A unified framework of secure prompting, state memory, and rule-based safety validation improves LLM-driven robot navigation under prompt injection attacks and obstacle-heavy environments, with modest real-robot verification.
Defending Language Models Against Image-Based Prompt Attacks via User-Provided Spec- ifications, in: 2024 IEEE Security and Privacy Workshops (SPW), IEEE
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Enhancing Reliability in LLM-Integrated Robotic Systems: A Unified Approach to Security and Safety
A unified framework of secure prompting, state memory, and rule-based safety validation improves LLM-driven robot navigation under prompt injection attacks and obstacle-heavy environments, with modest real-robot verification.