mlsanityData to 7 Oct 2026

Overthinking Loops in Agents: A Structural Risk via MCP Tools

arXiv 2602.14798 ↗First posted 16 Feb 2026cs.CL cs.CRcited by 5

On the map: LLM reasoning → Reasoning for agents, planning and code

also in 1 more
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

  1. 2025CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings2 Jan

Other references · 16

  1. 2025Qwen3 Technical Reportcited by 8,195
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  4. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
  5. 2025Stop Overthinking: A Survey on Efficient Reasoning for Large Language Modelscited by 456
  6. 2025GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Modelscited by 446
  7. 2025Dynamic Early Exit in Reasoning Modelscited by 217
  8. 2025The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Taskscited by 150
  9. 2025Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMscited by 115
  10. 2025Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoningcited by 82
  11. 2025MCPTox: A Benchmark for Tool Poisoning Attack on Real-World MCP Serverscited by 63
  12. 2024Denial-of-Service Poisoning Attacks against Large Language Modelscited by 34
4 more
  1. 2025Efficient Reasoning for Large Reasoning Language Models via Certainty-Guided Reflection Suppressioncited by 23
  2. 2026Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agentscited by 14
  3. 2025Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Modelcited by 12
  4. 2026Mitigating Overthinking in Large Reasoning Models via Difficulty-aware Reinforcement Learningcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026ChainFuzzer: Greybox Fuzzing for Workflow-Level Multi-Tool Vulnerabilities in LLM Agents
  2. 2025ToolTweak: An Attack on Tool Selection in LLM-based Agents
  3. 2026SOK: A Taxonomy of Attack Vectors and Defense Strategies for Agentic Supply Chain Runtime
  4. 2026MCP-ITP: An Automated Framework for Implicit Tool Poisoning in MCP
  5. 2026Think Twice Before You Act: Protecting LLM Agents Against Tool Description Poisoning via Isolated Planning
  6. 2026MemMorph: Tool Hijacking in LLM Agents via Memory Poisoning
  7. 2025Parasites in the Toolchain: A Large-Scale Analysis of Attacks on the MCP Ecosystem
  8. 2026Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents