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The paper introduces a novel economic Denial-of-Service (DoS) attack targeting LLM agents by exploiting the agent-tool communication loop in multi-turn interactions. The attack leverages a modified tool server to subtly steer agents into prolonged, verbose tool-calling sequences while preserving task correctness, thus bypassing conventional validation checks. Experiments on six LLMs demonstrate significant resource amplification, with token usage increasing to over 60,000, costs inflating by up to 658x, and GPU KV cache occupancy rising substantially, highlighting the vulnerability of the agent-tool interface.
LLM agents are shockingly vulnerable to stealthy economic DoS attacks that inflate costs by up to 658x, even while returning correct answers.
The agent-tool communication loop is a critical attack surface in modern Large Language Model (LLM) agents. Existing Denial-of-Service (DoS) attacks, primarily triggered via user prompts or injected retrieval-augmented generation (RAG) context, are ineffective for this new paradigm. They are fundamentally single-turn and often lack a task-oriented approach, making them conspicuous in goal-oriented workflows and unable to exploit the compounding costs of multi-turn agent-tool interactions. We introduce a stealthy, multi-turn economic DoS attack that operates at the tool layer under the guise of a correctly completed task. Our method adjusts text-visible fields and a template-governed return policy in a benign, Model Context Protocol (MCP)-compatible tool server, optimizing these edits with a Monte Carlo Tree Search (MCTS) optimizer. These adjustments leave function signatures unchanged and preserve the final payload, steering the agent into prolonged, verbose tool-calling sequences using text-only notices. This compounds costs across turns, escaping single-turn caps while keeping the final answer correct to evade validation. Across six LLMs on the ToolBench and BFCL benchmarks, our attack expands tasks into trajectories exceeding 60,000 tokens, inflates costs by up to 658x, and raises energy by 100-560x. It drives GPU KV cache occupancy from<1% to 35-74% and cuts co-running throughput by approximately 50%. Because the server remains protocol-compatible and task outcomes are correct, conventional checks fail. These results elevate the agent-tool interface to a first-class security frontier, demanding a paradigm shift from validating final answers to monitoring the economic and computational cost of the entire agentic process.
