Your LLM Agent Can Leak Your Data: Data Exfiltration via Backdoored Tool Use
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 exfiltratio...
| Authors | Wuyang Zhang, Shichao Pei |
|---|---|
| Primary category | cs.CR |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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