后门工具调用可让LLM代理偷偷窃取用户数据
Your LLM Agent Can Leak Your Data: Data Exfiltration via Backdoored Tool Use
- 用语义触发器在微调的LLM代理中植入后门
- 触发后通过伪装的工具调用窃取用户上下文信息
- 多轮交互放大泄露风险，适合安全研究者关注
工具调用的大语言模型（LLM）代理正被广泛用于处理敏感任务，依赖工具调用实现信息检索、外部API访问和会话记忆管理。尽管已有研究关注多种威胁，但针对后门代理引发系统性数据外泄的风险仍鲜有探讨。本文提出Back-Reveal攻击：通过在微调的LLM代理中嵌入语义触发器，当触发时，代理会调用记忆访问工具获取用户上下文，并通过伪装的检索工具调用将数据外泄。实验表明，多轮交互会加剧泄露影响，攻击者可通过控制检索响应逐步引导代理行为与用户互动，实现持续且累积的信息泄露。结果揭示了具备工具访问能力的LLM代理存在严重漏洞，亟需防范以数据外泄为导向的后门攻击。
原文摘要 · Abstract (English)
Tool-use large language model (LLM) agents are increasingly deployed to support sensitive workflows, relying on tool calls for retrieval, external API access, and session memory management. While prior research has examined various threats, the risk of systematic data exfiltration by backdoored agents remains underexplored. In this work, we present Back-Reveal, a data exfiltration attack that embeds semantic triggers into fine-tuned LLM agents. When triggered, the backdoored agent invokes memory-access tool calls to retrieve stored user context and exfiltrates it via disguised retrieval tool calls. We further demonstrate that multi-turn interaction amplifies the impact of data exfiltration, as attacker-controlled retrieval responses can subtly steer subsequent agent behavior and user interactions, enabling sustained and cumulative information leakage over time. Our experimental results expose a critical vulnerability in LLM agents with tool access and highlight the need for defenses against exfiltration-oriented backdoors.
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