AI security
Studying how LLM applications separate trusted tasks from untrusted content, and how defenses affect both security and utility.
Researcher / Builder / Engineer
Researching and building secure, reliable AI systems.
01Research focus
Where security meets the systems we build—and the evidence we use to trust them.
Studying how LLM applications separate trusted tasks from untrusted content, and how defenses affect both security and utility.
Building structured datasets, controlled comparisons, and inspectable evaluation workflows for software and AI systems.
Modeling authorization logic and system behavior, then checking whether comparable execution paths enforce consistent rules.
02Selected work
A selection of research and engineering work. Built to understand, evaluate, and improve.
Channel-aware defense for indirect prompt injection in LLM applications.
A black-box defense that separates trusted tasks from instructions embedded in untrusted content. The recorded local benchmark covers 1,546 curated records, four models, and six defenses; aggregate ASR changes from 0.671 to 0.322, with a documented utility trade-off. These are local results, not independent replication.
A benchmark for causal reasoning in physically grounded video scenarios.
Contributed to controlled physics simulation, rendering, and video processing at Shanghai AI Laboratory. The work includes video-question construction and comparative evaluation of multimodal models and human responses; no current submission outcome is asserted.
Pretrained audio model transfer and fusion for industrial anomalous sound detection.
Explored self-supervised audio model transfer, generative audio augmentation, Bayesian-optimized ensembles, and multi-sampling-rate fusion. The available project status is recorded as of December 2025; rankings, publication status, and numerical results are not asserted.
Image–text contrastive learning with controlled encoder and training experiments.
Implemented a CLIP-style dual encoder in PyTorch with an InfoNCE objective. Built a Flickr8k training pipeline using AdamW, cosine scheduling, and image augmentation, and explored BERT and Vision Transformer encoders. Retrieval metrics remain omitted pending evidence review.
Foundation-model transfer and reliable model selection for acoustic monitoring.
Bachelor’s thesis research combining foundation-model transfer, reliable model selection, fusion calibration, and dual-channel evidence for industrial anomalous sound detection.
A CVE/CWE-centered corpus for recurring logic-vulnerability scenarios and mechanisms.
The reviewed repository snapshot from August 2026 records 2,626 analyzed rows across a 23-CWE allowlist, with a Python pipeline for structured scenario and mechanism reports. The counts describe that snapshot, not a live database.
An evidence-first workflow for authorization logic in JavaScript and TypeScript backends.
A four-stage research method separating repository selection, safe-anchor discovery, authorization-context recovery, and V1/V2 comparison. It describes a conservative research workflow, not a validated one-command vulnerability scanner.
Camera calibration and screen-based flower recognition for a robotics project.
Computer-vision modules using OpenCV, NumPy, and Jupyter. The reviewed repository supports the implementation summary; it does not establish project dates, competition results, or an individual leadership role.
03Research journey
A few places that have shaped how I think—and what I work on next.
Selected · Singapore
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1.35°N / 103.82°E
Nanyang Technological University
PhD student in the College of Computing and Data Science, supervised by Prof. Xiaofeng Wang. Research interests include AI for security and harness engineering.
04Experience & education
2026
Authorization logic & LLM application security
Developing evidence-first workflows for logic-vulnerability analysis, historical case organization, and prompt-injection defense evaluation.
Related work2025.06 — 2025.09
Research intern
Contributed to a causal video-reasoning benchmark and a physics-based data-generation pipeline as a listed collaborator.
Related work2026.08 — Present
PhD Student · Computing and Data Science
Supervised by Prof. Xiaofeng Wang.
2022 — 2026
B.E. · Electronic Information Science and Technology
Department of Electronic Engineering. Thesis on reliable foundation-model transfer for industrial anomalous sound detection.
Related work05Publications & writing
Manuscripts, research software, and reports. Each work is listed with its current documented status.
Manuscript & research software
Repository-listed software citation
Co-authored work. No venue acceptance is asserted in the reviewed public record.
Bachelor’s thesis
Department of Electronic Engineering, Tsinghua University
The reviewed public summary is available on this site.
Course research report
Artificial Intelligence Safety and Governance course project
Results come from a recorded local benchmark, not an independently reproduced study.
06Recognition