Meikang Qiu

Abliterated LLMs can dramatically enhance vulnerability detection and patch validation, achieving up to 67.8% usability in early-stage validation compared to just 29.9% for their aligned counterparts.

Intermediate embeddings in distributed MLLM frameworks can leak sensitive image prompts, with new attacks achieving 100% extraction accuracy and revealing serious privacy implications.