DAERT generates diverse adversarial instructions via a uniform policy in RL to drop VLA task success rates from 93.33% to 5.85% on benchmarks with models like π0 and OpenVLA.
Embodied Red Teaming for Auditing Robotic Foundation Models
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A multi-level taxonomy of risks, attacks, and defenses across the full embodied AI pipeline, synthesizing 500+ papers and flagging overlooked failure modes.
SABER uses a trained ReAct agent to produce bounded adversarial edits to robot instructions, cutting task success by 20.6% and increasing execution length and violations on the LIBERO benchmark across six VLA models.
Q-DIG applies quality diversity optimization with vision-language models to generate diverse adversarial instructions that reveal VLA robot failures and enable robustness improvements via fine-tuning.
Proposes framing auditing of deployed AI systems as continuous statistical monitoring of risk-controlled constraints like fairness and safety under uncertainty.
citing papers explorer
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Uncovering Linguistic Fragility in Vision-Language-Action Models via Diversity-Aware Red Teaming
DAERT generates diverse adversarial instructions via a uniform policy in RL to drop VLA task success rates from 93.33% to 5.85% on benchmarks with models like π0 and OpenVLA.
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Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses
A multi-level taxonomy of risks, attacks, and defenses across the full embodied AI pipeline, synthesizing 500+ papers and flagging overlooked failure modes.
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SABER: A Stealthy Agentic Black-Box Attack Framework for Vision-Language-Action Models
SABER uses a trained ReAct agent to produce bounded adversarial edits to robot instructions, cutting task success by 20.6% and increasing execution length and violations on the LIBERO benchmark across six VLA models.
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Red-Teaming Vision-Language-Action Models via Quality Diversity Prompt Generation for Robust Robot Policies
Q-DIG applies quality diversity optimization with vision-language models to generate diverse adversarial instructions that reveal VLA robot failures and enable robustness improvements via fine-tuning.
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Towards Auditing AI Systems in the Wild
Proposes framing auditing of deployed AI systems as continuous statistical monitoring of risk-controlled constraints like fairness and safety under uncertainty.