Bypassing AI control protocols via the Agent-as-a-Proxy attack
Bypassing AI Control Protocols via Agent-as-a-Proxy Attacks
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Based on 论文全文 （AI 生成）; refer to the original
The researchers propose the Agent-as-a-Proxy attack, which uses an agent that has been indirectly prompt-injected as a vehicle to deliver adversarial strings to the monitoring model, thereby bypassing both the agent and the monitor. The accompanying Parallel-GCG algorithm jointly optimizes adversarial strings across multiple insertion points, including reasoning traces, tool arguments, and execution logs. On the AgentDojo benchmark, this method achieves an attack success rate of over 90% against AlignmentCheck, and is also effective against PromptGuard 2 + AlignmentCheck and Extract-and-Evaluate. The authors state that hybrid monitoring (CoT plus tool calls), although more robust against static attacks, is easier to bypass with adaptive attacks than monitoring only the CoT; GPT-4o mini and Llama 3.1 70B can bypass Qwen2.5 72B monitoring, showing that bypassing monitoring does not require a capability gap.
- AI agent 处理来自网页、邮件等不可信数据时易受 indirect prompt injection (IPI) 攻击。当前防御依赖监控协议，同时检查 agent 的 Chain-of-Thought 与工具调用是否偏离用户意图，作者要检验这类监控是否真的可靠。
- 提出 Agent-as-a-Proxy 攻击：让 IPI 字符串不只误导 agent，而是把 agent 当作向监控模型投递对抗字符串的载体，使其在 CoT 或工具参数中重复该字符串。为此设计 Parallel-GCG，在推理轨迹、函数参数、执行日志等多个插入点同时优化同一对抗字符串。
- 在 AgentDojo 上，对 AlignmentCheck、PromptGuard 2 + AlignmentCheck、Extract-and-Evaluate 三种监控协议均取得高攻击成功率，部分超过 90%。混合监控对静态攻击更稳，但对自适应攻击比仅监控 CoT 更脆弱；GPT-4o mini 和 Llama 3.1 70B 可绕过 Qwen2.5 72B 监控，说明不需要能力更强的 agent。Parallel-GCG 优于普通 GCG。
- 对抗字符串困惑度较高，可能被分类器或人检测；攻击针对白盒监控，未来可扩展到黑盒监控，例如借助 proxy-guided black-box attack。作者也指出攻击字符串在端到端执行中偶尔无法被 agent 完整复现。
The paper measures an attack success rate of over 90% against AlignmentCheck on AgentDojo, providing a reproducible baseline for evaluating the robustness of monitoring-based Agent defenses.