TrapVLA: Trapping Vision-Language-Action Models in Configured Failure Modes

• URL: http://arxiv.org/abs/2608.26578v1
• Date: Thu, 27 Aug 2026 03:44:49 GMT
• Title: TrapVLA: Trapping Vision-Language-Action Models in Configured Failure Modes
• Abstract summary: This work introduces Configured Failure Trapping, a novel backdoor attack task against Vision-Language-Action (VLA) models.<n>We propose an effective data engine for synthesizing high-quality target trajectories and an automated suite for measuring configured-failure fidelity.<n>Experiments show that TrapVLA effectively injects configured failure modes into VLA models while largely preserving performance on clean data.
• Score: 65.10128145059127
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This work introduces Configured Failure Trapping, a novel backdoor attack task against Vision-Language-Action (VLA) models, which aims to activate attacks through stealthy textual triggers and induce configured failure modes. Unlike prior backdoor attacks that treat any task failure as a successful attack, Configured Failure Trapping requires the attacker to control how the robot fails (e.g., causing the robot to grasp with a specified positional offset), making it substantially more challenging and hard to detect. To support the new task, we propose an effective data engine for synthesizing high-quality target trajectories and an automated suite for measuring configured-failure fidelity. Then, based on this foundation, we construct two new benchmarks, namely Trap-LIBERO and Trap-RoboTwin, that instantiate Configured Failure Trapping across four representative failure modes. To address this task, we identify sparse action deviation as a critical challenge and accordingly propose a novel method named TrapVLA, which explicitly learns trigger-induced action residuals to steer the policy toward the configured failure behavior. Extensive experiments across simulation benchmarks and real-world robotic settings show that TrapVLA effectively injects configured failure modes into VLA models while largely preserving performance on clean data. Project page: https://john-liua.github.io/TrapVLA/

Related papers

• FLARE: A Failure-Aware Framework for Autonomous Correction and Recovery in Visual-Language Robotic Manipulation [43.89036551517797]
  Vision-Language-Action Models (VLAs) have demonstrated significant promise in generalizing to complex, long-horizon robotic manipulation tasks.<n>Their performance remains brittle, as they are typically trained on trajectory-monotonic, failure-free demonstrations.<n>We propose FLARE, a novel framework that endows VLAs with robust error recovery capabilities through a Retry" and Reset" paradigm.
  arXiv  Detail & Related papers  (2026-08-27T05:58:06Z)
• Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents [58.49879338545782]
  Long-horizon tasks are common in real-world robotic deployments, yet failure detection for such tasks remains underexplored.<n>We present Foresight, a failure detection framework that monitors manipulation trajectories using latent representations from an action-conditioned world model.<n>Our results suggest that action-conditioned world-model embeddings provide a scalable representation for reliable failure monitoring in long-horizon manipulation.
  arXiv  Detail & Related papers  (2026-06-22T09:32:28Z)
• Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring [35.34120853605602]
  textbf-and-Seek is a framework that formulates VLA failure detection as a coarsely supervised learning problem.<n>We evaluate Hide-and-Seek on LIBERO, VLABench, and a real-world robotic platform across three representative VLA policies.
  arXiv  Detail & Related papers  (2026-05-29T04:40:12Z)
• CycleVLA: Proactive Self-Correcting Vision-Language-Action Models via Subtask Backtracking and Minimum Bayes Risk Decoding [60.06899554269808]
  CycleVLA is a system that equips Vision-Language-Action models (VLAs) with proactive self-correction.<n>CycleVLA achieves this by integrating a progress-aware VLA that flags critical subtask transition points.<n>Extensive experiments show that CycleVLA improves performance for both well-trained and under-trained VLAs.
  arXiv  Detail & Related papers  (2026-01-05T17:31:01Z)
• Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models [53.20969621498248]
  We propose an automatic robot failure synthesis approach that procedurally perturbs successful trajectories to generate diverse planning and execution failures.<n>We construct three new failure detection benchmarks: RLBench-Fail, BridgeDataV2-Fail, and UR5-Fail.<n>We then train Guardian, a VLM with multi-view images for detailed failure reasoning and detection.
  arXiv  Detail & Related papers  (2025-12-01T17:57:27Z)
• 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)
• TabVLA: Targeted Backdoor Attacks on Vision-Language-Action Models [63.51290426425441]
  A backdoored VLA agent can be covertly triggered by a pre-injected backdoor to execute adversarial actions.<n>We study targeted backdoor attacks on VLA models and introduce TabVLA, a novel framework that enables such attacks via black-box fine-tuning.<n>Our work highlights the vulnerability of VLA models to targeted backdoor manipulation and underscores the need for more advanced defenses.
  arXiv  Detail & Related papers  (2025-10-13T02:45:48Z)
• SAFE: Multitask Failure Detection for Vision-Language-Action Models [28.746068068444924]
  Vision-language-action models (VLAs) have shown promising robotic behaviors across a diverse set of manipulation tasks.<n>They achieve limited success rates when deployed on novel tasks out of the box.<n>We propose SAFE, a failure detector for generalist robot policies such asVLAs.
  arXiv  Detail & Related papers  (2025-06-11T16:59:13Z)

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.