LeftoverLocals: Listening to LLM Responses Through Leaked GPU Local Memory
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Abstract (arXiv)
This paper describes LeftoverLocals: a vulnerability that allows data recovery from GPU memory created by another process on Apple, Qualcomm, and AMD GPUs. LeftoverLocals impacts the security posture of GPU applications, with particular significance to LLMs and ML models that run on impacted GPUs. By recovering local memory, an optimized GPU memory region, we built a PoC where an attacker can listen into another user's interactive LLM session (e.g., llama.cpp) across process or container boundaries.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Jan 2024.
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Citing papers per year: 2024: 2; 2025: 1; 2026: 2.
5 papers.
- 2025Shadow in the Cache: Unveiling and Mitigating Privacy Risks of KV-cache in LLM Inferencecited by 21
- 2024A First Look At Efficient And Secure On-Device LLM Inference Against KV Leakagecited by 8
- 2024SoK: Blockchain-Based Decentralized AI (DeAI)cited by 6
- 2026Good-Enough LLM Obfuscation (GELO)cited by 0
- 2026When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AIcited by 0
- 2026When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI4 May 2026
- 2026Good-Enough LLM Obfuscation (GELO)5 Mar 2026
- 2025Shadow in the Cache: Unveiling and Mitigating Privacy Risks of KV-cache in LLM Inference13 Aug 2025
- 2024SoK: Blockchain-Based Decentralized AI (DeAI)26 Nov 2024
- 2024A First Look At Efficient And Secure On-Device LLM Inference Against KV Leakage6 Sep 2024