# Excessive Reasoning Attack on Reasoning LLMs

arXiv: https://arxiv.org/abs/2506.14374 · HTML: https://mlsanity.com/paper/2506.14374/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 0 of its 22 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Built on by (most cited first, 18 of 18)

- [A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models](https://mlsanity.com/paper/2509.03871/) (2025)
- [Adjacent Words, Divergent Intents: Jailbreaking Large Language Models via Task Concurrency](https://mlsanity.com/paper/2510.21189/) (2025)
- [JADES: A Universal Framework for Jailbreak Assessment via Decompositional Scoring](https://mlsanity.com/paper/2508.20848/) (2025)
- [POT: Inducing Overthinking in LLMs via Black-Box Iterative Optimization](https://mlsanity.com/paper/2508.19277/) (2025)
- [ReasoningBomb: A Stealthy Denial-of-Service Attack by Inducing Pathologically Long Reasoning in Large Reasoning Models](https://mlsanity.com/paper/2602.00154/) (2026)
- [RECUR: Resource Exhaustion Attack via Recursive-Entropy Guided Counterfactual Utilization and Reflection](https://mlsanity.com/paper/2602.08214/) (2026)
- [BenchOverflow: Measuring Overflow in Large Language Models via Plain-Text Prompts](https://mlsanity.com/paper/2601.08490/) (2026)
- [OTora: A Unified Red Teaming Framework for Reasoning-Level Denial-of-Service in LLM Agents](https://mlsanity.com/paper/2605.08876/) (2026)
- [Red Teaming Large Reasoning Models](https://mlsanity.com/paper/2512.00412/) (2025)
- [Rethinking Latency Denial-of-Service: Attacking the LLM Serving Framework, Not the Model](https://mlsanity.com/paper/2602.07878/) (2026)
- [Resource Consumption Threats in Large Language Models](https://mlsanity.com/paper/2603.16068/) (2026)
- [Finding and Reactivating Post-Trained LLMs' Hidden Safety Mechanisms](https://mlsanity.com/paper/2604.00012/) (2026)
- [Pruning Unsafe Tickets: A Resource-Efficient Framework for Safer and More Robust LLMs](https://mlsanity.com/paper/2604.15780/) (2026)
- [ReasonBreak: Probing Vulnerabilities in Reasoning-Enabled Vision-Language-Action Models for Autonomous Driving](https://mlsanity.com/paper/2605.29114/) (2026)
- [RecurGuard: Runtime Monitoring for Reasoning-Token Consumption Attacks](https://mlsanity.com/paper/2606.07968/) (2026)
- [Overthink-Triggered Slowdown Attacks on LVLM-Based Robotic Systems](https://mlsanity.com/paper/2607.01518/) (2026)
- [Never Stop Speaking: a Denial-of-Service Attack on End-to-End Speech Language Models](https://mlsanity.com/paper/2608.10405/) (2026)
- [Speedbumps: Rejection Attacks on Speculative Decoding](https://mlsanity.com/paper/2610.10929/) (2026)

## Abstract (arXiv)

Recent reasoning large language models (LLMs), such as OpenAI o1 and DeepSeek-R1, exhibit strong performance on complex tasks through test-time inference scaling. However, prior studies have shown that these models often incur significant computational costs due to excessive reasoning, such as frequent switching between reasoning trajectories (e.g., underthinking) or redundant reasoning on simple questions (e.g., overthinking). In this work, we expose a novel threat: adversarial inputs can be crafted to exploit excessive reasoning behaviors and substantially increase computational overhead without compromising model utility. Therefore, we propose a novel loss framework consisting of three components: (1) Priority Cross-Entropy Loss, a modification of the standard cross-entropy objective that emphasizes key tokens by leveraging the autoregressive nature of LMs; (2) Excessive Reasoning Loss, which encourages the model to initiate additional reasoning paths during inference; and (3) Delayed Termination Loss, which is designed to extend the reasoning process and defer the generation of final outputs. We optimize and evaluate our attack for the GSM8K and ORCA datasets on DeepSeek-R1-Distill-LLaMA and DeepSeek-R1-Distill-Qwen. Empirical results demonstrate a 3x to 9x increase in reasoning length with comparable utility performance. Furthermore, our crafted adversarial inputs exhibit transferability, inducing computational overhead in o3-mini, o1-mini, DeepSeek-R1, and QWQ models.