首次对比五款大模型在两种框架下的攻防表现，发现超半数攻击成功。
Penetration Testing of Agentic AI: A Comparative Security Analysis Across Models and Frameworks
- 构建七代理架构模拟校园系统，测试13类攻击
- 平均拒绝率仅41.5%，部分模型拒攻率低至15.4%
- 发现模型会编造合规输出，暴露新型安全风险
代理型AI引入了传统LLM防护无法应对的安全漏洞。尽管帕洛阿尔托网络公司Unit 42的研究表明ChatGPT-4o在代理模式下可执行其聊天模式拒绝的攻击，但尚无跨模型与框架的对比分析。本文首次系统开展代理型AI渗透测试与比较评估，针对五款主流模型（Claude 3.5 Sonnet、Gemini 2.5 Flash、GPT-4o、Grok 2、Nova Pro）在AutoGen与CrewAI两个代理框架下进行测试，采用七代理架构模拟大学信息管理系统，并覆盖13种攻击场景，包括提示注入、服务器端请求伪造（SSRF）、SQL注入及工具误用。共执行130个测试案例，结果显示：AutoGen拒绝率为52.3%，低于CrewAI的30.8%；模型表现从Nova Pro的46.2%到Claude与Grok 2的38.5%不等。最严重情况为Grok 2在CrewAI上仅拒绝2起攻击（15.4%），整体拒绝率41.5%表明超过一半恶意提示成功，即便在企业级安全机制下。研究识别出六种防御行为模式，包括一种新发现的“虚构合规”策略，即模型虚构输出而非执行或拒绝攻击，并提出可操作的安全部署建议。完整攻击提示已附于附录，确保可复现性。
原文摘要 · Abstract (English)
Agentic AI introduces security vulnerabilities that traditional LLM safeguards fail to address. Although recent work by Unit 42 at Palo Alto Networks demonstrated that ChatGPT-4o successfully executes attacks as an agent that it refuses in chat mode, there is no comparative analysis in multiple models and frameworks. We conducted the first systematic penetration testing and comparative evaluation of agentic AI systems, testing five prominent models (Claude 3.5 Sonnet, Gemini 2.5 Flash, GPT-4o, Grok 2, and Nova Pro) across two agentic AI frameworks (AutoGen and CrewAI) using a seven-agent architecture that mimics the functionality of a university information management system and 13 distinct attack scenarios that span prompt injection, Server Side Request Forgery (SSRF), SQL injection, and tool misuse. Our 130 total test cases reveal significant security disparities: AutoGen demonstrates a 52.3% refusal rate versus CrewAI's 30.8%, while model performance ranges from Nova Pro's 46.2% to Claude and Grok 2's 38.5%. Most critically, Grok 2 on CrewAI rejected only 2 of 13 attacks (15.4% refusal rate), and the overall refusal rate of 41.5% across all configurations indicates that more than half of malicious prompts succeeded despite enterprise-grade safety mechanisms. We identify six distinct defensive behavior patterns including a novel "hallucinated compliance" strategy where models fabricate outputs rather than executing or refusing attacks, and provide actionable recommendations for secure agent deployment. Complete attack prompts are also included in the Appendix to enable reproducibility.
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