通过关键词组合精准攻击，大幅降低文本对抗样本生成的查询次数。
PivotAttack: Rethinking the Search Trajectory in Hard-Label Text Attacks via Pivot Words
- 用多臂赌博机识别关键词组作为预测锚点，反向驱动攻击
- 在多个模型上实现更高成功率且查询次数减少30%以上
- 适合研究模型安全或优化对抗攻击的开发者使用
现有硬标签文本攻击通常采用低效的‘外向型’策略，需遍历庞大的搜索空间。本文提出PivotAttack，一种查询高效的‘内向型’框架。该方法利用多臂赌博机算法识别‘枢轴集’——即作为预测锚点的组合式标记组，并针对性地扰动这些关键片段以引发标签翻转。该方法捕捉词间依赖关系，显著降低查询成本。在传统模型与大语言模型上的大量实验表明，PivotAttack在攻击成功率和查询效率方面均持续优于当前最优基线。
原文摘要 · Abstract (English)
Existing hard-label text attacks often rely on inefficient "outside-in" strategies that traverse vast search spaces. We propose PivotAttack, a query-efficient "inside-out" framework. It employs a Multi-Armed Bandit algorithm to identify Pivot Sets-combinatorial token groups acting as prediction anchors-and strategically perturbs them to induce label flips. This approach captures inter-word dependencies and minimizes query costs. Extensive experiments across traditional models and Large Language Models demonstrate that PivotAttack consistently outperforms state-of-the-art baselines in both Attack Success Rate and query efficiency.
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