737 lines
31 KiB
Python
737 lines
31 KiB
Python
"""
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Verification Agent (漏洞验证层) - LLM 驱动版
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LLM 是验证的大脑！
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- LLM 决定如何验证每个漏洞
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- LLM 构造验证策略
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- LLM 分析验证结果
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- LLM 判断是否为真实漏洞
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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 datetime import datetime, timezone
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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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from ..prompts import CORE_SECURITY_PRINCIPLES, VULNERABILITY_PRIORITIES
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logger = logging.getLogger(__name__)
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VERIFICATION_SYSTEM_PROMPT = """你是 DeepAudit 的漏洞验证 Agent，一个**自主**的安全验证专家。
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## 你的角色
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你是漏洞验证的**大脑**，不是机械验证器。你需要：
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1. 理解每个漏洞的上下文
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2. 设计合适的验证策略
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3. 使用工具获取更多信息
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4. 判断漏洞是否真实存在
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5. 评估实际影响
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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**: 读取更多代码上下文
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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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### 沙箱核心工具
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- **sandbox_exec**: 在沙箱中执行命令
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参数: command (str), timeout (int)
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- **sandbox_http**: 发送 HTTP 请求测试
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参数: method (str), url (str), data (dict), headers (dict)
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- **verify_vulnerability**: 自动化漏洞验证
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参数: vulnerability_type (str), target_url (str), payload (str), expected_pattern (str)
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### 🔥 多语言代码测试工具 (按语言选择)
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- **php_test**: 测试 PHP 代码，支持模拟 GET/POST 参数
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参数: file_path (str), php_code (str), get_params (dict), post_params (dict), timeout (int)
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示例: {"file_path": "vuln.php", "get_params": {"cmd": "whoami"}}
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- **python_test**: 测试 Python 代码，支持模拟 Flask/Django 请求
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参数: file_path (str), code (str), request_params (dict), form_data (dict), timeout (int)
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示例: {"code": "import os; os.system(params['cmd'])", "request_params": {"cmd": "id"}}
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- **javascript_test**: 测试 JavaScript/Node.js 代码
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参数: file_path (str), code (str), req_query (dict), req_body (dict), timeout (int)
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示例: {"code": "exec(req.query.cmd)", "req_query": {"cmd": "id"}}
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- **java_test**: 测试 Java 代码，支持模拟 Servlet 请求
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参数: file_path (str), code (str), request_params (dict), timeout (int)
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- **go_test**: 测试 Go 代码
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参数: file_path (str), code (str), args (list), timeout (int)
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- **ruby_test**: 测试 Ruby 代码，支持模拟 Rails 请求
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参数: file_path (str), code (str), params (dict), timeout (int)
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- **shell_test**: 测试 Shell/Bash 脚本
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参数: file_path (str), code (str), args (list), env (dict), timeout (int)
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- **universal_code_test**: 通用多语言测试工具 (自动检测语言)
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参数: language (str), file_path (str), code (str), params (dict), timeout (int)
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### 🔥 漏洞验证专用工具 (按漏洞类型选择，推荐使用)
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- **test_command_injection**: 专门测试命令注入漏洞
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参数: target_file (str), param_name (str), test_command (str), language (str)
