钱意(Yi Qian)

钱意(Yi Qian)

Ph.D Candidate in Nanjing University

About me

This is Yi Qian’s home page! (Under construction).

Currently, I am a Ph.D candidate in Nanjing University, National Key Laboratory for Novel Software Technology, supervised by Professor Bing Mao.

I received my B.E. degree in information security from College of Computer Science, Chongqing University, China, in 2021. In the same year, I was admitted to pursue for a Ph.D degree in Nanjing University.

Research Interest

My research centers on the intersection of security for AI and AI for security.

For security for AI, I study how AI systems interact with real software environments, how adversaries may manipulate these interactions, and how to build analysis and defense techniques for safer AI-driven applications.

For AI for security, I explore how learning-based models and large language models can help understand program behavior, infer software specifications, guide security testing, and automate complex analysis tasks that are difficult to handle with manually designed rules alone.

Publications

  1. Qian, Y., et al. (2026, November). Mind the Gap: Action Rebinding Attacks against Android GUI Agents. In The 33rd ACM Conference on Computer and Communications Security (CCS). (CCF-A Conference)

  2. Qian, Y., Peng, F., Wu, H., Chen, L., & Mao, B. (2025, November). Uncovering Prompt Elements: Cloning System Prompts from Behavioral Traces. In 2025 40th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 457-468). IEEE. (CCF-A Conference)

  3. Chen, L., He, Z., Qian, Y., & Mao, B. (2025). CATI++: Empirical Study and Evaluation for Adjacent Instruction Enhanced Type Inference. The Computer Journal, bxaf004. (CCF-B Journal)

  4. Chen, L., Qian, Y., Wang, Y., & Mao, B. (2023). Nimbus++: Revisiting Efficient Function Signature Recovery with Depth Data Analysis. International Journal of Software Engineering and Knowledge Engineering, 33(10), 1537-1565. (CCF-C Journal)

  5. Qian, Y., Chen, L., Wang, Y., & Mao, B. (2022, December). Nimbus: Toward Speed Up Function Signature Recovery via Input Resizing and Multi-Task Learning. In 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) (pp. 454-463). IEEE. (CCF-C Conference)

  6. Chen, L., He, Z., Wu, H., Xu, F., Qian, Y., & Mao, B. (2022, March). Dicomp: Lightweight Data-Driven Inference of Binary Compiler Provenance with High Accuracy. In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) (pp. 112-122). IEEE. (CCF-B Conference)