Generative Textual Adversarial Attack Through Extensible Compositional Perturbation via Reinforcement Learning for Policy Optimization

Summary

GECOMP is a generative textual adversarial attack that uses reinforcement learning for policy optimization. An LLM-based generator rewrites input sequences using an extensible library of compositional perturbations, constrained by semantic similarity and edit magnitude. Across four public datasets and five victim models, it achieved higher attack success rates than ten baseline methods while using fewer queries and less edit magnitude.

Topics

• Adversarial machine learning

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