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Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics
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Recently in robotics, Vision-Language-Action (VLA) models have emerged as a transformative approach, enabling robots to execute complex tasks by integrating visual and linguistic inputs within an end-to-end learning framework. Despite their significant capabilities, VLA models introduce new attack surfaces. This paper systematically evaluates their robustness. Recognizing the unique demands of robotic execution, our attack objectives target the inherent spatial and functional characteristics of robotic systems. In particular, we introduce two untargeted attack objectives that leverage spatial foundations to destabilize robotic actions, and a targeted attack objective that manipulates the robotic trajectory. Additionally, we design an adversarial patch generation approach that places a small, colorful patch within the camera's view, effectively executing the attack in both digital and physical environments. Our evaluation reveals a marked degradation in task success rates, with up to a 100\% reduction across a suite of simulated robotic tasks, highlighting critical security gaps in current VLA architectures. By unveiling these vulnerabilities and proposing actionable evaluation metrics, we advance both the understanding and enhancement of safety for VLA-based robotic systems, underscoring the necessity for continuously developing robust defense strategies prior to physical-world deployments.
Forward citations
Cited by 8 Pith papers
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On Surjectivity of Neural Networks: Can you elicit any behavior from your model?
Pre-LayerNorm transformers and linear attention are almost always surjective, so any target output has an input that produces it in the continuous embedding space.
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State Backdoor: Towards Stealthy Real-world Poisoning Attack on Vision-Language-Action Model in State Space
A backdoor attack on vision-language-action robot policies uses the arm's initial joint configuration as the trigger, achieving >90% triggered failure with only small clean-task degradation.
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LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization
SOTA VLA models like OpenVLA and pi0 collapse when object positions are perturbed, indicating that standard LIBERO scores reward memorization; LIBERO-PRO provides a systematic perturbed evaluation suite.
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ANNIE: Be Careful of Your Robots
The authors build a safety-centered benchmark and attack method that induces vision-language-action robot policies to violate ISO-based safety rules in a majority of tested episodes.
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Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion
AdvOF crafts 3D adversarial objects that mislead VLM perception across multiple views and degrade VLN agent navigation success in simulation.
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Enhancing Reliability in LLM-Integrated Robotic Systems: A Unified Approach to Security and Safety
A unified framework of secure prompting, state memory, and rule-based safety validation improves LLM-driven robot navigation under prompt injection attacks and obstacle-heavy environments, with modest real-robot verification.
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On the Dual-Use Dilemma in Physical Reasoning and Force
Adding Asimov-style safety prompts to vision-language models lowers both harmful and helpful force generation for contact-rich robotic tasks.
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Embodied AI: Emerging Risks and Opportunities for Policy Action
A policy analysis arguing that embodied AI risks are real, under-covered by current US/EU/UK frameworks, and best handled through certification, benchmarks, clarified liability, and economic adaptation.
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