钱意(Yi Qian)

钱意(Yi Qian)

Ph.D Candidate in Nanjing University

About me

Currently, I am a Ph.D. candidate at Nanjing University, affiliated with the National Key Laboratory for Novel Software Technology. I work in the System and Software Security Laboratory under the supervision of Professor Bing Mao.

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

Random quote

Ciallo～(∠・ω< )⌒☆

Research Interest

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

Security for AI

  • Agent–environment interactions. Characterizing security boundaries across language models, tools, and software execution environments.
  • Agent and harness vulnerabilities. Investigating how context construction, instruction hierarchies, and tool-mediated execution expose agents to adversarial manipulation.
  • Security evaluation and defense. Developing systematic analyses, evaluation methods, and defenses for AI-driven applications.

AI for Security

  • Program understanding and specification inference. Applying language models to recover program semantics and infer specifications for security analysis.
  • Vulnerability discovery and validation. Integrating model-guided reasoning with program analysis and testing to identify and validate software vulnerabilities.
  • Automated exploitability assessment. Generating proof-of-concept tests and exploit prototypes to reproduce vulnerabilities and assess their security impact.

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. He, X., Chen, Y., Qian, Y., Wei, H., Chen, L., Fu, Z., Wang, L., Wu, H., & Mao, B. (2026, August). When Context Gets Root: Privilege Escalation in LLM Harnesses. arXiv preprint arXiv:2608.27299.

  3. 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)

  4. 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)

  5. 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)

  6. 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)

  7. 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)

Honors & Awards

  • 2025Outstanding Graduate Student, Nanjing University
  • 2025Graduate Talent Scholarship
  • 2020Second Prize, National College Student Information Security Competition (Project Track); project Antideepfake: Countering AI Face-Swapped Videos
  • 2017–2019First-Class Scholarship (three times)
  • 2017–2018Outstanding Student, Chongqing University