Benign Data

Benign data, seemingly innocuous information used in training large language models (LLMs), is increasingly recognized as a significant source of vulnerabilities. Research focuses on how seemingly harmless data can inadvertently enable adversarial attacks, bias propagation, and data leakage, even after attempts at model safety alignment and machine unlearning. This highlights the critical need for improved data curation and model robustness techniques to mitigate these risks and ensure responsible AI development. The findings underscore the limitations of current safety measures and the importance of a more holistic approach to data security and model integrity.