MENTOR: A Metacognition-Driven Self-Evolution Framework for Uncovering and Mitigating Implicit Domain Risks in LLMs
arXiv:2511.07107v3 Announce Type: replace Abstract: Ensuring the safety of Large Language Models (LLMs) is critical for real-world deployment. However, current safety measures often fail to address implicit, domain-specific risks. To investigate this gap, we introduce a dataset of 3,000 annotated queries spanning education, finance, and management. Evaluations across 14 leading LLMs reveal a concerning vulnerability: an average jailbreak success rate of 57.8\%. In response, we propose MENTOR, a metacognition-driven self-evolution framework. MENTOR performs metacognitive self-assessment, using
The increasing deployment of LLMs in sensitive domains necessitates robust safety measures to address implicit risks that current methods fail to mitigate.
This research highlights a significant vulnerability in leading LLMs, where implicit domain risks lead to high jailbreak success rates, posing substantial safety and reliability challenges for real-world applications.
The explicit recognition of implicit, domain-specific risks and the proposal of a metacognition-driven framework like MENTOR shifts the focus of LLM safety from general adversarial attacks to more nuanced contextual vulnerabilities.
- · AI safety researchers
- · LLM developers adopting advanced safety frameworks
- · Industries relying on secure LLM deployments
- · LLM providers with inadequate safety protocols
- · Applications subject to implicit domain risks
- · Users vulnerable to compromised LLM outputs
Increased investment and research into metacognitive and self-evolving AI safety mechanisms will become a priority.
New regulatory frameworks may emerge, mandating more sophisticated and context-aware safety testing for LLMs before deployment in critical sectors.
A potential bifurcation in the LLM market, with 'safe by design' models gaining a significant competitive advantage over less secure alternatives.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI