4 citations · 7 across the 16 of their papers we have counts for
16 papers
MOSAIC: Knowledge-Guided CLI Command Composition Attack in LLM Coding Agents
Jiangrong Wu, Huaijin Wang, Yihao Zhang +2
LLM coding agents increasingly complete development tasks by issuing ordinary CLI commands. Following the Unix design, these commands cooperate through shared operating-system stat…
Symbolic-Neural Soft-Logic Reasoning: Towards Robust and Verifiable Thinking Chains via Cooperative Evolution
Rui Wang, Zeming Wei, Yihao Zhang +1
Large Language Models (LLMs) have demonstrated impressive progress in complex reasoning tasks, largely driven by the Chain-of-Thought (CoT) paradigm, which decomposes difficult pro…
VOW: Verifiable and Oblivious Watermark Detection for Large Language Models
Xiaokun Luan, Yihao Zhang, Pengcheng Su +4
Regulation requires generated text to be marked and detectable, but symmetric-key watermarks leave detection to the key-holding provider. We study first-party provenance audit (FPA…
The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems
Yihao Zhang, Kai Wang, Jiangrong Wu +7
Large Language Models (LLMs) face prominent security risks from jailbreaking, a practice that manipulates models to bypass built-in security constraints and generate unethical or u…
AgentWorm: Self-Propagating Attacks Across LLM Agent Ecosystems
Yihao Zhang, Zeming Wei, Xiaokun Luan +7
Autonomous LLM-based agents increasingly operate as long-running processes forming densely interconnected multi-agent ecosystems, whose security properties remain largely unexplore…
RACC: Representation-Aware Coverage Criteria for LLM Safety Testing
Zeming Wei, Zhixin Zhang, Chengcan Wu +3
Large Language Models (LLMs) face severe safety risks from jailbreak attacks, yet current safety testing largely relies on static datasets and lacks systematic criteria to evaluate…
Automata-Based Steering of Large Language Models for Diverse Structured Generation
Xiaokun Luan, Zeming Wei, Yihao Zhang +1
Large language models (LLMs) are increasingly tasked with generating structured outputs. While structured generation methods ensure validity, they often lack output diversity, a cr…
Why Does Weak-OOD Help? A Further Step Towards Understanding Jailbreaking VLMs
Yuxuan Zhou, Yuzhao Peng, Yang Bai +7
Large Vision-Language Models (VLMs) are susceptible to jailbreak attacks: researchers have developed various attack strategies that bypass the safety mechanisms of VLMs. Among thes…
When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models
Kai Wang, Yihao Zhang, Meng Sun
The honesty of large language models (LLMs) is a critical alignment challenge, especially as advanced systems with chain-of-thought (CoT) reasoning may strategically deceive humans…
Secure LLM Fine-Tuning via Safety-Aware Probing
Chengcan Wu, Zhixin Zhang, Zeming Wei +3
Large language models (LLMs) have achieved remarkable success across many applications, but their ability to generate harmful content raises serious safety concerns. Although safet…
MILE: A Mutation Testing Framework of In-Context Learning Systems
Zeming Wei, Yihao Zhang, Meng Sun
In-context Learning (ICL) has achieved notable success in the applications of large language models (LLMs). By adding only a few input-output pairs that demonstrate a new task, the…
Automata Extraction from Transformers
Yihao Zhang, Zeming Wei, Meng Sun
In modern machine (ML) learning systems, Transformer-based architectures have achieved milestone success across a broad spectrum of tasks, yet understanding their operational mecha…
Boosting Jailbreak Attack with Momentum
Yihao Zhang, Zeming Wei
Large Language Models (LLMs) have achieved remarkable success across diverse tasks, yet they remain vulnerable to adversarial attacks, notably the well-known jailbreak attack. In p…
Exploring the Robustness of In-Context Learning with Noisy Labels
Chen Cheng, Xinzhi Yu, Haodong Wen +4
Recently, the mysterious In-Context Learning (ICL) ability exhibited by Transformer architectures, especially in large language models (LLMs), has sparked significant research inte…
Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models
Yihao Zhang, Zeming Wei, Jun Sun +1
Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent iss…
On the Duality Between Sharpness-Aware Minimization and Adversarial Training
Yihao Zhang, Hangzhou He, Jingyu Zhu +3
Adversarial Training (AT), which adversarially perturb the input samples during training, has been acknowledged as one of the most effective defenses against adversarial attacks, y…