Wenjie Jacky Mo
I am a second-year Ph.D. student in Computer Science at the University of California, Davis, advised by Prof. Muhao Chen and Prof. Zhe Zhao.
My research focuses on the safety, reliability, and evaluation of large language model systems. I study how LLMs fail under adversarial settings, how specialized safeguards can improve model robustness, and how emerging agentic systems can be evaluated and aligned. To this end, I develop benchmarks, red-teaming frameworks, and modular architectures for trustworthy AI.
news
| May 29, 2026 | Our new paper Triaging Threats to Specialized Guardrails is now available on arXiv. In this work, we introduce GuardZoo, a unified human-annotated benchmark for safety guardrails, and RouteGuard, a router-expert framework for threat-specific LLM safety detection. |
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| Apr 07, 2026 | Excited to share that our paper RedCoder: Automated Multi-Turn Red Teaming for Code LLMs has been accepted to ACL 2026 Main! RedCoder introduces an automated multi-turn red-teaming agent for stress-testing the security boundaries of Code LLMs. See you all at SD! |
| Mar 18, 2026 | Our paper DebugLM: Learning Traceable Training Data Provenance for LLMs is now available on arXiv. DebugLM enables LLMs to trace model behaviors back to their responsible training data sources and supports targeted test-time remediation without retraining. |