How can I efficiently protect my embodied LLM from jailbreaks?
CONCEPT ENHANCEMENT ENGINEERING: A LIGHTWEIGHT AND EFFICIENT ROBUST DEFENSE AGAINST JAILBREAK ATTACKS IN EMBODIED AI
This paper introduces Concept Enhancement Engineering (CEE), a new method for preventing "jailbreak" attacks against embodied AI systems that use large language models (LLMs). Jailbreaking tricks the LLM into performing harmful actions or revealing sensitive information. CEE works by identifying and amplifying the AI's internal safety mechanisms during operation. It analyzes the LLM's internal representations to extract safety patterns across multiple languages and uses these patterns to steer the AI towards safe behavior. This is done by rotating activation vectors within the LLM's "latent space" – a mathematical representation of the AI's internal knowledge and reasoning. This approach is faster and more efficient than existing methods, making it suitable for real-time use in embodied AI systems. It is also designed to work with multi-modal input (e.g., text, images, voice) and does not require retraining the entire LLM. Experiments show CEE effectively defends against several types of jailbreak attacks while having minimal negative impact on normal task performance.