Cats Confuse Reasoning LLM: Query-Agnostic Adversarial Triggers for Reasoning Models

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Conflicts of Interest

Identified Weaknesses

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Paper Summary

Paperzilla title

Tricking AI with Nonsense: How Silly Sentences Make Math Models Go Bonkers

This paper demonstrates that adding short, irrelevant text snippets to math problems can dramatically increase the error rate of AI models, even without changing the problem's meaning. This vulnerability was shown across different AI models and problem difficulties, raising concerns about the reliability of reasoning models in real-world applications.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Proxy Model Bias

The choice of Deepseek V3 as a proxy model might introduce biases specific to that model family, limiting the generalizability of the discovered triggers.

Benchmark Limitation

The heavy reliance on the GSM8K benchmark, while common, might not fully capture the diversity and complexity of real-world mathematical problems.

Limited Defense Analysis

While the paper explores two common defense strategies, the lack of a comprehensive study on defense mechanisms limits the practical implications of the findings.

Rating Explanation

This paper presents a novel approach to adversarial attacks on reasoning LLMs, demonstrating the vulnerability of these models to subtle, query-agnostic triggers. The automated attack pipeline and the demonstration of cross-family transferability are significant contributions. Despite some limitations in proxy model choice and benchmark coverage, the findings highlight important security and reliability concerns for reasoning models.

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