JailWAM: Jailbreaking World Action Models in Robot Control

• URL: http://arxiv.org/abs/2604.05498v1
• Date: Tue, 07 Apr 2026 06:41:41 GMT
• Title: JailWAM: Jailbreaking World Action Models in Robot Control
• Abstract summary: The World Action Model (WAM) can jointly predict future world states and actions, exhibiting stronger physical manipulation capabilities compared with traditional models.<n>Existing research pays extremely limited attention to the critical security gap: the vulnerability of WAM to jailbreak attacks.<n>We propose JailWAM, the first dedicated jailbreak attack and evaluation framework for WAM.
• Score: 31.442262458957174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The World Action Model (WAM) can jointly predict future world states and actions, exhibiting stronger physical manipulation capabilities compared with traditional models. Such powerful physical interaction ability is a double-edged sword: if safety is ignored, it will directly threaten personal safety, property security and environmental safety. However, existing research pays extremely limited attention to the critical security gap: the vulnerability of WAM to jailbreak attacks. To fill this gap, we define the Three-Level Safety Classification Framework to systematically quantify the safety of robotic arm motions. Furthermore, we propose JailWAM, the first dedicated jailbreak attack and evaluation framework for WAM, which consists of three core components: (1) Visual-Trajectory Mapping, which unifies heterogeneous action spaces into visual trajectory representations and enables cross-architectural unified evaluation; (2) Risk Discriminator, which serves as a high-recall screening tool that optimizes the efficiency-accuracy trade-off when identifying destructive behaviors in visual trajectories; (3) Dual-Path Verification Strategy, which first conducts rapid coarse screening via a single-image-based video-action generation module, and then performs efficient and comprehensive verification through full closed-loop physical simulation. In addition, we construct JailWAM-Bench, a benchmark for comprehensively evaluating the safety alignment performance of WAM under jailbreak attacks. Experiments in RoboTwin simulation environment demonstrate that the proposed framework efficiently exposes physical vulnerabilities, achieving an 84.2% attack success rate on the state-of-the-art LingBot-VA. Meanwhile, robust defense mechanisms can be constructed based on JailWAM, providing an effective technical solution for designing safe and reliable robot control systems.