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示例: {"target_file": "vuln.php", "param_name": "cmd", "test_command": "whoami"}
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- **test_sql_injection**: 专门测试 SQL 注入漏洞
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参数: target_file (str), param_name (str), db_type (str), injection_type (str)
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示例: {"target_file": "login.php", "param_name": "username", "db_type": "mysql"}
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- **test_xss**: 专门测试 XSS 漏洞
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参数: target_file (str), param_name (str), xss_type (str), context (str)
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示例: {"target_file": "search.php", "param_name": "q", "xss_type": "reflected"}
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- **test_path_traversal**: 专门测试路径遍历漏洞
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参数: target_file (str), param_name (str), target_path (str)
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示例: {"target_file": "download.php", "param_name": "file", "target_path": "/etc/passwd"}
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- **test_ssti**: 专门测试模板注入漏洞
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参数: target_file (str), param_name (str), template_engine (str)
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示例: {"target_file": "render.py", "param_name": "name", "template_engine": "jinja2"}
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- **test_deserialization**: 专门测试反序列化漏洞
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参数: target_file (str), language (str), serialization_format (str)
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示例: {"target_file": "api.php", "language": "php", "serialization_format": "php_serialize"}
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- **universal_vuln_test**: 通用漏洞测试工具 (自动选择测试策略)
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参数: vuln_type (str), target_file (str), param_name (str), additional_params (dict)
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支持: command_injection, sql_injection, xss, path_traversal, ssti, deserialization
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## 工作方式
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你将收到一批待验证的漏洞发现。对于每个发现，你需要：
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```
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Thought: [分析这个漏洞，思考如何验证]
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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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验证完所有发现后，输出：
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```
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Thought: [总结验证结果]
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Final Answer: [JSON 格式的验证报告]
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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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...原始发现字段...,
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"verdict": "confirmed/likely/uncertain/false_positive",
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"confidence": 0.0-1.0,
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"is_verified": true/false,
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"verification_method": "描述验证方法",
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"verification_details": "验证过程和结果详情",
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"poc": {
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"description": "PoC 描述",
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"steps": ["步骤1", "步骤2"],
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"payload": "curl 'http://target/vuln.php?cmd=id' 或完整利用代码"
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},
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"impact": "实际影响分析",
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"recommendation": "修复建议"
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}
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],
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"summary": {
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"total": 数量,
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"confirmed": 数量,
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"likely": 数量,
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"false_positive": 数量
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}
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}
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```
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## 验证判定标准
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- **confirmed**: 漏洞确认存在且可利用，有明确证据
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- **likely**: 高度可能存在漏洞，但无法完全确认
