arXiv:2606.25182v1 Announce Type: cross Abstract: Jailbreak attacks reveal a persistent weakness in aligned Large Language Models: carefully crafted prompts can elicit policy-violating responses despite safety training. While most defenses operate at the prompt or output level, it remains unclear how harmful intent is encoded within the model's internal representations. We investigate this question by analyzing token-level predictive entropy trajectories across layers of a frozen LLM using the logit lens. We find that static aggregate statistics of prompt-level entropy (e.g., mean, variance) c
This paper addresses a persistent and growing problem with large language models as they become more ubiquitous and are deployed in sensitive applications.
Improving the robustness and safety of LLMs against jailbreak attacks is critical for their reliable integration into advanced AI systems and for maintaining public trust.
New methods for detecting harmful intent 'inside' the model could lead to more robust, real-time defenses against sophisticated adversarial attacks, shifting the focus from external prompt/output filtering to internal model safety.
- · AI Safety Researchers
- · LLM Developers
- · AI-powered customer service platforms
- · Sensitive AI applications
- · Adversarial AI developers
- · Malicious actors attempting jailbreaks
Enhanced security and reliability of Large Language Models via internal state monitoring.
Increased adoption of LLMs in applications requiring high trust and safety, potentially accelerating the development of autonomous AI systems.
A potential arms race between internal model defenses and new, more subtle jailbreak techniques exploiting emergent model behaviors.
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Read at arXiv cs.LG