1 citations · 1 across the 10 of their papers we have counts for
13 papers
Agent Skills Matter: Inferring Proprietary Skills from Execution Trajectories
Jianing Geng, Ruiqi He, Zekun Fei +6
Agent skills package reusable procedures that improve downstream performance. Their lightweight, portable form enables marketplace monetization and private deployment behind cloud-…
Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
Baolei Zhang, Minghong Fang, Zhuqing Liu +5
Federated Learning (FL) allows multiple clients to collaboratively train a model without sharing their private data. However, FL is vulnerable to Byzantine attacks, where adversari…
Who Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented Generation
Baolei Zhang, Haoran Xin, Yuxi Chen +6
Retrieval-Augmented Generation (RAG) integrates external knowledge into large language models to improve response quality. However, recent work has shown that RAG systems are highl…
Gradient Surgery for Safe LLM Fine-Tuning
Biao Yi, Jiahao Li, Baolei Zhang +4
Fine-tuning-as-a-Service introduces a critical vulnerability where a few malicious examples mixed into the user's fine-tuning dataset can compromise the safety alignment of Large L…
Transform Before You Query: A Privacy-Preserving Approach for Vector Retrieval with Embedding Space Alignment
Ruiqi He, Zekun Fei, Jiaqi Li +5
Vector Database (VDB) can efficiently index and search high-dimensional vector embeddings from unstructured data, crucially enabling fast semantic similarity search essential for m…
BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit
Biao Yi, Zekun Fei, Jianing Geng +4
Large reasoning models (LRMs) have emerged as a significant advancement in artificial intelligence, representing a specialized class of large language models (LLMs) designed to tac…
Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models
Biao Yi, Tiansheng Huang, Sishuo Chen +4
Backdoor unalignment attacks against Large Language Models (LLMs) enable the stealthy compromise of safety alignment using a hidden trigger while evading normal safety auditing. Th…
CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning
Biao Yi, Tiansheng Huang, Baolei Zhang +4
Fine-tuning-as-a-service, while commercially successful for Large Language Model (LLM) providers, exposes models to harmful fine-tuning attacks. As a widely explored defense paradi…
Hiding in Plain Sight: A Steganographic Approach to Stealthy LLM Jailbreaks
Jianing Geng, Biao Yi, Zekun Fei +4
Jailbreak attacks pose a serious threat to Large Language Models (LLMs) by bypassing their safety mechanisms. A truly advanced jailbreak is defined not only by its effectiveness bu…
Traceback of Poisoning Attacks to Retrieval-Augmented Generation
Baolei Zhang, Haoran Xin, Minghong Fang +4
Large language models (LLMs) integrated with retrieval-augmented generation (RAG) systems improve accuracy by leveraging external knowledge sources. However, recent research has re…
Your Semantic-Independent Watermark is Fragile: A Semantic Perturbation Attack against EaaS Watermark
Zekun Fei, Biao Yi, Jianing Geng +3
Embedding-as-a-Service (EaaS) has emerged as a successful business pattern but faces significant challenges related to various forms of copyright infringement, particularly, the AP…
Prompt-Guided Internal States for Hallucination Detection of Large Language Models
Fujie Zhang, Peiqi Yu, Biao Yi +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of tasks in different domains. However, they sometimes generate responses that are logically…
BadActs: A Universal Backdoor Defense in the Activation Space
Biao Yi, Sishuo Chen, Yiming Li +3
Backdoor attacks pose an increasingly severe security threat to Deep Neural Networks (DNNs) during their development stage. In response, backdoor sample purification has emerged as…