VLALeaks: Membership Inference Attacks against Vision-Language-Action Models

• URL: http://arxiv.org/abs/2606.15165v1
• Date: Sat, 13 Jun 2026 07:29:33 GMT
• Title: VLALeaks: Membership Inference Attacks against Vision-Language-Action Models
• Abstract summary: We reveal, for the first time, the privacy vulnerabilities of Vision-Language-Action (VLA) models.<n>Our work is the first systematic study of MIAs on VLA models, aiming to provide insights for secure and trustworthy VLA models.
• Score: 8.754574712518263
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Vision-Language-Action (VLA) models enable end-to-end robot control and have garnered widespread attention. However, the memorization of training data inherent to VLA, coupled with the high cost of robotic data acquisition, raises serious concerns regarding data privacy leakage and intellectual property infringement. Membership inference attacks (MIAs) aim to determine whether a given sample belongs to the training set. While representing a significant privacy threat, this attack remains underexplored in the context of VLA models. To bridge this gap, we propose VLALeaks, which is based on attention discrepancies in VLA models. We reveal, for the first time, the privacy vulnerabilities of VLA models. Specifically, it comprises a two-stage process: (1) membership feature extraction, and (2) attack model construction. Experimental results across multiple VLA benchmarks demonstrate that VLALeaks readily reveals membership information and achieves optimal attack AUC and TPR@1\%FPR, highlighting the privacy vulnerabilities in current VLA model deployments. Our work is the first systematic study of MIAs on VLA models, aiming to provide insights for secure and trustworthy VLA models.

Related papers

• Membership Inference Attacks on Vision-Language-Action Models [18.964278149350747]
  We present the first systematic study of membership inference attacks against vision-language-action (VLA) models.<n>Our attacks exploit both classic MIA signals, such as token likelihood, and VLA-specific signals, such as observable action errors and temporal motion patterns.<n>Our findings reveal a previously underexplored privacy risk in robotic and embodied AI, and underscore the need for dedicated privacy evaluation and defenses for VLA models.
  arXiv  Detail & Related papers  (2026-05-08T01:16:00Z)
• DualVLA: Building a Generalizable Embodied Agent via Partial Decoupling of Reasoning and Action [62.70893433854428]
  We propose DualVLA, which enhances action performance through carefully designed post-training while still preserving reasoning capability.<n>Experiments show that DualVLA achieves an average success rate of 61.0 in SimplerEnv and an average score of 65.4 across eight competitive multimodal benchmarks.
  arXiv  Detail & Related papers  (2025-11-27T06:03:53Z)
• AttackVLA: Benchmarking Adversarial and Backdoor Attacks on Vision-Language-Action Models [60.39655329875822]
  Vision-Language-Action (VLA) models enable robots to interpret natural-language instructions and perform diverse tasks.<n>Despite growing interest in attacking such models, the effectiveness of existing techniques remains unclear.<n>We propose AttackVLA, a unified framework that aligns with the VLA development lifecycle.
  arXiv  Detail & Related papers  (2025-11-15T10:30:46Z)
• Adversarial Attacks on Robotic Vision Language Action Models [118.02118618146568]
  We study adversarial attacks on vision-language-action models (VLAs)<n>Our main algorithmic contribution is the adaptation and application of LLM jailbreaking attacks to obtain complete control authority.<n>This differs significantly from LLM jailbreaking literature, as attacks in the real world do not have to be semantically linked to notions of harm.
  arXiv  Detail & Related papers  (2025-06-03T19:43:58Z)
• BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization [45.97834622654751]
  BadVLA is a backdoor attack method based on Objective-Decoupled Optimization.<n>We show that BadVLA consistently achieves near-100% attack success rates with minimal impact on clean task accuracy.<n>Our work offers the first systematic investigation of backdoor vulnerabilities in VLA models.
  arXiv  Detail & Related papers  (2025-05-22T13:12:46Z)
• CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models [89.44024245194315]
  We introduce a method that incorporates explicit visual chain-of-thought (CoT) reasoning into vision-language-action models (VLAs)<n>We introduce CoT-VLA, a state-of-the-art 7B VLA that can understand and generate visual and action tokens.<n>Our experimental results demonstrate that CoT-VLA achieves strong performance, outperforming the state-of-the-art VLA model by 17% in real-world manipulation tasks and 6% in simulation benchmarks.
  arXiv  Detail & Related papers  (2025-03-27T22:23:04Z)
• Run-time Observation Interventions Make Vision-Language-Action Models More Visually Robust [9.647148940880381]
  Vision-language-action (VLA) models trained on large-scale internet data and robot demonstrations have the potential to serve as generalist robot policies. We introduce Bring Your Own VLA (BYOVLA): a run-time intervention scheme that dynamically identifies regions of the input image that the model is sensitive to. We show that BYOVLA enables state-of-the-art VLA models to nearly retain their nominal performance in the presence of distractor objects and backgrounds.
  arXiv  Detail & Related papers  (2024-10-02T19:29:24Z)
• VLATest: Testing and Evaluating Vision-Language-Action Models for Robotic Manipulation [7.8735930411335895]
  We present VLATest, a fuzzing framework designed to generate robotic manipulation scenes for testing VLA models.<n>Based on VLATest, we conducted an empirical study to assess the performance of seven representative VLA models.
  arXiv  Detail & Related papers  (2024-09-19T16:33:00Z)
• A Survey on Vision-Language-Action Models for Embodied AI [90.99896086619854]
  Embodied AI is widely recognized as a key element of artificial general intelligence.<n>A new category of multimodal models has emerged to address language-conditioned robotic tasks in embodied AI.<n>We present the first survey on vision-language-action models for embodied AI.
  arXiv  Detail & Related papers  (2024-05-23T01:43:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.