SABER：一种针对视觉-语言-动作模型的隐秘代理黑盒攻击框架
SABER: A Stealthy Agentic Black-Box Attack Framework for Vision-Language-Action Models
AI总结：
SABER通过生成最小有效指令修改，评估VLA模型的鲁棒性，展示小幅度指令扰动可显著降低机器人执行效果，且代理黑盒方法在红队测试中表现高效且可扩展。
摘要：
视觉-语言-动作（VLA）模型使机器人能够根据视觉观察执行自然语言指令，但指令通道也引入了关键漏洞：微小文本扰动可改变下游机器人行为。为此，本文提出SABER，一种基于代理的自动攻击框架，可在有限编辑预算下生成指令级对抗攻击。SABER利用GRPO训练的ReAct攻击者，在字符、令牌和提示级别工具下生成小而合理的对抗指令修改，诱导目标行为退化，包括任务失败、执行时间延长和约束违反增加。在LIBERO基准上，SABER在六个最先进的VLA模型上使任务成功率降低20.6%，动作序列长度增加55%，约束违反增加33%，同时比强GPT基线少54.7%的字符编辑和21.1%的工具调用。这些结果表明，小而合理的指令修改足以显著降低机器人执行效果，且代理黑盒流程为红队测试机器人基础模型提供了实用、可扩展和适应性强的方法。代码库可在https://github.com/wuxiyang1996/SABER上公开获取。
英文摘要：
Vision-language-action (VLA) models enable robots to follow natural-language instructions grounded in visual observations, but the instruction channel also introduces a critical vulnerability: small textual perturbations can alter downstream robot behavior. Systematic robustness evaluation therefore requires a black-box attacker that can generate minimal yet effective instruction edits across diverse VLA models. To this end, we present SABER, an agent-centric approach for automatically generating instruction-based adversarial attacks on VLA models under bounded edit budgets. SABER uses a GRPO-trained ReAct attacker to generate small, plausible adversarial instruction edits using character-, token-, and prompt-level tools under a bounded edit budget that induces targeted behavioral degradation, including task failure, unnecessarily long execution, and increased constraint violations. On the LIBERO benchmark across six state-of-the-art VLA models, SABER reduces task success by 20.6%, increases action-sequence length by 55%, and raises constraint violations by 33%, while requiring 21.1% fewer tool calls and 54.7% fewer character edits than strong GPT-based baselines. These results show that small, plausible instruction edits are sufficient to substantially degrade robot execution, and that an agentic black-box pipeline offers a practical, scalable, and adaptive approach for red-teaming robotic foundation models. The codebase is publicly available at https://github.com/wuxiyang1996/SABER.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
