2 citations · 2 across the 10 of their papers we have counts for
11 papers
CHASE: Adversarial Red-Blue Teaming for Improving LLM Safety using Reinforcement Learning
Rahul Markasserithodi, Aditya Joshi, Yuekang Li +3
Despite advances in safety alignment, prompt-rewriting attacks such as persona modulation, fictional framing and persuasion-based reformulation, can bypass safety filters even on f…
Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions
Zhuoran Pan, Yue Li, Zhi Guan +2
The emergence of Large Language Models (LLMs) offers a transformative interface for Web3, yet existing benchmarks fail to capture the complexity of translating high-level user inte…
Membership Inference Attacks Against Video Large Language Models
Wei Song, Yuxin Cao, Ziqi Ding +3
Video large language models (VideoLLMs) are increasingly trained or instruction-tuned on large-scale video--text corpora collected from heterogeneous sources, raising an immediate…
NGCaptcha: A CAPTCHA Bridging the Past and the Future
Ziqi Ding, Shangzhi Xu, Wei Song +1
CAPTCHAs are widely employed for distinguishing humans from automated bots online. However, current vision based CAPTCHAs face escalating security risks: traditional attacks contin…
Laugh, Relate, Engage: Stylized Comment Generation for Short Videos
Xuan Ouyang, Senan Wang, Bouzhou Wang +3
Short-video platforms have become a central medium in the modern Internet landscape, where efficient information delivery and strong interactivity are reshaping user engagement and…
Debiasing LLMs by Masking Unfairness-Driving Attention Heads
Tingxu Han, Wei Song, Ziqi Ding +6
Large language models (LLMs) increasingly mediate decisions in domains where unfair treatment of demographic groups is unacceptable. Existing work probes when biased outputs appear…
Help or Hurdle? Rethinking Model Context Protocol-Augmented Large Language Models
Wei Song, Haonan Zhong, Ziqi Ding +2
The Model Context Protocol (MCP) enables large language models (LLMs) to access external resources on demand. While commonly assumed to enhance performance, how LLMs actually lever…
"Pull or Not to Pull?'': Investigating Moral Biases in Leading Large Language Models Across Ethical Dilemmas
Junchen Ding, Penghao Jiang, Zihao Xu +4
As large language models (LLMs) increasingly mediate ethically sensitive decisions, understanding their moral reasoning processes becomes imperative. This study presents a comprehe…
A Rusty Link in the AI Supply Chain: Detecting Evil Configurations in Model Repositories
Ziqi Ding, Qian Fu, Junchen Ding +3
Recent advancements in large language models (LLMs) have spurred the development of diverse AI applications from code generation and video editing to text generation; however, AI s…
Good News for Script Kiddies? Evaluating Large Language Models for Automated Exploit Generation
David Jin, Qian Fu, Yuekang Li
Large Language Models (LLMs) have demonstrated remarkable capabilities in code-related tasks, raising concerns about their potential for automated exploit generation (AEG). This pa…
Socrates or Smartypants: Testing Logic Reasoning Capabilities of Large Language Models with Logic Programming-based Test Oracles
Zihao Xu, Junchen Ding, Yiling Lou +3
Large Language Models (LLMs) have achieved significant progress in language understanding and reasoning. Evaluating and analyzing their logical reasoning abilities has therefore be…