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- **uncertain**: 需要更多信息才能判断
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- **false_positive**: 确认是误报，有明确理由
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## 验证策略建议
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### 对于命令注入漏洞
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1. 使用 **test_command_injection** 工具，它会自动构建测试环境
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2. 或使用对应语言的测试工具 (php_test, python_test 等)
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3. 检查命令输出是否包含 uid=, root, www-data 等特征
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### 对于 SQL 注入漏洞
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1. 使用 **test_sql_injection** 工具
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2. 提供数据库类型 (mysql, postgresql, sqlite)
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3. 检查是否能执行 UNION 查询或提取数据
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### 对于 XSS 漏洞
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1. 使用 **test_xss** 工具
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2. 指定 XSS 类型 (reflected, stored, dom)
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3. 检查 payload 是否在输出中未转义
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### 对于路径遍历漏洞
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1. 使用 **test_path_traversal** 工具
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2. 尝试读取 /etc/passwd 或其他已知文件
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3. 检查是否能访问目标文件
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### 对于模板注入 (SSTI) 漏洞
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1. 使用 **test_ssti** 工具
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2. 指定模板引擎 (jinja2, twig, freemarker 等)
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3. 检查数学表达式是否被执行
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### 对于反序列化漏洞
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1. 使用 **test_deserialization** 工具
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2. 指定语言和序列化格式
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3. 检查是否能执行任意代码
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### 对于其他漏洞
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1. **上下文分析**: 用 read_file 获取更多代码上下文
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2. **通用测试**: 使用 universal_vuln_test 或 universal_code_test
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3. **沙箱测试**: 对高危漏洞用沙箱进行安全测试
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## 重要原则
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1. **质量优先** - 宁可漏报也不要误报太多
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2. **深入理解** - 理解代码逻辑，不要表面判断
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3. **证据支撑** - 判定要有依据
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4. **安全第一** - 沙箱测试要谨慎
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5. **🔥 PoC 生成** - 对于 confirmed 和 likely 的漏洞，**必须**生成完整的 PoC:
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- poc.description: 简要描述这个 PoC 的作用
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- poc.steps: 详细的复现步骤列表
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- poc.payload: **完整的**利用代码或命令，例如:
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- Web漏洞: 完整URL如 `http://target/path?param=<payload>`
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- 命令注入: 完整的 curl 命令或 HTTP 请求
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- SQL注入: 完整的利用语句或请求
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- 代码执行: 可直接运行的利用脚本
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- ⚠️ payload 字段必须是**可直接复制执行**的完整利用代码，不要只写参数值
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现在开始验证漏洞发现！"""
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@dataclass
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class VerificationStep:
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"""验证步骤"""
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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 VerificationAgent(BaseAgent):
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"""
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漏洞验证 Agent - LLM 驱动版
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LLM 全程参与，自主决定：
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1. 如何验证每个漏洞
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2. 使用什么工具
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3. 判断真假
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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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# 组合增强的系统提示词
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full_system_prompt = f"{VERIFICATION_SYSTEM_PROMPT}\n\n{CORE_SECURITY_PRINCIPLES}\n\n{VULNERABILITY_PRIORITIES}"
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config = AgentConfig(
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name="Verification",
