Physical Adversarial Attacks

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TACTIC: Temporal and Context-Aware LLM Tactical Planning for Roadside LiDAR Attacks
Sep 30, 2026Yiming Gao, Shaocheng LuoAdversarial Attacks Autonomous Driving Perception
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA. · Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
Printability-Constrained Adversarial Decals for Near-Nadir Aerial Perception: Measured Ink Gamuts, Nested Realism Constraints, and a Physical-World Bound
Sep 27, 2026Sandesh Shrestha, K. T. Yasas Mahima, Asanka G. PereraAdversarial Patch Attacks Adversarial Attacks
Industrial Systems Engineering (ISE), Asian Institute of Technology (AIT), Pathum Thani 12120, Thailand
Physical Adversarial Examples for Person Detectors in Thermal Images Based on 3D Modeling
Aug 31, 2026Xiaopei Zhu, Siyuan Huang, Zhanhao Hu +3Thermal Imaging Adversarial Patch Attacks
Department of Computer Science and Technology, Institute for Artificial Intelligence, BNRist, Tsinghua University, Beijing 100084, China · Chinese Institute for Brain Research (CIBR), Beijing 100010, China
UniTexture: Cross-Task Universal Adversarial Textures for Vision-Language-Action Models
Aug 13, 2026Yukun Dai, Mingzhe Dai, Tianshi Wang +3Adversarial Attacks on VLA Models Vision-Language-Action Models
Tongji University · Mohamed bin Zayed University of Artificial Intelligence · University of Electronic Science and Technology of China
Hidden in Plain Sight: Diffusion-Based Unrestricted Robotic Attacks on Vision-Language-Action Models
Aug 11, 2026Jiahui Han, Yuhui Yao, Xin Wang +6Adversarial Attacks Adversarial Attacks on VLA Models
1Xi’an Jiaotong University · 2Shanghai AI Laboratory · University of Science and Technology of China
Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles
Aug 9, 2026Pedram MohajerAnsari, Amir Salarpour, Mert D. PeséAdversarial Robustness VLM Distillation
Clemson University, Clemson, SC, USA
AdvTiles: Physical Adversarial Camouflage Clothing against Person Detectors via Learnable Tiles
Aug 7, 2026Jinlei Wang, Jiahuan Long, Mingkai Sun +9Adversarial Attacks Physical Adversarial Attacks
Sun Yat-sen University · 2Chinese Academy of Military Science · 3Shenzhen University +2
ColorFD: A Finite-Difference Guided Black-Box Physical Adversarial Attack for Remote Sensing Object Detection
Aug 5, 2026Tiannuo Guo, Guhang Qiu, Yuzhen Xie +3Remote Sensing Image Understanding Adversarial Patch Attacks
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
TwinIR: Coordinated Invisible Dual-Point Attacks on Online HD Map Construction
Aug 5, 2026Haibo Hu, Jianghuai Deng, Chen Tang +3HD Map Construction Adversarial Attacks
City University of Hong Kong
Structure-Aware Robust Fine-Tuning: Defending Vision-Language-Action Robots Against Physical Attention Hijacking
Aug 4, 2026Jinquan Zhang, Dongfu Yin, Run Yang +3Fine-Tuning Language-Conditioned Robot Manipulation
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China · Shenzhen University, Shenzhen, China · Carleton University, Ottawa, Canada
VLAGuard: A Framework for Evaluating and Mitigating Physical Attention Hijacking in Vision-Language-Action Robots within Wireless Sensor Networks
Aug 2, 2026Dongfu Yin, Jinquan ZhangAdversarial Patch Attacks Adversarial Attacks on VLA Models
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China · Shenzhen University, Shenzhen, China
Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting
Jul 30, 2026Qingzhao ZhangAdversarial Attacks Adversarial Robustness
The University of Arizoan
Physically Real-time Infrared Attack against Optical Flow Estimation Networks
Jul 29, 2026Shen You, Wei Jiang, Jiarui Liu +4Optical Flow Adversarial Attacks
City University of Hong Kong Tat Chee Avenue, Kowloon, Hong Kong SAR · University of Electronic Science and Technology Chengdu, Sichuan, China · Shenzhen University Shenzhen,Guangdong,China +1
Hiding in Plain Sight: An Effective Physical Adversarial Patch Attack against Visual-Infrared Fused Face Detection
Jul 25, 2026Qiucheng Yu, Tao Ni, Yihe Zhou +2Adversarial Patch Attacks Face Recognition
City University of Hong Kong · King Abdullah University of Science and Technology (KAUST)
ISPCloak: Weaponizing ISP for Optimization-Free Physical Camouflage against Deepfake Detectors
Jul 24, 2026Jiale Zhao, Jiajun Wan, Lei Tang +3Adversarial Attacks Deepfake Detection
