A large language model (LLM) has been manipulated to provide advice that serves an attacker’s...

AAISM categorizes the manipulation of an LLM at inference time, where crafted inputs cause outputs to serve attacker objectives, as an evasion attack. Evasion attacks exploit weaknesses in the model’s decision-making boundaries by altering queries to produce compromised or misleading outputs. Privilege escalation refers to unauthorized access rights, data poisoning targets the training phase, and model inversion reconstructs training data. In this case, manipulation of outputs to align with an attacker’s goals reflects an evasion attack.