most citedUnraveling the Key of Machine Learning-based Android Malware Detection

4 citations · 4 across the 2 of their papers we have counts for

collaborators

8 papers

cs.CR2026

Heimdallr: Characterizing and Detecting LLM-Induced Security Risks in GitHub CI Workflows

Bonan Ruan, Yeqi Fu, Chuqi Zhang +3

GitHub Continuous Integration (CI) workflows increasingly integrate Large Language Models (LLMs) to automate review, triage, content generation, and repository maintenance. This cr…

cs.CR20264 cited

Unraveling the Key of Machine Learning-based Android Malware Detection

Jiahao Liu, Jun Zeng, Fabio Pierazzi +3

With the rapid advancement of machine learning (ML), ML-based Android malware detection has gained significant popularity due to its ability to automatically learn malicious patter…

cs.SE2026

DevOps-Gym: Benchmarking AI Agents in Software DevOps Cycle

Yuheng Tang, Kaijie Zhu, Bonan Ruan +14

Even though demonstrating extraordinary capabilities in code generation and software issue resolving, AI agents' capabilities in the full software DevOps cycle are still unknown. D…

cs.CR2025

TraceAegis: Securing LLM-Based Agents via Hierarchical and Behavioral Anomaly Detection

Jiahao Liu, Bonan Ruan, Xianglin Yang +5

LLM-based agents have demonstrated promising adaptability in real-world applications. However, these agents remain vulnerable to a wide range of attacks, such as tool poisoning and…

cs.SE2025

Propagation-Based Vulnerability Impact Assessment for Software Supply Chains

Bonan Ruan, Zhiwei Lin, Jiahao Liu +3

Identifying the impact scope and scale is critical for software supply chain vulnerability assessment. However, existing studies face substantial limitations. First, prior studies…

cs.CR2025

When MCP Servers Attack: Taxonomy, Feasibility, and Mitigation

Weibo Zhao, Jiahao Liu, Bonan Ruan +2

Model Context Protocol (MCP) servers enable AI applications to connect to external systems in a plug-and-play manner, but their rapid proliferation also introduces severe security…