arXiv:2606. 09764v1 Announce Type: new Abstract: A useful phone agent needs to be personally intelligent.
By Lawrence Keunho Jang, Mareks Woodside, Geronimo Carom, Andrew Keunwoo Jang, Jing Yu Koh, Ruslan Salakhutdinov
arXiv:2609.23980v1 Announce Type: cross
Abstract: AI agents now report vulnerabilities faster than maintainers can review them. Reports often depend on security properties specific to the application...
By Andy K. Zhang, Ava Huang, Joey Ji, Wai Han, Thomas Qin, Nardos Demilew, Michael Tian-Yue Liu, Brian Song, Riya Dulepet, Brian Wang, Kyleen Liao, Cuiyuanxiu Chen, Nishka Kacheria, Andrew Wu, Pratham Rangwala, Xinjie Wang, Laura Gomezjurado Gonzalez, Anita Ding, Benjamin Yi, Daniel E. Ho, Dan Boneh, Dawn Song, Ion Stoica, Percy Liang
arXiv:2609.22724v1 Announce Type: cross
Abstract: Mobile agents powered by foundation models now automate complex, multi-step workflows on real devices, but their trajectories can violate app-specifi...
By Changyue Jiang, Jiayi Wang, Xin Wen, Jiarun Dai, Geng Hong, Xudong Pan
arXiv:2512. 20872v2 Announce Type: replace-cross Abstract: Function call graphs (FCGs) have emerged as a powerful abstraction for malware detection, capturing the behavioral structure of applications beyond surface-level signatures.
By Jakir Hossain, Jue Guo, Gurvinder Singh, Lukasz Ziarek, Ahmet Erdem Sar{\i}y\"uce
arXiv:2610.14855v1 Announce Type: new
Abstract: The growing deployment of compact large language models on smartphones is enabling autonomous on-device decision-making, such as emerging mobile GUI ag...
By Haowen Wan, Shangzhuo Xie, Syue-Ju Syue, Xianyue Wu, Runhua Li, Qianqian Yang
MobileWorldSafety is a benchmark that evaluates the safety of large language model–powered GUI agents on Android by exposing them to 142 real-world risk tasks involving environmental injection attacks. The benchmark uses a two‑stage verification pipeline—rule‑based checks for clear cases and an LLM judge for ambiguous ones—to distinguish safety failures from capability failures. Experiments on six agents show high vulnerability, with attack success rates between 40.4% and 66.9%, highlighting that current agents often fail to remain safe when faced with adversarial content presented as normal mobile context.
By Sujin Chen, Lijun Li, Tianyi Du, Jing Shao