14 citations · 25 across the 15 of their papers we have counts for
15 papers · 1 filter
LongPIBench: A Long-Context Benchmark for Prompt Injection
Yupei Liu, Yuqi Jia, Neil Zhenqiang Gong +1
Prompt injection attacks pose a serious security risk to large language models in real-world applications. However, existing prompt injection benchmarks primarily focus on short-co…
Agent Against Agent: An Agentic System for Automatic Prompt Injection Red Teaming
Yanting Wang, Chenlong Yin, Runpeng Geng +1
Prompt injection poses significant security risks to LLM agents. Efficient and effective red-teaming is therefore critical, both for evaluating these risks and for collecting train…
PIArena: A Platform for Prompt Injection Evaluation
Runpeng Geng, Chenlong Yin, Yanting Wang +2
Prompt injection attacks pose serious security risks across a wide range of real-world applications. While receiving increasing attention, the community faces a critical gap: the l…
TRUSTDESC: Preventing Tool Poisoning in LLM Applications via Trusted Description Generation
Hengkai Ye, Zhechang Zhang, Jinyuan Jia +1
Large language models (LLMs) increasingly rely on external tools to perform time-sensitive tasks and real-world actions. While tool integration expands LLM capabilities, it also in…
EnsembleSHAP: Faithful and Certifiably Robust Attribution for Random Subspace Method
Yanting Wang, Jinyuan Jia
Random subspace method has wide security applications such as providing certified defenses against adversarial and backdoor attacks, and building robustly aligned LLM against jailb…
PromptLocate: Localizing Prompt Injection Attacks
Yuqi Jia, Yupei Liu, Zedian Shao +2
Prompt injection attacks deceive a large language model into completing an attacker-specified task instead of its intended task by contaminating its input data with an injected pro…
PIShield: Detecting Prompt Injection Attacks via Intrinsic LLM Features
Wei Zou, Yupei Liu, Yanting Wang +3
LLM-integrated applications are vulnerable to prompt injection attacks, where an attacker contaminates the input to inject malicious instructions, causing the LLM to follow the att…
SecInfer: Preventing Prompt Injection via Inference-time Scaling
Yupei Liu, Yanting Wang, Yuqi Jia +2
Prompt injection attacks pose a pervasive threat to the security of Large Language Models (LLMs). State-of-the-art prevention-based defenses typically rely on fine-tuning an LLM to…
A Critical Evaluation of Defenses against Prompt Injection Attacks
Yuqi Jia, Zedian Shao, Yupei Liu +3
Large Language Models (LLMs) are vulnerable to prompt injection attacks, and several defenses have recently been proposed, often claiming to mitigate these attacks successfully. Ho…
DataSentinel: A Game-Theoretic Detection of Prompt Injection Attacks
Yupei Liu, Yuqi Jia, Jinyuan Jia +2
LLM-integrated applications and agents are vulnerable to prompt injection attacks, where an attacker injects prompts into their inputs to induce attacker-desired outputs. A detecti…
TrojanDec: Data-free Detection of Trojan Inputs in Self-supervised Learning
Yupei Liu, Yanting Wang, Jinyuan Jia
An image encoder pre-trained by self-supervised learning can be used as a general-purpose feature extractor to build downstream classifiers for various downstream tasks. However, m…
Evaluating LLM-based Personal Information Extraction and Countermeasures
Yupei Liu, Yuqi Jia, Jinyuan Jia +1
Automatically extracting personal information -- such as name, phone number, and email address -- from publicly available profiles at a large scale is a stepstone to many other sec…
Certifiably Robust Image Watermark
Zhengyuan Jiang, Moyang Guo, Yuepeng Hu +2
Generative AI raises many societal concerns such as boosting disinformation and propaganda campaigns. Watermarking AI-generated content is a key technology to address these concern…
Formalizing and Benchmarking Prompt Injection Attacks and Defenses
Yupei Liu, Yuqi Jia, Runpeng Geng +2
A prompt injection attack aims to inject malicious instruction/data into the input of an LLM-Integrated Application such that it produces results as an attacker desires. Existing w…
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients
Zaixi Zhang, Xiaoyu Cao, Jinyuan Jia +1
Federated learning (FL) is vulnerable to model poisoning attacks, in which malicious clients corrupt the global model via sending manipulated model updates to the server. Existing…