Li Hu, Guoqiang Chen, Xiuwei Shang, Shaoyin Cheng, Benlong Wu, LiGangyang LiGangyang, Xu Zhu, Weiming Zhang, Nenghai Yu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Binary analysis is crucial for software security, offering insights into compiled programs without source code. As large language models (LLMs) excel in language tasks, their potential for complex decoding binary data structures is growing. However, the lack of standardized benchmarks hinders their evaluation and progress in this domain. To bridge this gap, we introduce BinMetric, a first comprehensive benchmark designed specifically to evaluate LLMs performance on binary analysis tasks. BinMetric comprises 1,000 questions derived from 20 real-world open-source projects across 6 practical binary analysis tasks, including decompilation, code summarization, etc., which reflect actual reverse engineering scenarios. Our empirical study on this benchmark investigates various state-of-the-art LLMs, revealing their strengths and limitations. The findings indicate that while LLMs show strong potential, challenges still exist, particularly in the areas of precise binary lifting and assembly synthesis. In summary, BinMetric makes a significant step forward in measuring binary analysis capabilities of LLMs, establishing a new benchmark leaderboard, and our study offers valuable insights for advancing LLMs in software security.

Advances in automatic graphical user interface (GUI) agents have brought significant privacy and security challenges, especially cloud-based solutions that may leak sensitive data and be vulnerable to man-in-the-middle attacks. To address these issues, we propose MMPro, a novel GUI agent framework that adopts a separated perception-thinking-execution architecture. The perception module and the execution module process inputs locally to generate outputs to ensure security, and the thinking module operates on abstract representations to ensure the effectiveness of the GUI agent.By modularizing each stage while introducing a hybrid description language (HDL), MMPro transforms screen images into abstract structured representations, minimizing the risk of sensitive information leakage. Experimental results on the OSWorld benchmark show that MMPro outperforms existing GUI agent methods while ensuring strong privacy protection and real-time interaction capabilities. This work presents a pioneering approach to developing efficient, privacy-conscious, and explainable automated GUI agents.

Recent provably secure linguistic steganography (PSLS) methods rely on mainstream autoregressive language models (ARMs) to address historically challenging tasks, that is, to disguise covert communication as ``innocuous'' natural language communication. However, due to the characteristic of sequential generation of ARMs, the stegotext generated by ARM-based PSLS methods will produce serious error propagation once it changes, making existing methods unavailable under an active tampering attack. To address this, we propose a robust, provably secure linguistic steganography with diffusion language models (DLMs). Unlike ARMs, DLMs can generate text in a partially parallel manner, allowing us to find robust positions for steganographic embedding that can be combined with error-correcting codes. Furthermore, we introduce error correction strategies, including pseudo-random error correction and neighborhood search correction, during steganographic extraction. Theoretical proof and experimental results demonstrate that our method is secure and robust. It can resist token ambiguity in stegotext segmentation and, to some extent, withstand token-level attacks of insertion, deletion, and substitution.

Penetration testing is essential for ensuring Web security by identifying and mitigating vulnerabilities in advance, and the rapid progress of large language models (LLMs) shows great potential to revolutionize this process through intelligent, automated agents. In this work, we establish a comprehensive end-to-end penetration testing benchmark using a real-world penetration testing environment to explore the capabilities of LLM-based agents in this domain. Our results reveal that the agents are familiar to procedures of penetration testing tasks, but they still face limitations in generating accurate commands and executing complete processes. Accordingly, we summarize the current challenges, including the difficulty of maintaining the entire message history and the tendency for the agent to become stuck. Based on the above insights, we propose a Penetration testing State Machine (PSM) that utilizes the Finite State Machine (FSM) methodology to address these limitations. Then, we introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs, which utilizes the inherent inference ability of LLM and the constraint framework of state machines. Our evaluation results show that AutoPT outperforms the the ReAct-based baseline and improves the task completion rate from 22% to 41% on the benchmark target. Compared with the baseline and manual work, AutoPT also reduces time and economic costs further. In general, our AutoPT has facilitated the development of automated penetration testing and bring new findings and insights for both academia and industry.