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Targeted Bit-Flip Attacks on LLM-Based Agents
arXiv β CS AI|Jialai Wang, Ya Wen, Zhongmou Liu, Yuxiao Wu, Bingyi He, Zongpeng Li, Ee-Chien Chang|
π€AI Summary
Researchers have introduced Flip-Agent, the first targeted bit-flip attack framework specifically designed to exploit LLM-based agents by manipulating hardware faults. The attack can manipulate both final outputs and tool invocations in multi-stage AI agent pipelines, revealing critical security vulnerabilities in these systems.
Key Takeaways
- βFlip-Agent is the first targeted bit-flip attack framework designed specifically for LLM-based agents.
- βThe attack exploits hardware faults to manipulate model parameters in multi-stage AI agent systems.
- βUnlike previous attacks on single-step models, this targets the complex pipelines and external tools used by LLM agents.
- βExperiments show Flip-Agent significantly outperforms existing targeted bit-flip attacks on real-world agent tasks.
- βThe research reveals critical security vulnerabilities in LLM-based agent systems that were previously unexplored.
#llm-security#bit-flip-attacks#ai-agents#hardware-exploits#cybersecurity#vulnerability#flip-agent#model-manipulation
Read Original βvia arXiv β CS AI
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