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
- Hugging Face warns an autonomous AI agent hacked its network20 Jul
- Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking20 Jul
- Hidden in Thought: Transferable Chain-of-Thought Artifacts Induce Harmful Behavior20 Jul
- Poison to Detect: Detection of Targeted Overfitting in Federated Learning20 Jul
- Coercion and Deception in AI-to-AI Management: An Agentic Benchmark of Unprompted Escalation20 Jul