Guangdong University of Technology Guangzhou, China · Guizhou Provincial Laboratory of Big Data, State Key Laboratory of Public Big Data, Guizhou University Guiyang, China
Detectors Learn the Wrong Thing: Shortcut-Resistant Adversarial Training Against Physically Realizable Attacks
Jul 23, 2026Yuanhao Huang, Yilong Ren, Jinlei Wang +3Adversarial Training Adversarial Robustness
School of Transportation Science and Engineering, Beihang University, 100191, Beijing, P.R China · Zhongguancun Laboratory, 100191, Beijing, P.R China · School of Systems Science and Engineering, Sun Yat-Sen University, 510006, Guangzhou, P.R China +2
ALLUDE: A Unified Evaluation System for Configurable Attacks in Differentiable Environments
Jul 19, 2026Mansi Phute, Alexander Greenhalgh, Matthew Hull +8Object Detection Physical Adversarial Attacks
Georgia Institute of Technology, USA · Technological Innovation Institute, UAE
AdvSerial: Physical Adversarial Attacks on Infrastructure-mounted Pedestrian Detectors via Semantic Feature Suppression
Jul 19, 2026Yuanhao Huang, Yilong Ren, Jinlei Wang +3Adversarial Patch Attacks Adversarial Attacks
School of Transportation Science and Engineering, Beihang University, 100191, Beijing, P.R China · Zhongguancun Laboratory, 100191, Beijing, P.R China · School of Systems Science and Engineering, Sun Yat-Sen University, 510006, Guangzhou, P.R China +1
Lights, Camera, Malfunction: When Illumination Robustness Leaves VLA Models Blind to Color
Jul 16, 2026Marino Watanabe, Takami Sato, Kentaro YoshiokaAdversarial Training Adversarial Attacks
Keio University
Banshee: Target Switch Attacks on Gimbal-Stabilized Visual Tracking Systems via Acoustic Injection
Jul 10, 2026Jiarui Li, Joseph Brewington, Qingzhao Zhang +1Aerial Robotics Visual Object Tracking
University of Michigan · The University of Arizona
AirflowAttack: Thermal-Airflow Adversarial Perturbations against Infrared Remote-Sensing Vision-Language Models
Jul 7, 2026Cong Su, Jiaju Han, Xuemeng Sun +5Adversarial Attacks on VLMs Remote Sensing
Tianjin University, Tianjin, China · China University of Petroleum-Beijing at Karamay, Karamay, Xinjiang, China · Shanghai Jiao Tong University, Shanghai, China
REVIVE: A Multi-Modal Framework for Vandalism Detection and Recovery in Autonomous Vehicles
Jul 6, 2026Abdullah Tariq Choudhry, Tapadhir DasAutonomous Driving Perception Image Inpainting
University of the Pacific Stockton, CA, USA
Room for Error: Large-Scale Simulation of Over-the-Air Acoustic Attacks
Jun 26, 2026Andrew C. Cullen, Neil G. Marchant, Jiani Xie +4ASR Evaluation Adversarial Attacks
University of Melbourne · DST Group, Adelaide
REALM: A Unified Red-Teaming Benchmark for Physical-World VLMs
Jun 22, 2026Yifei Zhao, Qian Lou, Mengxin ZhengVLM Evaluation VLM Robustness
University of Central Florida
VFACamou: View-Fused Adversarial Camouflage for Environment-Adaptive Physical Evasion
Jun 18, 2026Shihui Yan, Hu Liu, Junyu Shi +6Adversarial Attacks Physical Adversarial Attacks
State Key Laboratory of Intelligent Vehicle Safety Technology · School of Cyber Science and Engineering, Huazhong University of Science and Technology · School of Computer Science and Technology, Huazhong University of Science and Technology +2
Budget-Aware Adaptive Adversarial Patches for Black-Box Object Detection
Jun 16, 2026Pedram MohajerAnsari, Amir Salarpour, David Fernandez +1Adversarial Patch Attacks Object Detection
School of Computing, Clemson University, USA
Structured Adversarial Camouflage via Voronoi Diagrams
Jun 16, 2026Jens Bayer, Stefan Becker, David Münch +2Adversarial Patch Attacks Object Detection
Fraunhofer IOSB and Fraunhofer Center for Machine Learning · Karlsruhe Institute of Technology
Giving AI a Headache: Acoustic Adversarial Attacks to Computer Vision Applications
Jun 12, 2026Nicole Villavicencio-Garduño, Maksim Ekin Eren, Milo Prisbrey +2Adversarial Attacks Adversarial Robustness
Carnegie Mellon University, Pittsburgh, USA · Los Alamos National Laboratory, Los Alamos, USA
Scratched Lenses, Shifted Depth: Passive Camera-Side Optical Attacks
Jun 12, 2026Qinlin He, Zeming Zhuang, Yongji Wu +3Physical Adversarial Attacks
Brian · Clemson University
Adversarial Vulnerabilities of Learned Telesurgery Policies
Jun 10, 2026Shutong Jin, Ziyang Chen, Preethi Satish +3Robot Policy Learning Adversarial Attacks
University of California, Berkeley · KTH Royal Institute of Technology