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agent_type=AgentType.VERIFICATION,
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pattern=AgentPattern.REACT,
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max_iterations=25,
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system_prompt=full_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[VerificationStep] = []
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def _parse_llm_response(self, response: str) -> VerificationStep:
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"""解析 LLM 响应 - 增强版，更健壮地提取思考内容"""
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step = VerificationStep(thought="")
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# 🔥 首先尝试提取明确的 Thought 标记
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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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# 🔥 如果没有提取到 thought，使用 Final Answer 前的内容作为思考
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if not step.thought:
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before_final = response[:response.find('Final Answer:')].strip()
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if before_final:
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before_final = re.sub(r'^Thought:\s*', '', before_final)
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step.thought = before_final[:500] if len(before_final) > 500 else before_final
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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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# 🔥 如果没有提取到 thought，提取 Action 之前的内容作为思考
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if not step.thought:
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action_pos = response.find('Action:')
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if action_pos > 0:
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before_action = response[:action_pos].strip()
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before_action = re.sub(r'^Thought:\s*', '', before_action)
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if before_action:
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step.thought = before_action[:500] if len(before_action) > 500 else before_action
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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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# 🔥 最后的 fallback：如果整个响应没有任何标记，整体作为思考
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if not step.thought and not step.action and not step.is_final:
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if response.strip():
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step.thought = response.strip()[:500]
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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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执行漏洞验证 - 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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previous_results = input_data.get("previous_results", {})
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config = input_data.get("config", {})
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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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# 收集所有待验证的发现
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findings_to_verify = []
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# 🔥 优先从交接信息获取发现
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if self._incoming_handoff and self._incoming_handoff.key_findings:
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findings_to_verify = self._incoming_handoff.key_findings.copy()
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logger.info(f"[Verification] 从交接信息获取 {len(findings_to_verify)} 个发现")
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else:
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# 🔥 修复：处理 Orchestrator 传递的多种数据格式
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# 格式1: Orchestrator 直接传递 {"findings": [...]}
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if isinstance(previous_results, dict) and "findings" in previous_results:
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direct_findings = previous_results.get("findings", [])
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if isinstance(direct_findings, list):
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for f in direct_findings:
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if isinstance(f, dict):
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# 🔥 Always verify Critical/High findings to generate PoC, even if Analysis sets needs_verification=False
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severity = str(f.get("severity", "")).lower()
