43 citations · 74 across the 17 of their papers we have counts for
18 papers
ICON: Intent-Context Coupling for Efficient Multi-Turn Jailbreak Attack
Xingwei Lin, Wenhao Lin, Sicong Cao +4
Multi-turn jailbreak attacks have emerged as a critical threat to Large Language Models (LLMs), bypassing safety mechanisms by progressively constructing adversarial contexts from…
CSCBench: A PVC Diagnostic Benchmark for Commodity Supply Chain Reasoning
Yaxin Cui, Yuanqiang Zeng, Jiapeng Yan +8
Large Language Models (LLMs) have achieved remarkable success in general benchmarks, yet their competence in commodity supply chains (CSCs) -- a domain governed by institutional ru…
GPO: Learning from Critical Steps to Improve LLM Reasoning
Jiahao Yu, Zelei Cheng, Xian Wu +1
Large language models (LLMs) are increasingly used in various domains, showing impressive potential on different tasks. Recently, reasoning LLMs have been proposed to improve the \…
Building Coding Agents via Entropy-Enhanced Multi-Turn Preference Optimization
Jiahao Yu, Zelei Cheng, Xian Wu +1
Software engineering presents complex, multi-step challenges for Large Language Models (LLMs), requiring reasoning over large codebases and coordinated tool use. The difficulty of…
Locus: Agentic Predicate Synthesis for Directed Fuzzing
Jie Zhu, Chihao Shen, Ziyang Li +3
Directed fuzzing aims to find program inputs that lead to specified target program states. It has broad applications, such as debugging system crashes, confirming reported bugs, an…
BandFuzz: An ML-powered Collaborative Fuzzing Framework
Wenxuan Shi, Hongwei Li, Jiahao Yu +3
Collaborative fuzzing combines multiple individual fuzzers and dynamically chooses appropriate combinations for different programs. Unlike individual fuzzers that rely on specific…
The Illusion of Role Separation: Hidden Shortcuts in LLM Role Learning (and How to Fix Them)
Zihao Wang, Yibo Jiang, Jiahao Yu +1
Large language models (LLMs) that integrate multiple input roles (e.g., system instructions, user queries, external tool outputs) are increasingly prevalent in practice. Ensuring t…
A Survey on Explainable Deep Reinforcement Learning
Zelei Cheng, Jiahao Yu, Xinyu Xing
Deep Reinforcement Learning (DRL) has achieved remarkable success in sequential decision-making tasks across diverse domains, yet its reliance on black-box neural architectures hin…
Soft-Label Integration for Robust Toxicity Classification
Zelei Cheng, Xian Wu, Jiahao Yu +3
Toxicity classification in textual content remains a significant problem. Data with labels from a single annotator fall short of capturing the diversity of human perspectives. Ther…
UTF:Undertrained Tokens as Fingerprints A Novel Approach to LLM Identification
Jiacheng Cai, Jiahao Yu, Yangguang Shao +1
Fingerprinting large language models (LLMs) is essential for verifying model ownership, ensuring authenticity, and preventing misuse. Traditional fingerprinting methods often requi…
BlockScan: Detecting Anomalies in Blockchain Transactions
Jiahao Yu, Xian Wu, Hao Liu +2
We propose BlockScan, a customized Transformer for anomaly detection in blockchain transactions. Unlike existing methods that rely on rule-based systems or directly apply off-the-s…
PROMPTFUZZ: Harnessing Fuzzing Techniques for Robust Testing of Prompt Injection in LLMs
Jiahao Yu, Yangguang Shao, Hanwen Miao +1
Large Language Models (LLMs) have gained widespread use in various applications due to their powerful capability to generate human-like text. However, prompt injection attacks, whi…
RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation
Zelei Cheng, Xian Wu, Jiahao Yu +3
Deep reinforcement learning (DRL) is playing an increasingly important role in real-world applications. However, obtaining an optimally performing DRL agent for complex tasks, espe…
Mind the Inconspicuous: Revealing the Hidden Weakness in Aligned LLMs' Refusal Boundaries
Jiahao Yu, Haozheng Luo, Jerry Yao-Chieh Hu +3
Recent advances in Large Language Models (LLMs) have led to impressive alignment where models learn to distinguish harmful from harmless queries through supervised finetuning (SFT)…
Assessing Prompt Injection Risks in 200+ Custom GPTs
Jiahao Yu, Yuhang Wu, Dong Shu +3
In the rapidly evolving landscape of artificial intelligence, ChatGPT has been widely used in various applications. The new feature - customization of ChatGPT models by users to ca…
GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts
Jiahao Yu, Xingwei Lin, Zheng Yu +1
Large language models (LLMs) have recently experienced tremendous popularity and are widely used from casual conversations to AI-driven programming. However, despite their consider…
Hybrid CNN -Interpreter: Interpret local and global contexts for CNN-based Models
Wenli Yang, Guan Huang, Renjie Li +4
Convolutional neural network (CNN) models have seen advanced improvements in performance in various domains, but lack of interpretability is a major barrier to assurance and regula…
Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization
Shaofeng Li, Minhui Xue, Benjamin Zi Hao Zhao +2
Deep neural networks (DNNs) have been proven vulnerable to backdoor attacks, where hidden features (patterns) trained to a normal model, which is only activated by some specific in…