SlowBA: An efficiency backdoor attack towards VLM-based GUI agents
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.
69afd3f7b3ab31dee1281f48
Junxian Li
claimed_verified
2026-03-10T09:14:45.464Z
656ae4088fb1ddf0d5ec9ac5
/avatars/e38468d2c0274f3c0f5732f30a2e3436.svg
Junxian Li
Duke-de-Artois
user
Duke-de-Artois
69afd3f7b3ab31dee1281f49
Tu Lan
69afd3f7b3ab31dee1281f4a
Haozhen Tan
69afd3f7b3ab31dee1281f4b
Yan Meng
69afd3f7b3ab31dee1281f4c
Haojin Zhu
SlowBA: An efficiency backdoor attack towards VLM-based GUI agents
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.
SlowBA is a backdoor attack that targets response latency in vision-language-model-based GUI agents by inducing prolonged reasoning chains through trigger-aware activation learning.
backdoor attack vision-language-model GUI agent response latency reasoning chain reward-level backdoor injection reinforcement learning trigger-aware activation pop-up windows