Yes, here's a simple example of an adversarial attack against a large language model like GPT-3:

Input: "The sky is green."

Adversarial Attack: Add a small perturbation to the input sentence, such as "The sky is green. ;)".

Expected Outcome: The LLM is likely to predict the wrong label for the input, despite the small perturbation, due to its sensitivity to small changes in input.

This is a simple example of an "evasion attack", where the attacker aims to mislead the model by crafting an input that is similar to the original but produces a different prediction.

Adversarial attacks are a major concern for the deployment of LLMs in sensitive applications, and researchers are actively working on developing robust models and mitigation techniques to address this issue.
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