Dario Pasquini, Martin Strohmeier, and Carmela Troncoso introduce Neural Exec, a set of adversarial prompt injection attacks designed to threaten the security of RAG-based systems. Unlike conventional attacks, Neural Execs are generated through learning-based methods, making them more versatile and challenging to detect using traditional blacklist-based approaches. Key highlights include:

• Creation of versatile and effective execution triggers through a differentiable search problem.
• Production of triggers capable of surviving multi-stage preprocessing pipelines.
• Difficulty in detecting these sophisticated forms of attacks by current methods.
• Call for new solutions to protect RAG systems from such nuanced and potent security threats.

This research underscores the need for continual vigilance and innovation in the realm of AI security, particularly as attackers adopt increasingly sophisticated methods.