BackdoorAgent is a novel framework developed to address the growing concern of backdoor threats in large language model (LLM) agents. LLM agents, characterized by their multi-step workflows involving planning, memory, and tool use, possess an expanded attack surface compared to traditional LLMs. Existing research often examines individual attack vectors in isolation, leading to a fragmented understanding of how backdoor triggers interact and propagate across different stages of an agent's operation. BackdoorAgent fills this critical gap by offering a unified, agent-centric perspective. It structures the attack surface into three distinct functional stages—planning, memory, and tool-use attacks—and instruments agent execution to facilitate systematic analysis of trigger activation and propagation. This framework is crucial for researchers and ML engineers focused on enhancing the security and robustness of autonomous LLM agents in complex applications.
BackdoorAgent is a research framework for understanding how hidden malicious instructions, called backdoors, can infect and spread through complex AI systems known as LLM agents. It breaks down an agent's actions into stages like planning and memory to see exactly where and how these backdoors take hold and affect the agent's behavior.
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