arXiv:2602.12418v2 Announce Type: replace-cross Abstract: Jailbreak attacks remain a persistent threat to large language model safety. We propose Context-Conditioned Delta Steering (CC-Delta), an SAE-based defense that identifies jailbreak-relevant sparse features by comparing token-level representations of the same harmful request with and without jailbreak context. Using paired harmful/jailbreak prompts, CC-Delta selects features via statistical testing and applies inference-time mean-shift steering in SAE latent space. Across four aligned instruction-tuned models and twelve jailbreak attack
The proliferation of powerful large language models necessitates increasingly sophisticated methods to ensure their safety and prevent misuse, driving research into robust defense mechanisms. This research addresses the immediate and growing threat of jailbreak attacks as LLM capabilities expand.
The safety and trustworthiness of large language models are critical for their societal adoption and mitigate risks from malicious actors seeking to exploit their capabilities. Effective jailbreak mitigation directly impacts the security and ethical deployment of AI systems, preserving public trust and regulatory acceptance.
New methods using Sparse Autoencoders offer a more granular and context-aware approach to identifying and neutralizing jailbreak attempts in Large Language Models, improving the robustness of AI safety measures. This implies an evolution in AI defense strategies, moving beyond simple filtering to more adaptive and integrated security protocols.
- · AI safety researchers
- · LLM developers
- · Organizations deploying LLMs
- · Malicious actors attempting LLM jailbreaks
- · Organizations with inadequate AI safety protocols
Increased resilience of large language models against adversarial attacks, leading to safer deployment and use.
Accelerated development of advanced AI safety features becoming a key differentiator among LLM providers, influencing market competition.
Enhanced public trust in AI systems due to improved safety, potentially expanding the scope of applications where LLMs are deemed acceptable for sensitive tasks.
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Read at arXiv cs.LG