Backdoor Defense

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4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

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Plan2Cleanse: Test-Time Backdoor Defense via Monte-Carlo Planning in Deep Reinforcement Learning
May 10, 2026Sze-Ann Chen, Zhi-Yi Chin, Kui-Yuan Chen +2Backdoor Detection Backdoor Defense
National Yang Ming Chiao Tung University, Hsinchu, Taiwan · CISPA Helmholtz Center for Information Security
Trapping Attacker in Dilemma: Examining Internal Correlations and External Influences of Trigger for Defending GNN Backdoors
May 8, 2026Fan Yang, Binyan Xu, Di Tang +1Graph Neural Networks Backdoor Attacks
The Chinese University of Hong Kong · Sun Yat-Sen University
BehaviorGuard: Online Backdoor Defense for Deep Reinforcement Learning
May 7, 2026Yinbo Yu, Xueyu Yin, Jiadai Wang +4Backdoor Attacks Backdoor Detection
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics · School of Cybersecurity, Northwestern Polytechnical University · School of Computer Science and Technology, Harbin Institute of Technology +1
Backdoor Mitigation in Object Detection via Adversarial Fine-Tuning
May 7, 2026Kealan Dunnett, Reza Arablouei, Dimity Miller +2Fine-Tuning Backdoor Attacks
Queensland University of Technology, Brisbane Australia · CSIRO, Pullenvale QLD, Australia
Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing
Apr 27, 2026Kaisheng Fan, Weizhe Zhang, Yishu Gao +2LLM Backdoor Attacks Backdoor Defense in LLMs
School of Cyber Science and Technology Harbin Institute of Technology Harbin, China · Department of New Networks Peng Cheng Laboratory Shenzhen, China · SnT University of Luxembourg Luxembourg City, Luxembourg
CSC: Turning the Adversary's Poison against Itself
Apr 23, 2026Yuchen Shi, Xin Guo, Huajie Chen +3Backdoor Attacks Backdoor Defense
Faculty of Data Science, City University of Macau, Taipa, Macau. · Faculty of Engineering and Information Technology, University of Technology Sydney, New South Wales, Australia.
BackdoorVLM: A Benchmark for Backdoor Attacks and Defenses on Vision-Language Models
Nov 24, 2025Juncheng Li, Yige Li, Hanxun Huang +5VLM Evaluation VLM Robustness
Fudan University · Singapore Management University · The University of Melbourne

May 10, 2026, cs.LGJ/K move · Enter open · S save

Plan2Cleanse: Test-Time Backdoor Defense via Monte-Carlo Planning in Deep Reinforcement Learning

Sze-Ann Chen, Zhi-Yi Chin, Kui-Yuan Chen, Chi-Yu Li+1

National Yang Ming Chiao Tung University, Hsinchu, Taiwan · CISPA Helmholtz Center for Information Security

Backdoor Detection Backdoor Defense Robust RL

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Ensuring the security of reinforcement learning (RL) models is critical, particularly when they are trained by third parties and deployed in real-world systems. Attackers can implant backdoors into these models, causing them to behave normally under typical conditions, but execute malicious behaviors when specific triggers are activated. In this work, we propose Plan2Cleanse, a test-time detection and mitigation framework that adapts Monte Carlo Tree Search to efficiently identify and neutralize RL backdoor attacks without requiring model retraining. Our approach recasts backdoor detection as a planning problem, enabling systematic exploration of temporally extended trigger sequences while maintaining black-box access to the target policy. By leveraging the detection results, Plan2Cleanse can further achieve efficient mitigation through tree-search preventive replanning. We evaluated our method in competitive MuJoCo environments, simulated O-RAN wireless networks, and Atari games. Plan2Cleanse achieves substantial improvements, increasing trigger detection success rates by more than 61.4 percentage points in stealthy O-RAN scenarios and improving win rates from 35% to 53% in competitive Humanoid environments. These results demonstrate the effectiveness of our test-time defense approach and highlight the importance of proactive defenses against backdoor threats in RL deployments. Our implementation is publicly available at https://github.com/rl-bandits-lab/RL-Backdoor.

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