arXiv:2506.07031v5 Announce Type: replace-cross Abstract: Emerging Large Reasoning Models (LRMs) consistently excel in mathematical and reasoning tasks, showcasing remarkable capabilities. However, the enhancement of reasoning abilities and the exposure of internal reasoning processes introduce new safety vulnerabilities. A critical question arises: when reasoning becomes intertwined with harmfulness, will LRMs become more vulnerable to jailbreaks in reasoning mode? To investigate this, we introduce HauntAttack, a novel and general-purpose black-box adversarial attack framework that systematic
The increased sophistication and transparency of Large Reasoning Models (LRMs) are exposing new attack vectors, prompting focused research into their safety vulnerabilities.
This research highlights a critical and evolving security challenge for advanced AI, particularly as these models become more embedded in sensitive decision-making processes.
The understanding of AI safety and security now explicitly includes vulnerabilities arising from the reasoning processes of advanced models, beyond traditional prompt injection.
- · AI safety researchers
- · Cybersecurity firms specializing in AI
- · Developers of robust AI defense mechanisms
- · Developers of unaudited advanced AI models
- · Organizations deploying vulnerable LRMs
- · AI systems without robust adversarial training
Increased investment in adversarial AI research and red-teaming for Large Reasoning Models.
Development of industry standards and regulations for the safety and robustness of advanced reasoning AI.
A potential slowing of LRM deployment in critical infrastructure until these vulnerabilities are adequately addressed.
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Read at arXiv cs.AI