Conflicts Make Large Reasoning Models Vulnerable to Attacks

• URL: http://arxiv.org/abs/2604.09750v1
• Date: Fri, 10 Apr 2026 11:44:57 GMT
• Title: Conflicts Make Large Reasoning Models Vulnerable to Attacks
• Abstract summary: This work investigates how LRMs respond to harmful queries when confronted with two categories of conflicts.<n>Internal conflicts pit alignment values against each other and dilemmas impose mutually contradictory choices.<n>Using over 1,300 prompts across five benchmarks, we evaluate three representative LRMs.
• Score: 17.460309581795595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Reasoning Models (LRMs) have achieved remarkable performance across diverse domains, yet their decision-making under conflicting objectives remains insufficiently understood. This work investigates how LRMs respond to harmful queries when confronted with two categories of conflicts: internal conflicts that pit alignment values against each other and dilemmas, which impose mutually contradictory choices, including sacrificial, duress, agent-centered, and social forms. Using over 1,300 prompts across five benchmarks, we evaluate three representative LRMs - Llama-3.1-Nemotron-8B, QwQ-32B, and DeepSeek R1 - and find that conflicts significantly increase attack success rates, even under single-round non-narrative queries without sophisticated auto-attack techniques. Our findings reveal through layerwise and neuron-level analyses that safety-related and functional representations shift and overlap under conflict, interfering with safety-aligned behavior. This study highlights the need for deeper alignment strategies to ensure the robustness and trustworthiness of next-generation reasoning models. Our code is available at https://github.com/DataArcTech/ConflictHarm. Warning: This paper contains inappropriate, offensive and harmful content.

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