Cats Confuse Reasoning LLM: Query Agnostic Adversarial Triggers for Reasoning Models
This work exposes a critical semantic robustness vulnerability in reasoning-oriented large language models (LLMs) on mathematical problem-solving tasks: the models are highly susceptible to query-irrelevant adversarial triggers—semantically unrelated phrases such as “Interesting fact: cats sleep most of their lives”—which drastically increase erroneous outputs. To address this, the authors formally introduce the concept of *query-irrelevant adversarial triggers* and propose CatAttack, a lightweight, automated attack framework. CatAttack leverages a DeepSeek V3 surrogate model for gradient-guided optimization and black-box transferable attacks, incorporating explicit semantic-irrelevance constraints. Experiments demonstrate that generated triggers elevate error rates by over 300% on state-of-the-art models like DeepSeek R1; even a single trigger sentence doubles the probability of incorrect answers. Furthermore, the authors release CatAttack—the first dedicated adversarial trigger dataset for reasoning models—establishing a new benchmark and open-source toolset for evaluating and improving reasoning LLM robustness.