arXiv:2606.05743v1 Announce Type: cross Abstract: Despite advances in safety alignment, large language models remain vulnerable to continuously evolving jailbreaks. Existing fine-tuned safety classifiers cannot adapt to these evolving attacks, while adaptive memory-based guardrails tend to over-refuse benign queries that resemble stored attacks. We propose Membrane, a self-evolving guardrail built on Contrastive Safety Memory (CSM): each cell pairs the conditions for blocking a harmful query with those for permitting a superficially similar benign request. Without retraining, Membrane evolves
The continuous evolution of LLM attacks necessitates adaptable defenses, pushing research towards dynamic safety mechanisms like Membrane.
Evolving LLM defenses are critical for the secure and reliable deployment of AI agents in sensitive applications, impacting trust and adoption.
The ability of LLM safety systems to self-evolve without constant retraining will significantly improve their resilience against novel jailbreaks, making them more robust.
- · AI development platforms
- · Enterprises deploying LLMs
- · Cybersecurity firms
- · Users of LLM agents
- · Malicious actors designing jailbreaks
- · Models relying on static safety classifiers
Increased reliability and trustworthiness of LLM agents, leading to wider enterprise adoption.
A potential arms race between self-evolving defenses and increasingly sophisticated attack vectors, driving further AI safety research.
Enhanced regulatory confidence in AI systems, potentially influencing policy and standards for autonomous agents.
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Read at arXiv cs.CL