6 citations · 24 across the 12 of their papers we have counts for
12 papers
Efficient Detection of Toxic Prompts in Large Language Models
Yi Liu, Junzhe Yu, Huijia Sun +4
Large language models (LLMs) like ChatGPT and Gemini have significantly advanced natural language processing, enabling various applications such as chatbots and automated content g…
SPOLRE: Semantic Preserving Object Layout Reconstruction for Image Captioning System Testing
Yi Liu, Guanyu Wang, Xinyi Zheng +4
Image captioning (IC) systems, such as Microsoft Azure Cognitive Service, translate image content into descriptive language but can generate inaccuracies leading to misinterpretati…
Source Code Summarization in the Era of Large Language Models
Weisong Sun, Yun Miao, Yuekang Li +6
To support software developers in understanding and maintaining programs, various automatic (source) code summarization techniques have been proposed to generate a concise natural…
Repairing Catastrophic-Neglect in Text-to-Image Diffusion Models via Attention-Guided Feature Enhancement
Zhiyuan Chang, Mingyang Li, Junjie Wang +3
Text-to-Image Diffusion Models (T2I DMs) have garnered significant attention for their ability to generate high-quality images from textual descriptions. However, these models ofte…
Oedipus: LLM-enchanced Reasoning CAPTCHA Solver
Gelei Deng, Haoran Ou, Yi Liu +3
CAPTCHAs have become a ubiquitous tool in safeguarding applications from automated bots. Over time, the arms race between CAPTCHA development and evasion techniques has led to incr…
Glitch Tokens in Large Language Models: Categorization Taxonomy and Effective Detection
Yuxi Li, Yi Liu, Gelei Deng +7
With the expanding application of Large Language Models (LLMs) in various domains, it becomes imperative to comprehensively investigate their unforeseen behaviors and consequent ou…
Groot: Adversarial Testing for Generative Text-to-Image Models with Tree-based Semantic Transformation
Yi Liu, Guowei Yang, Gelei Deng +5
With the prevalence of text-to-image generative models, their safety becomes a critical concern. adversarial testing techniques have been developed to probe whether such models can…
Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues
Zhiyuan Chang, Mingyang Li, Yi Liu +3
With the development of LLMs, the security threats of LLMs are getting more and more attention. Numerous jailbreak attacks have been proposed to assess the security defense of LLMs…
Pandora: Jailbreak GPTs by Retrieval Augmented Generation Poisoning
Gelei Deng, Yi Liu, Kailong Wang +3
Large Language Models~(LLMs) have gained immense popularity and are being increasingly applied in various domains. Consequently, ensuring the security of these models is of paramou…
MiniScope: Automated UI Exploration and Privacy Inconsistency Detection of MiniApps via Two-phase Iterative Hybrid Analysis
Shenao Wang, Yuekang Li, Kailong Wang +4
The advent of MiniApps, operating within larger SuperApps, has revolutionized user experiences by offering a wide range of services without the need for individual app downloads. H…
Digger: Detecting Copyright Content Mis-usage in Large Language Model Training
Haodong Li, Gelei Deng, Yi Liu +7
Pre-training, which utilizes extensive and varied datasets, is a critical factor in the success of Large Language Models (LLMs) across numerous applications. However, the detailed…
A Prompt Learning Framework for Source Code Summarization
Tingting Xu, Yun Miao, Chunrong Fang +8
(Source) code summarization is the task of automatically generating natural language summaries (also called comments) for given code snippets. Recently, with the successful applica…