Overthinking Loops in Agents: A Structural Risk via MCP Tools
On the map: LLM reasoning → Reasoning for agents, planning and code
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Abstract (arXiv)
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× 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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 17 of its 41 references that are in the map. First posted on arXiv 16 Feb 2026.
Builds on
17 of 41 references.
Closest ancestors · 1
Same direction, newest first
- 2025CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings2 Jan
Other references · 16
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- 2025Dynamic Early Exit in Reasoning Modelscited by 217
- 2025The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Taskscited by 150
- 2025Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMscited by 115
- 2025Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoningcited by 82
- 2025MCPTox: A Benchmark for Tool Poisoning Attack on Real-World MCP Serverscited by 63
- 2024Denial-of-Service Poisoning Attacks against Large Language Modelscited by 34
4 more
- 2025Efficient Reasoning for Large Reasoning Language Models via Certainty-Guided Reflection Suppressioncited by 23
- 2026Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agentscited by 14
- 2025Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Modelcited by 12
- 2026Mitigating Overthinking in Large Reasoning Models via Difficulty-aware Reinforcement Learningcited by 5
Built on by
Citing papers per year: 2026: 5.
5 papers.
- 2026Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluationcited by 5
- 2026Resource Consumption Threats in Large Language Modelscited by 2
- 2026AEGIS: Preventing Cross-Domain Resource Abuse in MCPcited by 0
- 2026Persistent Billable State: Denial-of-Wallet Attacks and Defenses in Tool-Calling LLM Agentscited by 0
- 2026Absorbed in Inertia: Activation Analysis for Computer-Use Agentscited by 0
- 2026Absorbed in Inertia: Activation Analysis for Computer-Use Agents29 Sep 2026
- 2026Persistent Billable State: Denial-of-Wallet Attacks and Defenses in Tool-Calling LLM Agents23 Sep 2026
- 2026AEGIS: Preventing Cross-Domain Resource Abuse in MCP20 Aug 2026
- 2026Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation9 Jun 2026
- 2026Resource Consumption Threats in Large Language Models17 Mar 2026
Similar, not cited
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