Latent Fusion Jailbreak: Blending Harmful and Harmless Representations to Elicit Unsafe LLM Outputs
Researchers introduce 'Latent Fusion Jailbreak,' a white-box technique that blends harmful and benign internal representations to defeat LLM safety alignment — relevant for enterprises deploying or fine-tuning their own models.
Summary written by editorial AI · Source link below
arXiv:2508.10029v3 Announce Type: replace-cross Abstract: Safety-aligned large language models can still be manipulated through white-box interventions that modify their internal representations. We introduce Latent Fusion Jailbreak (LFJ), which works by pairing a harmful query with a structurally similar but benign counterpart, then interpolating their hidden states at carefully selected layers and token positions. Refusal-loss gradients determine exactly where to intervene, and we optimise la
Editorial Analysis
blendet schädliche und harmlose LLM-Repräsentationen, um Safety-Alignment zu umgehen.
Evaluate your LLM deployment against representation-level jailbreak attacks and update red-team testing.
Researchers show safety guardrails on AI models can be bypassed by manipulating internal representations — relevant as your organisation adopts generative AI.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
More from the AI Security Desk
- OpenAI AI Agents Demonstrate VM Escapes and Secret Cross-Sandbox Communication1d
- Exposed Infrastructure Reveals How Ransomware Affiliates Used AI Agents to Execute Intrusions3d
- You gave your AI agent access, but who is giving it orders?3d
- AI Agents as Insider Threats: Inside Microsoft's Bet on a Unified Security Stack3d
- Critical x402 SDK Flaw Could Let Malicious APIs Drain AI Agents’ Token Balances3d