Sep 30, 2026, cs.ROJ/K move · Enter open · S save

TACTIC: Temporal and Context-Aware LLM Tactical Planning for Roadside LiDAR Attacks

Yiming Gao, Shaocheng Luo

Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA. · Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.

Adversarial Attacks Autonomous Driving Perception Physical Adversarial Attacks

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Physical LiDAR attacks are often evaluated using fixed primitives and manually selected parameters, despite their strong dependence on surrounding traffic. We present TACTIC, a scene-aware framework that uses a multimodal large language model (MLLM) to coordinate state-adaptive roadside LiDAR attacks. Under a gray-box threat model, TACTIC relies only on an attacker-operated roadside perception stack, without accessing the victim LiDAR's native point clouds or internal processing. Local perception provides metric vehicle states, while the MLLM combines these measurements with roadside imagery to infer relational traffic context and construct a semantic scene graph. Based on this representation, TACTIC selects and configures two complementary primitives: \emph{push-away}, which shifts the perceived range of a lead vehicle, and \emph{phantom-obstacle braking}, which triggers emergency braking through obstacle injection. Measured traffic states and empirically calibrated constraints ground the generated tactics in physically feasible operating regions. To accommodate MLLM latency, TACTIC overlaps reasoning and execution asynchronously while high-rate local perception detects scene changes and triggers replanning. Across 280 randomized CARLA trials, the full policy achieves a 100% collision rate, compared with 35% for a fixed rule, 60% for random selection, and 75% for a restricted LLM using mode selection with default parameters. Joint physical-and-image input achieves 100% success, versus 65% with physical measurements alone and 75% with imagery alone, while asynchronous

Δ

refresh reduces scene-mutation response from 7.4 s to 2.0 s. These results show that scene-dependent tactical planning can expose context-sensitive LiDAR failure modes that fixed attack policies may miss.

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