$DA^3$: A Distribution-Aware Adversarial Attack against Language Models
This addresses the issue of detectable adversarial examples in language models for security and robustness applications, representing an incremental improvement over prior attack methods.
The paper tackles the problem that adversarial attacks on language models often produce detectable examples due to distribution shifts, proposing a Distribution-Aware Adversarial Attack (DA^3) method to improve effectiveness under detection, with experiments showing high attack success rates and transferability across models like BERT, RoBERTa, and LLaMA2-7b.
Language models can be manipulated by adversarial attacks, which introduce subtle perturbations to input data. While recent attack methods can achieve a relatively high attack success rate (ASR), we've observed that the generated adversarial examples have a different data distribution compared with the original examples. Specifically, these adversarial examples exhibit reduced confidence levels and greater divergence from the training data distribution. Consequently, they are easy to detect using straightforward detection methods, diminishing the efficacy of such attacks. To address this issue, we propose a Distribution-Aware Adversarial Attack ($DA^3$) method. $DA^3$ considers the distribution shifts of adversarial examples to improve attacks' effectiveness under detection methods. We further design a novel evaluation metric, the Non-detectable Attack Success Rate (NASR), which integrates both ASR and detectability for the attack task. We conduct experiments on four widely used datasets to validate the attack effectiveness and transferability of adversarial examples generated by $DA^3$ against both the white-box BERT-base and RoBERTa-base models and the black-box LLaMA2-7b model.