Overthinking Loops in Agents: A Structural Risk via MCP Tools

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Authors: Yohan Lee, Jisoo Jang, Seoyeon Choi, Sangyeop Kim, Seungtaek Choi
Cited by: 0

Abstract

Tool-using LLM agents increasingly coordinate real workloads by selecting and chaining third-party tools based on text-visible metadata such as tool names, descriptions, and return messages. We show that this convenience creates a supply-chain attack surface: a malicious MCP tool server can be co-registered alongside normal tools and induce overthinking loops, where individually trivial or plausible tool calls compose into cyclic trajectories that inflate end-to-end tokens and latency without any single step looking abnormal. We formalize this as a structural overthinking attack, distinguishable from token-level verbosity, and implement 14 malicious tools across three servers that trigger repetition, forced refinement, and distraction. Across heterogeneous registries and multiple tool-capable models, the attack causes severe resource amplification (up to $142.4\times$ tokens) and can degrade task outcomes. Finally, we find that decoding-time concision controls do not reliably prevent loop induction, suggesting defenses should reason about tool-call structure rather than tokens alone.

Fields of Study & Subjects

Adversarial Robustness in Machine Learning Security and Verification in Computing Network Security and Intrusion Detection

Publication details

Journal / source
arXiv (Cornell University)
Publication date
16 February 2026
Language
EN
Document type
Preprint