2 citations · 6 across the 19 of their papers we have counts for
15 papers · 1 filter
CoRedSkill: Stealthy Skill Composition Attacks via AST-Guided Injection
Qichen Zhao, Shengfang Zhai, Qingni Shen +2
Agents extend their capabilities through reusable skills that package instructions, executable code, and resources. Composing these skills enables complex task workflows, but their…
Securing LLM Agents Need Intent-to-Execution Integrity
Wenjie Qu, Ming Xu, Peiran Wang +3
This position paper argues that securing LLM agents requires first defining an end-to-end correctness property that specifies when an agent's execution faithfully reflects the user…
BadDLM: Backdooring Diffusion Language Models with Diverse Targets
Shengfang Zhai, Xiaoyang Ji, Yuling Shi +6
Diffusion language models (DLMs) have recently emerged as an alternative modeling paradigm to autoregressive (AR) language models, enabling parallel generation and bidirectional co…
Purify Once, Edit Freely: Breaking Image Protections under Model Mismatch
Qichen Zhao, Shengfang Zhai, Xinjian Bai +4
Diffusion models enable high-fidelity image editing but can also be misused for unauthorized style imitation and harmful content generation. To mitigate these risks, proactive imag…
IMMACULATE: A Practical LLM Auditing Framework via Verifiable Computation
Yanpei Guo, Wenjie Qu, Linyu Wu +7
Commercial large language models are typically deployed as black-box API services, requiring users to trust providers to execute inference correctly and report token usage honestly…
MemPot: Defending Against Memory Extraction Attack with Optimized Honeypots
Yuhao Wang, Shengfang Zhai, Guanghao Jin +3
Large Language Model (LLM)-based agents employ external and internal memory systems to handle complex, goal-oriented tasks, yet this exposes them to severe extraction attacks, and…
Silent Leaks: Implicit Knowledge Extraction Attack on RAG Systems through Benign Queries
Yuhao Wang, Wenjie Qu, Shengfang Zhai +5
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by incorporating external knowledge bases, but this may expose them to extraction attacks, leading…
Life-Cycle Routing Vulnerabilities of LLM Router
Qiqi Lin, Xiaoyang Ji, Shengfang Zhai +4
Large language models (LLMs) have achieved remarkable success in natural language processing, yet their performance and computational costs vary significantly. LLM routers play a c…
Efficient Input-level Backdoor Defense on Text-to-Image Synthesis via Neuron Activation Variation
Shengfang Zhai, Jiajun Li, Yue Liu +7
In recent years, text-to-image (T2I) diffusion models have gained significant attention for their ability to generate high quality images reflecting text prompts. However, their gr…
GuardReasoner: Towards Reasoning-based LLM Safeguards
Yue Liu, Hongcheng Gao, Shengfang Zhai +9
As LLMs increasingly impact safety-critical applications, ensuring their safety using guardrails remains a key challenge. This paper proposes GuardReasoner, a new safeguard for LLM…
Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy
Shengfang Zhai, Huanran Chen, Yinpeng Dong +5
Text-to-image diffusion models have achieved tremendous success in the field of controllable image generation, while also coming along with issues of privacy leakage and data copyr…
Discovering Universal Semantic Triggers for Text-to-Image Synthesis
Shengfang Zhai, Weilong Wang, Jiajun Li +3
Recently text-to-image models have gained widespread attention in the community due to their controllable and high-quality generation ability. However, the robustness of such model…
Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning
Shengfang Zhai, Yinpeng Dong, Qingni Shen +3
With the help of conditioning mechanisms, the state-of-the-art diffusion models have achieved tremendous success in guided image generation, particularly in text-to-image synthesis…
NCL: Textual Backdoor Defense Using Noise-augmented Contrastive Learning
Shengfang Zhai, Qingni Shen, Xiaoyi Chen +4
At present, backdoor attacks attract attention as they do great harm to deep learning models. The adversary poisons the training data making the model being injected with a backdoo…
Kallima: A Clean-label Framework for Textual Backdoor Attacks
Xiaoyi Chen, Yinpeng Dong, Zeyu Sun +3
Although Deep Neural Network (DNN) has led to unprecedented progress in various natural language processing (NLP) tasks, research shows that deep models are extremely vulnerable to…