AWE用智能代理框架提升网页渗透测试的精准与效率
AWE: Adaptive Agents for Dynamic Web Penetration Testing
- 构建轻量级多代理系统，结合记忆与浏览器验证实现定向漏洞探测
- 在XBOW基准上对注入类漏洞成功率超87%（XSS）和66.7%（盲注）
- 适合关注自动化安全测试精度与可复现性的研发及安全团队
现代网页应用通过AI辅助开发与快速无代码部署加速演进，但现有安全工具适应性不足。传统扫描器难以应对新场景，而基于大模型的渗透测试常因无约束探索导致成本高、行为不稳定、结果不可复现。本文提出AWE——一种内存增强的多代理框架，将结构化漏洞分析流程嵌入轻量级大模型调度层。不同于通用代理，AWE通过上下文感知的载荷变异与生成、持久化记忆及浏览器验证，实现确定性、以利用为导向的结果。在104个挑战的XBOW基准测试中，对注入类漏洞表现显著：XSS成功率87%（较MAPTA提升30.5%），盲注成功率66.7%（提升33.3%），且速度更快、成本更低、令牌使用更高效。尽管采用中端模型Claude Sonnet 4，仍优于使用GPT-5的MAPTA。MAPTA覆盖更广，凸显专用与通用架构的互补优势。结果表明，架构设计与模型推理能力同等重要：将大模型融入有原则的漏洞感知流程，可大幅提升注入类漏洞攻击的准确率、效率与可确定性。AWE源码已开源。
原文摘要 · Abstract (English)
Modern web applications are increasingly produced through AI-assisted development and rapid no-code deployment pipelines, widening the gap between accelerating software velocity and the limited adaptability of existing security tooling. Pattern-driven scanners fail to reason about novel contexts, while emerging LLM-based penetration testers rely on unconstrained exploration, yielding high cost, unstable behavior, and poor reproducibility. We introduce AWE, a memory-augmented multi-agent framework for autonomous web penetration testing that embeds structured, vulnerability-specific analysis pipelines within a lightweight LLM orchestration layer. Unlike general-purpose agents, AWE couples context aware payload mutations and generations with persistent memory and browser-backed verification to produce deterministic, exploitation-driven results. Evaluated on the 104-challenge XBOW benchmark, AWE achieves substantial gains on injection-class vulnerabilities - 87% XSS success (+30.5% over MAPTA) and 66.7% blind SQL injection success (+33.3%) - while being much faster, cheaper, and more token-efficient than MAPTA, despite using a midtier model (Claude Sonnet 4) versus MAPTA's GPT-5. MAPTA retains higher overall coverage due to broader exploratory capabilities, underscoring the complementary strengths of specialized and general-purpose architectures. Our results demonstrate that architecture matters as much as model reasoning capabilities: integrating LLMs into principled, vulnerability-aware pipelines yields substantial gains in accuracy, efficiency, and determinism for injection-class exploits. The source code for AWE is available at: https://github.com/stuxlabs/AWE
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