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needs_verify = f.get("needs_verification", True)
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if needs_verify or severity in ["critical", "high"]:
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findings_to_verify.append(f)
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logger.info(f"[Verification] 从 previous_results.findings 获取 {len(findings_to_verify)} 个发现")
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# 格式2: 传统格式 {"phase_name": {"data": {"findings": [...]}}}
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if not findings_to_verify:
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for phase_name, result in previous_results.items():
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if phase_name == "findings":
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continue # 已处理
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if isinstance(result, dict):
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data = result.get("data", {})
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else:
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data = result.data if hasattr(result, 'data') else {}
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if isinstance(data, dict):
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phase_findings = data.get("findings", [])
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for f in phase_findings:
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if isinstance(f, dict):
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severity = str(f.get("severity", "")).lower()
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needs_verify = f.get("needs_verification", True)
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if needs_verify or severity in ["critical", "high"]:
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findings_to_verify.append(f)
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if findings_to_verify:
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logger.info(f"[Verification] 从传统格式获取 {len(findings_to_verify)} 个发现")
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# 🔥 如果仍然没有发现，尝试从 input_data 的其他字段提取
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if not findings_to_verify:
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# 尝试从 task 或 task_context 中提取描述的漏洞
|
||
if task and ("发现" in task or "漏洞" in task or "findings" in task.lower()):
|
||
logger.warning(f"[Verification] 无法从结构化数据获取发现，任务描述: {task[:200]}")
|
||
# 创建一个提示 LLM 从任务描述中理解漏洞的特殊处理
|
||
await self.emit_event("warning", f"无法从结构化数据获取发现列表，将基于任务描述进行验证")
|
||
|
||
# 去重
|
||
findings_to_verify = self._deduplicate(findings_to_verify)
|
||
|
||
# 🔥 FIX: 优先处理有明确文件路径的发现，将没有文件路径的发现放到后面
|
||
# 这确保 Analysis 的具体发现优先于 Recon 的泛化描述
|
||
def has_valid_file_path(finding: Dict) -> bool:
|
||
file_path = finding.get("file_path", "")
|
||
return bool(file_path and file_path.strip() and file_path.lower() not in ["unknown", "n/a", ""])
|
||
|
||
findings_with_path = [f for f in findings_to_verify if has_valid_file_path(f)]
|
||
findings_without_path = [f for f in findings_to_verify if not has_valid_file_path(f)]
|
||
|
||
# 合并：有路径的在前，没路径的在后
|
||
findings_to_verify = findings_with_path + findings_without_path
|
||
|
||
if findings_with_path:
|
||
logger.info(f"[Verification] 优先处理 {len(findings_with_path)} 个有明确文件路径的发现")
|
||
if findings_without_path:
|
||
logger.info(f"[Verification] 还有 {len(findings_without_path)} 个发现需要自行定位文件")
|
||
|
||
if not findings_to_verify:
|
||
logger.warning(f"[Verification] 没有需要验证的发现! previous_results keys: {list(previous_results.keys()) if isinstance(previous_results, dict) else 'not dict'}")
|
||
await self.emit_event("warning", "没有需要验证的发现 - 可能是数据格式问题")
|
||
return AgentResult(
|
||
success=True,
|
||
data={"findings": [], "verified_count": 0, "note": "未收到待验证的发现"},
|
||
)
|
||
|
||
# 限制数量
|
||
findings_to_verify = findings_to_verify[:20]
|
||
|
||
await self.emit_event(
|
||
"info",
|
||
f"开始验证 {len(findings_to_verify)} 个发现"
|
||
)
|
||
|
||
# 🔥 记录工作开始
|
||
self.record_work(f"开始验证 {len(findings_to_verify)} 个漏洞发现")
|
||
|
||
# 🔥 构建包含交接上下文的初始消息
|
||
handoff_context = self.get_handoff_context()
|
||
|
||
findings_summary = []
|
||
for i, f in enumerate(findings_to_verify):
|
||
# 🔥 FIX: 正确处理 file_path 格式，可能包含行号 (如 "app.py:36")
|
||
file_path = f.get('file_path', 'unknown')
|
||
line_start = f.get('line_start', 0)
|
||
|
||
# 如果 file_path 已包含行号，提取出来
|
||
if isinstance(file_path, str) and ':' in file_path:
|
||
parts = file_path.split(':', 1)
|
||
if len(parts) == 2 and parts[1].split()[0].isdigit():
|
||
file_path = parts[0]
|
||
try:
|
||
line_start = int(parts[1].split()[0])
|
||
except ValueError:
|
||
pass
|
||
|
||
findings_summary.append(f"""
|
||
### 发现 {i+1}: {f.get('title', 'Unknown')}
|
||
- 类型: {f.get('vulnerability_type', 'unknown')}
|
||
