Reverse Exposure

Mitigation strategy

1. Implement rigorous prompt separation and input sanitization: Architecturally isolate system-level instructions from user input, treating all untrusted content as data rather than executable commands. This requires robust filtering of special characters and delimiters to prevent instruction overriding. 2. Establish mandatory output validation and safety filtering: Subject all model-generated content to a post-processing layer that enforces safety policies and validates against known "should-not-do" categories, such as illegal or harmful information, before the output is presented to the user. 3. Utilize advanced model alignment techniques: Employ techniques such as Reinforcement Learning with Human Feedback (RLHF) and adversarial training during the model development lifecycle to systematically improve the model's inherent resistance to instruction hijacking and its adherence to designated ethical and safety guardrails.