Institution profile

Nanyang Technological University

Academic institutionasia · sg
Official website
Research library4,631linked papers
Opportunities0open roles
Selected work

Representative Papers

Prompt Injection attack against LLM-integrated Applications

Jun 08, 2023arXiv.org

Prompt injection attacks pose an increasingly severe security threat to large language model (LLM) integrated applications, yet existing black-box attack methods suffer from limited practical efficacy. Method: This paper proposes HouYi—the first real-world-oriented, three-stage black-box prompt injection framework comprising pre-prompt injection, context-aware segmentation, and malicious payload delivery. HouYi uniquely enables automated triggering of high-impact consequences—including arbitrary LLM misuse and application-level prompt stealing—via black-box fuzzing, context-aware prompt engineering, and web-injection-inspired modeling. Contribution/Results: Evaluated through real-world penetration testing across 36 mainstream LLM applications, HouYi uncovered 31 critical vulnerabilities, independently confirmed by ten vendors—including Notion—with impact on millions of users. The work significantly advances LLM security practice by bridging the gap between theoretical attack models and deployable, scalable exploitation techniques.

536 citations38 influentialRead paper

A Systematic Literature Review on Large Language Models for Automated Program Repair

May 02, 2024arXiv.org

Research on large language models (LLMs) for automated program repair (APR) remains fragmented and lacks a systematic, unified understanding. Method: We conduct a systematic literature review (SLR) covering 127 papers published between 2020 and 2024, establishing the first comprehensive conceptual framework for LLM-based APR. We categorize model utilization strategies into three types—fine-tuning, prompt engineering, and hybrid ensemble—and perform multidimensional thematic analysis across input representation, semantic/security-specific repair scenarios, and open-science practices. Contribution/Results: We identify core challenges including model robustness, evaluation bias, and real-world deployment adaptability. The study yields a reusable taxonomy, benchmark insights, and methodological guidelines—delivering the APR community’s first holistic landscape map to precisely identify research gaps and inform future innovation pathways.

39 citations1 influentialRead paper

An Empirical Study of Automated Vulnerability Localization with Large Language Models

Mar 30, 2024arXiv.org

This work systematically evaluates the effectiveness of large language models (LLMs) for line-level vulnerability localization (AVL)—a task lacking comprehensive empirical investigation. Experiments are conducted on BigVul (C/C++) and smart contract vulnerability datasets, covering over ten code-understanding LLMs (60M–16B parameters) spanning encoder-only, encoder-decoder, and decoder-only architectures, under zero-shot, one-shot, discriminative fine-tuning, and generative fine-tuning paradigms. Key contributions include: (1) the first empirical demonstration that discriminative fine-tuning substantially outperforms existing approaches; (2) the proposal of sliding-window context partitioning and right-forward embedding to mitigate context-length limitations; and (3) strong cross-CWE and cross-project generalization, yielding significant improvements in localization accuracy and surpassing state-of-the-art methods.

25 citations2 influentialRead paper

MeViS: A Multi-Modal Dataset for Referring Motion Expression Video Segmentation

Aug 19, 2025IEEE Transactions on Pattern Analysis and Machine Intelligence

Existing video segmentation datasets emphasize static attribute descriptions, neglecting the critical role of motion in video understanding. To address this, we introduce MeViS—the first multimodal video segmentation dataset explicitly guided by motion expression—comprising 33K human-annotated text and audio motion descriptions across 2,006 complex scenes and 8,171 objects, supporting four tasks: Referring Video Object Segmentation (RVOS), Audio-Visual Object Segmentation (AVOS), Referring Multi-Object Tracking (RMOT), and Referring Motion Expression Grounding (RMEG). MeViS pioneers motion semantics as the core referential cue, breaking the static-dominant paradigm. We further propose LMPM++, a model integrating multimodal aligned annotation, motion-aware modeling, and joint audio-visual-linguistic representation, achieving new state-of-the-art performance on RVOS, AVOS, and RMOT. Comprehensive evaluation of 15 mainstream methods reveals systematic motion reasoning bottlenecks; leveraging MeViS significantly improves segmentation and tracking accuracy, advancing motion-centric video understanding.

22 citationsRead paper
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