Autonomy Comes with Costs: Detecting Denial-of-Service Vulnerabilities Caused by Resource Abusing in LLM-based Agents

LLM-based agents have recently attracted significant attention. By leveraging the semantic understanding capabilities of large language models (LLMs), these agents can autonomously perform complex tasks according to user requests, such as downloading files and summarizing content. However, the lack of comprehensive resource governance renders them susceptible to abuse, potentially leading to resource exhaustion and denial-of-service (DoS) conditions.

In this work, we present the first systematic security study of resource management in LLM-based agents. We identify three representative patterns of resource lifecycle management, each of which enables distinct avenues for DoS exploitation. Building on these insights, we propose AgentDoS, a novel directed grey-box fuzzing framework designed to detect DoS vulnerabilities arising from resource exhaustion. AgentDoS first analyzes the resource lifecycle within the agent and then leverages an LLM to generate functionality-specific seed prompts in natural language that drive the agent toward excessive resource consumption. We evaluated AgentDoS on 20 widely used open-source LLM-based agents and discovered 36 zero-day vulnerabilities affecting 16 agents, 15 of which have over 10,000 stars on GitHub. To date, 15 CVE IDs have been assigned for these vulnerabilities.