Consistency of Large Reasoning Models Under Multi-Turn Attacks

• URL: http://arxiv.org/abs/2602.13093v2
• Date: Mon, 16 Feb 2026 02:14:01 GMT
• Title: Consistency of Large Reasoning Models Under Multi-Turn Attacks
• Authors: Yubo Li, Ramayya Krishnan, Rema Padman,
• Abstract summary: We evaluate nine frontier reasoning models under adversarial attacks.<n>Most reasoning models studied significantly outperform instruction-tuned baselines.<n>Confidence-Aware Response Generation fails for reasoning models due to overconfidence induced by extended reasoning traces.
• Score: 8.86745721473138
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large reasoning models with reasoning capabilities achieve state-of-the-art performance on complex tasks, but their robustness under multi-turn adversarial pressure remains underexplored. We evaluate nine frontier reasoning models under adversarial attacks. Our findings reveal that reasoning confers meaningful but incomplete robustness: most reasoning models studied significantly outperform instruction-tuned baselines, yet all exhibit distinct vulnerability profiles, with misleading suggestions universally effective and social pressure showing model-specific efficacy. Through trajectory analysis, we identify five failure modes (Self-Doubt, Social Conformity, Suggestion Hijacking, Emotional Susceptibility, and Reasoning Fatigue) with the first two accounting for 50% of failures. We further demonstrate that Confidence-Aware Response Generation (CARG), effective for standard LLMs, fails for reasoning models due to overconfidence induced by extended reasoning traces; counterintuitively, random confidence embedding outperforms targeted extraction. Our results highlight that reasoning capabilities do not automatically confer adversarial robustness and that confidence-based defenses require fundamental redesign for reasoning models.

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