Targeted Bit-Flip Attacks on LLM-Based Agents

2026-03-07

AI Summary

This work addresses a critical gap in existing research by investigating the vulnerability of large language model (LLM) agents to hardware-level bit-flip attacks in multi-stage reasoning and tool-use scenarios. We propose Flip-Agent, a novel framework that extends targeted bit-flip attacks to LLM agent systems for the first time, leveraging hardware fault injection techniques to precisely manipulate agent behavior at the parameter level. Our approach enables fine-grained control over the agent’s reasoning trajectory and tool invocation decisions. Experimental results on realistic multi-stage tasks demonstrate that Flip-Agent significantly outperforms existing attack methods, successfully steering both the agent’s outputs and its tool usage patterns. These findings reveal a fundamental security weakness in LLM agents when deployed in complex, real-world environments.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Targeted bit-flip attacks (BFAs) exploit hardware faults to manipulate model parameters, posing a significant security threat. While prior work targets single-step inference models (e.g., image classifiers), LLM-based agents with multi-stage pipelines and external tools present new attack surfaces, which remain unexplored. This work introduces Flip-Agent, the first targeted BFA framework for LLM-based agents, manipulating both final outputs and tool invocations. Our experiments show that Flip-Agent significantly outperforms existing targeted BFAs on real-world agent tasks, revealing a critical vulnerability in LLM-based agent systems.

Problem

• Targeted Bit-Flip Attacks
• LLM-based Agents
• Hardware Faults
• Security Threat
• Tool Invocation

Innovation

• bit-flip attack
• LLM-based agents
• hardware fault injection
• tool invocation manipulation
• targeted attack