Related papers

• Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models [9.029672953412787]
  Text-to-Image generative models are vulnerable to adversarial misuse.<n>Most existing jailbreak attacks rely on prompt engineering or black-box optimization.<n>We propose MIND, a cognitive jailbreak framework that reframes adversarial prompt generation as a belief-state inference problem.
  arXiv  Detail & Related papers  (2026-07-20T10:09:30Z)
• OopsieVerse: A Safety Benchmark with Damage-Aware Simulation for Robot Manipulation [12.792879405441163]
  OOPSIEVERSE is a unified simulation framework and benchmark for damage-aware household manipulation.<n>OOPSIEVERSE provides damage as an explicit, physically-grounded, and taskagnostic signal.<n>Our results highlight the potential of OOPSIEVERSE as an open-source foundation for systematic, scalable research on safe robot manipulation.
  arXiv  Detail & Related papers  (2026-06-30T17:30:05Z)
• SafeThinker: Reasoning about Risk to Deepen Safety Beyond Shallow Alignment [43.86865924673546]
  We propose SafeThinker, an adaptive framework that allocates defensive resources via a lightweight gateway classifier.<n> Experiments show that SafeThinker significantly lowers attack success rates across diverse jailbreak strategies without compromising robustness.
  arXiv  Detail & Related papers  (2026-01-23T07:12:53Z)
• Cognitive Control Architecture (CCA): A Lifecycle Supervision Framework for Robustly Aligned AI Agents [1.014002853673217]
  LLM agents are vulnerable to Indirect Prompt Injection (IPI) attacks.<n>IPI attacks hijack agent behavior by polluting external information sources.<n>We propose the Cognitive Control Architecture (CCA), a holistic framework achieving full-lifecycle cognitive supervision.
  arXiv  Detail & Related papers  (2025-12-07T08:11:19Z)
• ANNIE: Be Careful of Your Robots [48.89876809734855]
  We present the first systematic study of adversarial safety attacks on embodied AI systems.<n>We show attack success rates exceeding 50% across all safety categories.<n>Results expose a previously underexplored but highly consequential attack surface in embodied AI systems.
  arXiv  Detail & Related papers  (2025-09-03T15:00:28Z)
• ARMOR: Aligning Secure and Safe Large Language Models via Meticulous Reasoning [64.32925552574115]
  ARMOR is a large language model that analyzes jailbreak strategies and extracts the core intent.<n> ARMOR achieves state-of-the-art safety performance, with an average harmful rate of 0.002 and an attack success rate of 0.06 against advanced optimization-based jailbreaks.
  arXiv  Detail & Related papers  (2025-07-14T09:05:54Z)
• T2V-OptJail: Discrete Prompt Optimization for Text-to-Video Jailbreak Attacks [67.91652526657599]
  We formalize the T2V jailbreak attack as a discrete optimization problem and propose a joint objective-based optimization framework, called T2V-OptJail.<n>We conduct large-scale experiments on several T2V models, covering both open-source models and real commercial closed-source models.<n>The proposed method improves 11.4% and 10.0% over the existing state-of-the-art method in terms of attack success rate.
  arXiv  Detail & Related papers  (2025-05-10T16:04:52Z)
• T2VShield: Model-Agnostic Jailbreak Defense for Text-to-Video Models [88.63040835652902]
  Text to video models are vulnerable to jailbreak attacks, where specially crafted prompts bypass safety mechanisms and lead to the generation of harmful or unsafe content.<n>We propose T2VShield, a comprehensive and model agnostic defense framework designed to protect text to video models from jailbreak threats.<n>Our method systematically analyzes the input, model, and output stages to identify the limitations of existing defenses.
  arXiv  Detail & Related papers  (2025-04-22T01:18:42Z)
• CEE: An Inference-Time Jailbreak Defense for Embodied Intelligence via Subspace Concept Rotation [23.07221882519171]
  Large Language Models (LLMs) are increasingly becoming the cognitive core of Embodied Intelligence (EI) systems.<n>We propose a novel and efficient inference-time defense framework: Concept Enhancement Engineering (CEE)<n>CEE enhances the model's inherent safety mechanisms by directly manipulating its internal representations.
  arXiv  Detail & Related papers  (2025-04-15T03:50:04Z)
• Tit-for-Tat: Safeguarding Large Vision-Language Models Against Jailbreak Attacks via Adversarial Defense [90.71884758066042]
  Large vision-language models (LVLMs) introduce a unique vulnerability: susceptibility to malicious attacks via visual inputs.<n>We propose ESIII (Embedding Security Instructions Into Images), a novel methodology for transforming the visual space from a source of vulnerability into an active defense mechanism.
  arXiv  Detail & Related papers  (2025-03-14T17:39:45Z)
• Guardians of the Agentic System: Preventing Many Shots Jailbreak with Agentic System [0.8136541584281987]
  This work uses three examination methods to detect rogue agents through a Reverse Turing Test and analyze deceptive alignment through multi-agent simulations.<n>We develop an anti-jailbreaking system by testing it with GEMINI 1.5 pro and llama-3.3-70B, deepseek r1 models.<n>The detection capabilities are strong such as 94% accuracy for GEMINI 1.5 pro yet the system suffers persistent vulnerabilities when under long attacks.
  arXiv  Detail & Related papers  (2025-02-23T23:35:15Z)
• Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks [62.923992740383966]
  We present the first study of security issues of MSF-based perception in AD systems. We generate a physically-realizable, adversarial 3D-printed object that misleads an AD system to fail in detecting it and thus crash into it. Our results show that the attack achieves over 90% success rate across different object types and MSF.
  arXiv  Detail & Related papers  (2021-06-17T05:11:07Z)

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.