl_verify",
|
||
"B502": "ssl_verify",
|
||
"B503": "ssl_verify",
|
||
"B504": "ssl_verify",
|
||
"B505": "weak_crypto",
|
||
"B506": "yaml_load",
|
||
"B507": "ssh_key",
|
||
"B601": "command_injection",
|
||
"B602": "command_injection",
|
||
"B603": "command_injection",
|
||
"B604": "command_injection",
|
||
"B605": "command_injection",
|
||
"B606": "command_injection",
|
||
"B607": "command_injection",
|
||
"B608": "sql_injection",
|
||
"B609": "sql_injection",
|
||
"B610": "sql_injection",
|
||
"B611": "sql_injection",
|
||
"B701": "xss",
|
||
"B702": "xss",
|
||
"B703": "xss",
|
||
}
|
||
return mappings.get(test_id, "other")
|
||
|