الملخص
Mobile agents powered by foundation models now automate complex, multi-step workflows on real devices, but their trajectories can violate app-specific security policies. Existing trajectory-level defenses rely on LLM prompting or rigid rules, and thus fail to support fine-grained, natural-language policies that generalize across apps and tasks. In this work, we introduce MATE, a lightweight, policy-conditioned auditor that encodes both agent trajectories and natural-language security policies to determine whether a trajectory violates a given policy and to explain why. Treating policies as editable text rather than fixed model parameters allows MATE to handle user-defined and evolving requirements without retraining. To construct MATE, we build a knowledge base by extracting app descriptions, workflows, and policies from hundreds of popular mobile apps worldwide, and synthesizing over 140K semantically realistic, policy-conditioned trajectories with a multi-stage pipeline. We further release MATEBench, a trajectory-level auditing benchmark with two synthetic subsets and one real-world subset of manually collected trajectories. Models trained with our synthesis-driven trajectory learning achieve over 95% accuracy on MATEBench, retain strong performance on external safety benchmarks, and audit trajectories from Zhipu's AutoGLM and Alibaba's Mobile-Agent on real devices with over 95% accuracy, outperforming prior methods by over 20%. MATE shows that practical, fine-grained security auditing for heterogeneous mobile agents is both feasible and effective.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Jiang, C., Wang, J., Wen, X., Dai, J., Hong, G., & Pan, X. (2026). MATE: Policy-Aware Security Auditing for Mobile Agents via Synthesis-Driven Trajectory Learning. https://omanscience.com/ar/articles/mate-policy-aware-security-auditing-for-mobile-agents-via-synthesis-driven-trajectory-learning
MLA 9
Jiang, Changyue, et al. "MATE: Policy-Aware Security Auditing for Mobile Agents via Synthesis-Driven Trajectory Learning." https://omanscience.com/ar/articles/mate-policy-aware-security-auditing-for-mobile-agents-via-synthesis-driven-trajectory-learning.
شيكاغو (المؤلف–التاريخ)
Jiang, Changyue, Jiayi Wang, Xin Wen, Jiarun Dai, Geng Hong, and Xudong Pan. 2026. "MATE: Policy-Aware Security Auditing for Mobile Agents via Synthesis-Driven Trajectory Learning." https://omanscience.com/ar/articles/mate-policy-aware-security-auditing-for-mobile-agents-via-synthesis-driven-trajectory-learning.
هارفارد
Jiang, C., Wang, J., Wen, X., Dai, J., Hong, G. and Pan, X. (2026) 'MATE: Policy-Aware Security Auditing for Mobile Agents via Synthesis-Driven Trajectory Learning', Available at: https://omanscience.com/ar/articles/mate-policy-aware-security-auditing-for-mobile-agents-via-synthesis-driven-trajectory-learning.
فانكوفر
Jiang C, Wang J, Wen X, Dai J, Hong G, Pan X. MATE: Policy-Aware Security Auditing for Mobile Agents via Synthesis-Driven Trajectory Learning. https://omanscience.com/ar/articles/mate-policy-aware-security-auditing-for-mobile-agents-via-synthesis-driven-trajectory-learning
IEEE
C. Jiang, J. Wang, X. Wen, J. Dai, G. Hong, and X. Pan, "MATE: Policy-Aware Security Auditing for Mobile Agents via Synthesis-Driven Trajectory Learning," https://omanscience.com/ar/articles/mate-policy-aware-security-auditing-for-mobile-agents-via-synthesis-driven-trajectory-learning.