A Survey of Fuzzing Techniques for Large Language Model Agents: Challenges, Methods, and Future Directions

Abstract

With the rapid development of large language models (LLMs), LLM-based agents are evolving from single-turn text generators into autonomous decision-making systems capable of task planning, tool invocation, memory retrieval, and environmental interaction. However, the openness of agent environments and the nondeterminism of model-driven decision logic introduce security and reliability risks that are difficult to capture with conventional software testing. Fuzzing, as an automated and scalable vulnerability discovery technique, provides a promising approach for assessing the robustness of LLM agent systems. This paper presents a systematic survey of fuzzing techniques for LLM agents. First, we analyze the distinctive architecture of LLM agents and summarize three representative threat categories: indirect prompt injection, planning hallucination, and tool abuse. Second, we review existing fuzzing methods from three perspectives: test-case generation and mutation, environment simulation and interaction monitoring, and test-oracle construction. Particular attention is paid to LLM-enhanced fuzzers that exploit the semantic understanding and generation capabilities of LLMs to improve mutation quality and attack effectiveness. Finally, we connect the main technical challenges with an actionable research roadmap, including semantic coverage, cost-aware execution, reproducible oracles, multi-agent fuzzing, gray-box guidance, and automated repair.