About Me
Hi! I'm Jianshuo Dong, a PhD student at Tsinghua University, advised by Prof. Han Qiu.
Research Interests
- Safety & Security of Large Model Systems: Convey trustworthy large model services to downstream users.
- Autonomous Agentic AI: Developing strong, safe, and efficient autonomous agents.
- Explainable AI: Reverse-engineer and closely monitor AI models.
Email: dongjs23(at)mails(dot)tsinghua(dot)edu(dot)cn
News
2026-09-05
Congratulations to Yutong! Our paper INTENT-AS-A-TOOL was accepted to EMNLP 2026 Main!
2026-08-01
Our new paper on IPI probing is now available on arXiv!
2026-05-01
One paper (SafeSearch) was accepted to ICML 2026 as a regular paper!
2026-04-04
One paper (IFEval++) got accepted to ACL 2026 main (oral)!
2025-10-17
I became a PhD candidate at Tsinghua University!
2025-08-23
One paper (Leakage-intent probing) got accepted to EMNLP 2025 main (oral)!
2025-08-10
I created this website for sharing my research and career!
Selected Papers
INTENT-AS-A-TOOL Makes it Easy to Track Agentic Misalignment
EMNLP 2026 Main
TL;DR: Tracking agentic misalignment through fine-grained intent-tool call probabilities.
@article{zhang2026intent,
title={{INTENT-AS-A-TOOL} Makes it Easy to Track Agentic Misalignment},
author={Zhang, Yutong and Dong, Jianshuo and Xu, Peng and Wang, Long and Zhang, Jie and Zhang, Tianwei and Zhang, Xiaoping and Qiu, Han},
journal={arXiv preprint arXiv:2608.27348},
year={2026},
url={https://arxiv.org/abs/2608.27348}
}
Your Agentic LLMs Secretly Encode Indirect Prompt-Injection Exposure in Hidden States
arXiv:2608.02657
TL;DR: Detecting, defending against, and explaining indirect prompt-injection exposure through latent signals in agentic LLMs.
@article{dong2026agentic,
title={Your Agentic LLMs Secretly Encode Indirect Prompt-Injection Exposure in Hidden States},
author={Dong, Jianshuo and Liu, Yiming and Zhang, Maosen and Deng, Nan and Xu, Peng and Zhang, Xiaoping and Zhang, Tianwei and Zhang, Jie and Qiu, Han},
journal={arXiv preprint arXiv:2608.02657},
year={2026},
url={https://arxiv.org/abs/2608.02657}
}
SafeSearch: Automated Red-Teaming of LLM-Based Search Agents
ICML 2026 Regular
TL;DR: To proactively discover the potential vulnerabilities of LLM-based search agents.
@inproceedings{dong2026safesearch,
title={SafeSearch: Automated Red-Teaming of LLM-Based Search Agents},
author={Dong, Jianshuo and Guo, Sheng and Wang, Hao and Chen, Xun and Liu, Zhuotao and Zhang, Tianwei and Xu, Ke and Huang, Minlie and Qiu, Han},
booktitle={Proceedings of the 43rd International Conference on Machine Learning},
month=jul,
year={2026},
address={Seoul, South Korea},
url={https://icml.cc/virtual/2026/poster/65893}
}
Revisiting the Reliability of Language Models in Instruction-Following
ACL 2026 Oral
TL;DR: Investigating nuance-oriented reliability of LLMs in instruction-following with varied phrasings.
@inproceedings{dong-etal-2026-revisiting,
title={Revisiting the Reliability of Language Models in Instruction-Following},
author={Dong, Jianshuo and Zhang, Yutong and Liu, Yan and Zhong, Zhenyu and Wei, Tao and Zhang, Chao and Qiu, Han},
booktitle={Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)},
month=jul,
year={2026},
address={San Diego, California, United States},
publisher={Association for Computational Linguistics},
url={https://aclanthology.org/2026.acl-long.354/},
doi={10.18653/v1/2026.acl-long.354},
pages={7784--7812}
}
Towards Understanding the Cognitive Habits of Large Reasoning Models
MIR 2026
TL;DR: Studying whether large reasoning models exhibit human-like cognitive habits.
@article{dong2026cognitive,
title={Towards Understanding the Cognitive Habits of Large Reasoning Models},
author={Dong, Jianshuo and Fu, Yujia and Hu, Chuanrui and Zhang, Chao and Qiu, Han},
journal={Machine Intelligence Research},
volume={23},
number={4},
pages={873--886},
year={2026},
doi={10.1007/s11633-026-1654-9}
}
"I've Decided to Leak": Probing Internals Behind Prompt Leakage Intents
EMNLP 2025 Oral
TL;DR: Diving into the internals to understand LLMs' prompt leakage intents.
@inproceedings{dong-etal-2025-ive,
title = "``{I}{'}ve Decided to Leak'': Probing Internals Behind Prompt Leakage Intents",
author = "Dong, Jianshuo and Zhang, Yutong and Liu, Yan and Zhong, Zhenyu and Wei, Tao and Xu, Ke and Huang, Minlie and Zhang, Chao and Qiu, Han",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1082/",
doi = "10.18653/v1/2025.emnlp-main.1082",
pages = "21318--21348"
}
Can Large Language Models Automate the Refinement of Cellular Network Specifications?
arXiv:2507.04214
TL;DR: Evaluate and improve the performance of LLMs in refining cellular network specifications concerning security issues.
@article{dong2025cellular,
title={Can Large Language Models Automate the Refinement of Cellular Network Specifications?},
author={Dong, Jianshuo and Li, Yuanjie and Liu, Jun and Li, Hewu and Qiu, Han},
journal={arXiv preprint arXiv:2507.04214},
year={2025},
url={https://arxiv.org/abs/2507.04214}
}
An Engorgio Prompt Makes Large Language Model Babble on
ICLR 2025 Poster
TL;DR: An inference cost attack targeting modern auto-regressive LLMs.
@inproceedings{dong2025engorgio,
title={An Engorgio Prompt Makes Large Language Model Babble on},
author={Jianshuo Dong and Ziyuan Zhang and Qingjie Zhang and Tianwei Zhang and Hao Wang and Hewu Li and Qi Li and Chao Zhang and Ke Xu and Han Qiu},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=m4eXBo0VNc}
}
One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training
ICCV 2023 Poster
TL;DR: Insert unactivated backdoor in the model training process and make it activated by bit-flip attack.
@inproceedings{dong2023onebit,
title={One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training},
author={Dong, Jianshuo and Qiu, Han and Li, Yiming and Zhang, Tianwei and Li, Yuanjie and Lai, Zeqi and Zhang, Chao and Xia, Shu-Tao},
booktitle={ICCV},
year={2023}
}
Experiences
Education
2023 - 2028 (expected)
Tsinghua University, Beijing, China — Ph.D. Student
2019 - 2023
Wuhan University, Wuhan, China — B.E.
Internships
2026 Jun - Aug
ByteDance · Mamoda Team — Research Intern, GUI Agent Reinforcement Learning
Academic Services
Teaching
2025 Spring
Trustworthy Machine Learning, Tsinghua University — Teaching Assistant
Reviewing
AAAI
2027
ARR
2027 Aug
ICLR
2026; 2025 (Notable)
ICML
2026 (Gold)
NeurIPS
2025 (Top)