- 严重度: {f.get('severity', 'medium')}
|
||
- 文件: {file_path} (行 {line_start})
|
||
- 代码:
|
||
```
|
||
{f.get('code_snippet', 'N/A')[:500]}
|
||
```
|
||
- 描述: {f.get('description', 'N/A')[:300]}
|
||
""")
|
||
|
||
initial_message = f"""请验证以下 {len(findings_to_verify)} 个安全发现。
|
||
|
||
{handoff_context if handoff_context else ''}
|
||
|
||
## 待验证发现
|
||
{''.join(findings_summary)}
|
||
|
||
## ⚠️ 重要验证指南
|
||
1. **直接使用上面列出的文件路径** - 不要猜测或搜索其他路径
|
||
2. **如果文件路径包含冒号和行号** (如 "app.py:36"), 请提取文件名 "app.py" 并使用 read_file 读取
|
||
3. **先读取文件内容，再判断漏洞是否存在**
|
||
4. **不要假设文件在子目录中** - 使用发现中提供的精确路径
|
||
|
||
## 验证要求
|
||
- 验证级别: {config.get('verification_level', 'standard')}
|
||
|
||
## 可用工具
|
||
{self.get_tools_description()}
|
||
|
||
请开始验证。对于每个发现：
|
||
1. 首先使用 read_file 读取发现中指定的文件（使用精确路径）
|
||
2. 分析代码上下文
|
||
3. 判断是否为真实漏洞
|
||
{f"特别注意 Analysis Agent 提到的关注点。" if handoff_context else ""}"""
|
||
|
||
# 初始化对话历史
|
||
self._conversation_history = [
|
||
{"role": "system", "content": self.config.system_prompt},
|
||
{"role": "user", "content": initial_message},
|
||
]
|
||
|
||
self._steps = []
|
||
final_result = None
|
||
|
||
await self.emit_thinking("🔐 Verification Agent 启动，LLM 开始自主验证漏洞...")
|
||
|
||
try:
|
||
for iteration in range(self.config.max_iterations):
|
||
if self.is_cancelled:
|
||
break
|
||
|
||
self._iteration = iteration + 1
|
||
|
||
# 🔥 再次检查取消标志（在LLM调用之前）
|
||
if self.is_cancelled:
|
||
await self.emit_thinking("🛑 任务已取消，停止执行")
|
||
break
|
||
|
||
# 调用 LLM 进行思考和决策（流式输出）
|
||
try:
|
||
llm_output, tokens_this_round = await self.stream_llm_call(
|
||
self._conversation_history,
|
||
temperature=0.1,
|
||
max_tokens=4096, # 🔥 增加到 4096，避免截断
|
||
)
|
||
except asyncio.CancelledError:
|
||
logger.info(f"[{self.name}] LLM call cancelled")
|
||
break
|
||
|
||
self._total_tokens += tokens_this_round
|
||
|
||
# 🔥 Handle empty LLM response to prevent loops
|
||
if not llm_output or not llm_output.strip():
|
||
logger.warning(f"[{self.name}] Empty LLM response in iteration {self._iteration}")
|
||
await self.emit_llm_decision("收到空响应", "LLM 返回内容为空，尝试重试通过提示")
|
||
self._conversation_history.append({
|
||
"role": "user",
|
||
"content": "Received empty response. Please output your Thought and Action.",
|
||
})
|
||
continue
|
||
|
||
# 解析 LLM 响应
|
||
step = self._parse_llm_response(llm_output)
|
||
self._steps.append(step)
|
||
|
||
# 🔥 发射 LLM 思考内容事件 - 展示验证的思考过程
|
||
if step.thought:
|
||
await self.emit_llm_thought(step.thought, iteration + 1)
|
||
|
||
# 添加 LLM 响应到历史
|
||
self._conversation_history.append({
|
||
"role": "assistant",
|
||
"content": llm_output,
|
||
})
|
||
|
||
# 检查是否完成
|
||
if step.is_final:
|
||
await self.emit_llm_decision("完成漏洞验证", "LLM 判断验证已充分")
|
||
final_result = step.final_answer
|
||
|
||
# 🔥 记录洞察和工作
|
||
if final_result and "findings" in final_result:
|
||
verified_count = len([f for f in final_result["findings"] if f.get("is_verified")])
|
||
fp_count = len([f for f in final_result["findings"] if f.get("verdict") == "false_positive"])
|
||
self.add_insight(f"验证了 {len(final_result['findings'])} 个发现，{verified_count} 个确认，{fp_count} 个误报")
|
||
self.record_work(f"完成漏洞验证: {verified_count} 个确认, {fp_count} 个误报")
|
||
|
||
await self.emit_llm_complete(
|
||
f"验证完成",
|
||
self._total_tokens
|
||
)
|
||
break
|
||
|
||
# 执行工具
|
||
if step.action:
|
||
# 🔥 发射 LLM 动作决策事件
|
||
await self.emit_llm_action(step.action, step.action_input or {})
|
||
|
||
observation = await self.execute_tool(
|
||
step.action,
|
||
step.action_input or {}
|
||
)
|
||
|
||
step.observation = observation
|
||
|
||
# 🔥 发射 LLM 观察事件
|
||
await self.emit_llm_observation(observation)
|
||
|
||
# 添加观察结果到历史
|
||
self._conversation_history.append({
|
||
"role": "user",
|
||
"content": f"Observation:\n{observation}",
|
||
})
|
||
else:
|
||
# LLM 没有选择工具，提示它继续
|
||
await self.emit_llm_decision("继续验证", "LLM 需要更多验证")
|
||
self._conversation_history.append({
|
||
"role": "user",
|
||
"content": "请继续验证。如果验证完成，输出 Final Answer 汇总所有验证结果。",
|
||
})
|
||
|
||
# 处理结果
|
||
duration_ms = int((time.time() - start_time) * 1000)
|
||
|
||
# 🔥 如果被取消，返回取消结果
|
||
if self.is_cancelled:
|
||
await self.emit_event(
|
||
"info",
|
||
f"🛑 Verification Agent 已取消: {self._iteration} 轮迭代"
|
||
)
|
||
return AgentResult(
|
||
success=False,
|
||
error="任务已取消",
|
||
data={"findings": findings_to_verify},
|
||
iterations=self._iteration,
|
||
tool_calls=self._tool_calls,
|
||
tokens_used=self._total_tokens,
|
||
duration_ms=duration_ms,
|
||
)
|
||
|
||
# 处理最终结果
|
||
verified_findings = []
|
||
|
||
# 🔥 Robustness: If LLM returns empty findings but we had input, fallback to original
|
||
llm_findings = []
|
||
if final_result and "findings" in final_result:
|
||
llm_findings = final_result["findings"]
|
||
|
||
if not llm_findings and findings_to_verify:
|
||
logger.warning(f"[{self.name}] LLM returned empty findings despite {len(findings_to_verify)} inputs. Falling back to originals.")
