AI Agent Security

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43 papers in the last four weeks, up 258% on the four weeks before. 0.4% of all new papers.

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SlowBA: An efficiency backdoor attack towards VLM-based GUI agents
Mar 9, 2026Junxian Li, Tu Lan, Haozhen Tan +2Backdoor Attacks GUI Agents
School of Computer Science, Shanghai Jiao Tong University
Proof-of-Guardrail in AI Agents and What (Not) to Trust from It
Mar 6, 2026Xisen Jin, Michael Duan, Qin Lin +4LLM Guardrails Remote Attestation
Sahara AI · University of Southern California
Next-Gen CAPTCHAs: Leveraging the Cognitive Gap for Scalable and Diverse GUI-Agent Defense
Feb 9, 2026Jiacheng Liu, Yaxin Luo, Jiacheng Cui +3Cybersecurity AI Agent Security
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · MetaAgentX · University College London
StepShield: When, Not Whether to Intervene on Rogue Agents
Jan 29, 2026Gloria Felicia, Zitha Sasindran, Jinfeng He +3LLM Guardrails AI Agent Security
University of Virginia · Indian Institute of Science Bangalore · Indian Institute of Science, Bangalore +5
Mind the Gap: Action Rebinding Attacks against Android GUI Agents
Jan 18, 2026Yi Qian, Kunwei Qian, Xingbang He +7Mobile GUI Automation Adversarial Attacks
State Key Laboratory for Novel Software Technology, Nanjing University · Hornor Device Co., Ltd · Institute of Dataspace, Hefei Comprehensive National Science Center China
Breaking the Code: Security Assessment of AI Code Agents Through Systematic Jailbreaking Attacks
Oct 1, 2025Shoumik Saha, Jifan Chen, Sam Mayers +3AI Coding Agents AI Agent Security
University of Maryland – College Park · AWS AI Labs · Meta Superintelligence Labs
Enabling Regulatory Multi-Agent Collaboration: Architecture, Challenges, and Solutions
Sep 11, 2025Qinnan Hu, Yuntao Wang, Yuan Gao +3Agent Reliability AI Agent Security
School of Cyber Science and Engineering, Xi’an Jiaotong University, China · School of Mechatronic Engineering and Automation, Shanghai University, China
Easier Said Than Done: Unpacking Intent-Behavior Gap in Jailbreaking LLM-Based Robots
Dec 21, 2024Xuancun Lu, Zhengxian Huang, Xinfeng Li +3Large Language Model-Based Robot Planning Adversarial Attacks on LLMs
Zhejiang University
Capable but Careless: Do Computer-Use Agents Follow Contextual Integrity?
Date pendingAnmol Goel, Iryna GurevychData Leakage Contextual Integrity
Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science, TU Darmstadt and National Research Center for Applied Cybersecurity ATHENE
Builder, Defender, Breaker: Measurable Independence and Bounded Autonomy When Generative Models Build, Defend and Test Software
Date pendingMohamed Chahine GhanemAI Assurance AI Agent Security
School of Computer Science and Mathematics, Keele University, Newcastle-under-Lyme, United Kingdom · Cybersecurity Institute, University of Liverpool, Liverpool, United Kingdom
Your Agent Says Yes: Interpreting Adversarial Market Behavior Beyond Individual Transactions
Date pendingZelin Li, Yiyun Su, Matt White +3LLM Agent Evaluation AI Agent Security
The Ohio State University · Rutgers University · University of California, Berkeley +3

Mar 9, 2026, cs.CRJ/K move · Enter open · S save

SlowBA: An efficiency backdoor attack towards VLM-based GUI agents

Junxian Li, Tu Lan, Haozhen Tan, Yan Meng+1

School of Computer Science, Shanghai Jiao Tong University

Backdoor Attacks GUI Agents AI Agent Security Vision-Language Agents

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Modern vision-language-model (VLM) based graphical user interface (GUI) agents are expected not only to execute actions accurately but also to respond to user instructions with low latency. While existing research on GUI-agent security mainly focuses on manipulating action correctness, the security risks related to response efficiency remain largely unexplored. In this paper, we introduce SlowBA, a novel backdoor attack that targets the responsiveness of VLM-based GUI agents. The key idea is to manipulate response latency by inducing excessively long reasoning chains under specific trigger patterns. To achieve this, we propose a two-stage reward-level backdoor injection (RBI) strategy that first aligns the long-response format and then learns trigger-aware activation through reinforcement learning. In addition, we design realistic pop-up windows as triggers that naturally appear in GUI environments, improving the stealthiness of the attack. Extensive experiments across multiple datasets and baselines demonstrate that SlowBA can significantly increase response length and latency while largely preserving task accuracy. The attack remains effective even with a small poisoning ratio and under several defense settings. These findings reveal a previously overlooked security vulnerability in GUI agents and highlight the need for defenses that consider both action correctness and response efficiency. Code can be found in https://github.com/tu-tuing/SlowBA.

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