Benchmarking Gaslighting Negation Attacks Against Reasoning Models
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arXiv
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| Format: | Preprint |
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2025
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| author | Zhu, Bin Yin, Hailong Chen, Jingjing Jiang, Yu-Gang |
| author_facet | Zhu, Bin Yin, Hailong Chen, Jingjing Jiang, Yu-Gang |
| contents | Recent advances in reasoning-centric models promise improved robustness through mechanisms such as chain-of-thought prompting and test-time scaling. However, their ability to withstand gaslighting negation attacks-adversarial prompts that confidently deny correct answers-remains underexplored. In this paper, we conduct a systematic evaluation of three state-of-the-art reasoning models, i.e., OpenAI's o4-mini, Claude-3.7-Sonnet and Gemini-2.5-Flash, across three multimodal benchmarks: MMMU, MathVista, and CharXiv. Our evaluation reveals significant accuracy drops (25-29% on average) following gaslighting negation attacks, indicating that even top-tier reasoning models struggle to preserve correct answers under manipulative user feedback. Built upon the insights of the evaluation and to further probe this vulnerability, we introduce GaslightingBench-R, a new diagnostic benchmark specifically designed to evaluate reasoning models' susceptibility to defend their belief under gaslighting negation attacks. Constructed by filtering and curating 1,025 challenging samples from the existing benchmarks, GaslightingBench-R induces even more dramatic failures, with accuracy drops exceeding 53% on average. Our findings highlight a fundamental gap between step-by-step reasoning and resistance to adversarial manipulation, calling for new robustness strategies that safeguard reasoning models against gaslighting negation attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09677 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmarking Gaslighting Negation Attacks Against Reasoning Models Zhu, Bin Yin, Hailong Chen, Jingjing Jiang, Yu-Gang Computer Vision and Pattern Recognition Artificial Intelligence Recent advances in reasoning-centric models promise improved robustness through mechanisms such as chain-of-thought prompting and test-time scaling. However, their ability to withstand gaslighting negation attacks-adversarial prompts that confidently deny correct answers-remains underexplored. In this paper, we conduct a systematic evaluation of three state-of-the-art reasoning models, i.e., OpenAI's o4-mini, Claude-3.7-Sonnet and Gemini-2.5-Flash, across three multimodal benchmarks: MMMU, MathVista, and CharXiv. Our evaluation reveals significant accuracy drops (25-29% on average) following gaslighting negation attacks, indicating that even top-tier reasoning models struggle to preserve correct answers under manipulative user feedback. Built upon the insights of the evaluation and to further probe this vulnerability, we introduce GaslightingBench-R, a new diagnostic benchmark specifically designed to evaluate reasoning models' susceptibility to defend their belief under gaslighting negation attacks. Constructed by filtering and curating 1,025 challenging samples from the existing benchmarks, GaslightingBench-R induces even more dramatic failures, with accuracy drops exceeding 53% on average. Our findings highlight a fundamental gap between step-by-step reasoning and resistance to adversarial manipulation, calling for new robustness strategies that safeguard reasoning models against gaslighting negation attacks. |
| title | Benchmarking Gaslighting Negation Attacks Against Reasoning Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.09677 |