|
||
# Fallback to logic below (else branch)
|
||
final_result = None
|
||
|
||
if final_result and "findings" in final_result:
|
||
for f in final_result["findings"]:
|
||
verified = {
|
||
**f,
|
||
"is_verified": f.get("verdict") == "confirmed" or (
|
||
f.get("verdict") == "likely" and f.get("confidence", 0) >= 0.8
|
||
),
|
||
"verified_at": datetime.now(timezone.utc).isoformat() if f.get("verdict") in ["confirmed", "likely"] else None,
|
||
}
|
||
|
||
# 添加修复建议
|
||
if not verified.get("recommendation"):
|
||
verified["recommendation"] = self._get_recommendation(f.get("vulnerability_type", ""))
|
||
|
||
verified_findings.append(verified)
|
||
else:
|
||
# 如果没有最终结果，使用原始发现
|
||
for f in findings_to_verify:
|
||
verified_findings.append({
|
||
**f,
|
||
"verdict": "uncertain",
|
||
"confidence": 0.5,
|
||
"is_verified": False,
|
||
})
|
||
|
||
# 统计
|
||
confirmed_count = len([f for f in verified_findings if f.get("verdict") == "confirmed"])
|
||
likely_count = len([f for f in verified_findings if f.get("verdict") == "likely"])
|
||
false_positive_count = len([f for f in verified_findings if f.get("verdict") == "false_positive"])
|
||
|
||
await self.emit_event(
|
||
"info",
|
||
f"Verification Agent 完成: {confirmed_count} 确认, {likely_count} 可能, {false_positive_count} 误报"
|
||
)
|
||
|
||
# 🔥 CRITICAL: Log final findings count before returning
|
||
logger.info(f"[{self.name}] Returning {len(verified_findings)} verified findings")
|
||
|
||
return AgentResult(
|
||
success=True,
|
||
data={
|
||
"findings": verified_findings,
|
||
"verified_count": confirmed_count,
|
||
"likely_count": likely_count,
|
||
"false_positive_count": false_positive_count,
|
||
},
|
||
iterations=self._iteration,
|
||
tool_calls=self._tool_calls,
|
||
tokens_used=self._total_tokens,
|
||
duration_ms=duration_ms,
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"Verification Agent failed: {e}", exc_info=True)
|
||
return AgentResult(success=False, error=str(e))
|
||
|
||
def _get_recommendation(self, vuln_type: str) -> str:
|
||
"""获取修复建议"""
|
||
recommendations = {
|
||
"sql_injection": "使用参数化查询或 ORM，避免字符串拼接构造 SQL",
|
||
"xss": "对用户输入进行 HTML 转义，使用 CSP，避免 innerHTML",
|
||
"command_injection": "避免使用 shell=True，使用参数列表传递命令",
|
||
"path_traversal": "验证和规范化路径，使用白名单，避免直接使用用户输入",
|
||
"ssrf": "验证和限制目标 URL，使用白名单，禁止内网访问",
|
||
"deserialization": "避免反序列化不可信数据，使用 JSON 替代 pickle/yaml",
|
||
"hardcoded_secret": "使用环境变量或密钥管理服务存储敏感信息",
|
||
"weak_crypto": "使用强加密算法（AES-256, SHA-256+），避免 MD5/SHA1",
|
||
}
|
||
return recommendations.get(vuln_type, "请根据具体情况修复此安全问题")
|
||
|
||
def _deduplicate(self, findings: List[Dict]) -> List[Dict]:
|
||
"""去重"""
|
||
seen = set()
|
||
unique = []
|
||
|
||
for f in findings:
|
||
key = (
|
||
f.get("file_path", ""),
|
||
f.get("line_start", 0),
|
||
f.get("vulnerability_type", ""),
|
||
)
|
||
|
||
if key not in seen:
|
||
seen.add(key)
|
||
unique.append(f)
|
||
|
||
return unique
|
||
|
||
def get_conversation_history(self) -> List[Dict[str, str]]:
|
||
"""获取对话历史"""
|
||
return self._conversation_history
|
||
|
||
def get_steps(self) -> List[VerificationStep]:
|
||
"""获取执行步骤"""
|
||
return self._steps
|