66 citations · 77 across the 19 of their papers we have counts for
19 papers
Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference
Xuyan Ma, Yawen Wang, Junjie Wang +5
Platform-orchestrated agentic workflows have become a popular paradigm for developing LLM-based applications. However, their reliability remains a major challenge due to the uncert…
Fall into a Pit, Gain in a Wit: Cognitive-Guided Harmful Meme Detection via Misjudgment Risk Pattern Retrieval
Wenshuo Wang, Ziyou Jiang, Junjie Wang +6
Internet memes have emerged as a popular multimodal medium, yet they are increasingly weaponized to convey harmful opinions through subtle rhetorical devices like irony and metapho…
VulRTex: A Reasoning-Guided Approach to Identify Vulnerabilities from Rich-Text Issue Report
Ziyou Jiang, Mingyang Li, Guowei Yang +2
Software vulnerabilities exist in open-source software (OSS), and the developers who discover these vulnerabilities may submit issue reports (IRs) to describe their details. Securi…
Demystifying the Lifecycle of Failures in Platform-Orchestrated Agentic Workflows
Xuyan Ma, Xiaofei Xie, Yawen Wang +4
Agentic workflows built on low-code orchestration platforms enable rapid development of multi-agent systems, but they also introduce new and poorly understood failure modes that hi…
Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems
Haowei Wang, Rupeng Zhang, Junjie Wang +4
Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by retrieving relevant documents from external corpora before generating responses. This approach…
AdInject: Real-World Black-Box Attacks on Web Agents via Advertising Delivery
Haowei Wang, Junjie Wang, Xiaojun Jia +5
Vision-Language Model (VLM) based Web Agents represent a significant step towards automating complex tasks by simulating human-like interaction with websites. However, their deploy…
One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems
Zhiyuan Chang, Mingyang Li, Xiaojun Jia +5
Large Language Models (LLMs) enhanced with Retrieval-Augmented Generation (RAG) have shown improved performance in generating accurate responses. However, the dependence on externa…
Mimicking the Familiar: Dynamic Command Generation for Information Theft Attacks in LLM Tool-Learning System
Ziyou Jiang, Mingyang Li, Guowei Yang +4
Information theft attacks pose a significant risk to Large Language Model (LLM) tool-learning systems. Adversaries can inject malicious commands through compromised tools, manipula…
What External Knowledge is Preferred by LLMs? Characterizing and Exploring Chain of Evidence in Imperfect Context for Multi-Hop QA
Zhiyuan Chang, Mingyang Li, Xiaojun Jia +5
Incorporating external knowledge has emerged as a promising way to mitigate outdated knowledge and hallucinations in LLM. However, external knowledge is often imperfect, encompassi…
From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial Injection
Haowei Wang, Rupeng Zhang, Junjie Wang +4
Tool-calling has changed Large Language Model (LLM) applications by integrating external tools, significantly enhancing their functionality across diverse tasks. However, this inte…
CodePurify: Defend Backdoor Attacks on Neural Code Models via Entropy-based Purification
Fangwen Mu, Junjie Wang, Zhuohao Yu +4
Neural code models have found widespread success in tasks pertaining to code intelligence, yet they are vulnerable to backdoor attacks, where an adversary can manipulate the victim…
Adversarial Robustness of Open-source Text Classification Models and Fine-Tuning Chains
Hao Qin, Mingyang Li, Junjie Wang +1
Context:With the advancement of artificial intelligence (AI) technology and applications, numerous AI models have been developed, leading to the emergence of open-source model host…
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…
A Roadmap for Software Testing in Open Collaborative Development Environments
Qing Wang, Junjie Wang, Mingyang Li +2
Amidst the ever-expanding digital sphere, the evolution of the Internet has not only fostered an atmosphere of information transparency and sharing but has also sparked a revolutio…
VEglue: Testing Visual Entailment Systems via Object-Aligned Joint Erasing
Zhiyuan Chang, Mingyang Li, Junjie Wang +2
Visual entailment (VE) is a multimodal reasoning task consisting of image-sentence pairs whereby a promise is defined by an image, and a hypothesis is described by a sentence. The…
Adversarial Testing for Visual Grounding via Image-Aware Property Reduction
Zhiyuan Chang, Mingyang Li, Junjie Wang +4
Due to the advantages of fusing information from various modalities, multimodal learning is gaining increasing attention. Being a fundamental task of multimodal learning, Visual Gr…
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…
Putting Them under Microscope: A Fine-Grained Approach for Detecting Redundant Test Cases in Natural Language
Zhiyuan Chang, Mingyang Li, Junjie Wang +2
Natural language (NL) documentation is the bridge between software managers and testers, and NL test cases are prevalent in system-level testing and other quality assurance activit…
Cutting Away the Confusion From Crowdtesting
Junjie Wang, Mingyang Li, Song Wang +2
Crowdtesting is effective especially when it comes to the feedback on GUI systems, or subjective opinions about features. Despite of this, we find crowdtesting reports are highly r…