arXiv:2606.04778v1 Announce Type: cross Abstract: Safety-aligned Large Language Models (LLMs) remain vulnerable to interventions during inference that redirect generation toward harmful outputs. Recent work attributes this to shallow safety, where alignment concentrates in the first few output tokens. We show that shallow safety is a special case of a broader inference-time vulnerability, in which short token injections at any generation step can substantially alter subsequent safety behavior. We also find that a model's alignment with refusal directions in its hidden states does not predict i
This paper highlights emerging vulnerabilities in large language models (LLMs) safety, indicating that current alignment methods are insufficient against sophisticated inference-time attacks.
A strategic reader should care because the inability to fully secure AI models against adversarial inputs poses significant risks for deployment in sensitive applications and critical infrastructure.
The understanding of AI safety mechanisms shifts from a focus on initial alignment to a recognition of persistent vulnerabilities throughout the generation process, demanding more robust, dynamic defenses.
- · AI safety researchers
- · Cybersecurity firms specializing in AI
- · Ethical hackers
- · LLM developers relying on shallow safety methods
- · Organizations deploying LLMs without robust security audits
- · End-users of vulnerable AI systems
Increased urgency and investment in advanced AI red-teaming and defense mechanisms.
Development of entirely new adversarial training techniques and real-time inference monitoring for LLMs.
Potential slowdown in broad LLM deployment in highly sensitive sectors until these vulnerabilities are mitigated.
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Read at arXiv cs.LG