2026
FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences
ICML 2026poster
Literature reveals that a Large Language Model's (LLM) behavior is not only conditioned by its original weights but also its instance-level parameters, such as instructional prompt, sampling configuration or quantization. A model that generates safe outputs under one configuration may produce toxic …