14 citations · 26 across the 11 of their papers we have counts for
11 papers
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…
AgentWatcher: A Rule-based Prompt Injection Monitor
Yanting Wang, Wei Zou, Runpeng Geng +1
Large language models (LLMs) and their applications, such as agents, are highly vulnerable to prompt injection attacks. State-of-the-art prompt injection detection methods have the…
PISmith: Reinforcement Learning-based Red Teaming for Prompt Injection Defenses
Chenlong Yin, Runpeng Geng, Yanting Wang +1
Prompt injection poses serious security risks to real-world LLM applications, particularly autonomous agents. Although many defenses have been proposed, their robustness against ad…
TASO: Jailbreak LLMs via Alternative Template and Suffix Optimization
Yanting Wang, Runpeng Geng, Jinghui Chen +2
Many recent studies showed that LLMs are vulnerable to jailbreak attacks, where an attacker can perturb the input of an LLM to induce it to generate an output for a harmful questio…
PISanitizer: Preventing Prompt Injection to Long-Context LLMs via Prompt Sanitization
Runpeng Geng, Yanting Wang, Chenlong Yin +3
Long context LLMs are vulnerable to prompt injection, where an attacker can inject an instruction in a long context to induce an LLM to generate an attacker-desired output. Existin…
UniC-RAG: Universal Knowledge Corruption Attacks to Retrieval-Augmented Generation
Runpeng Geng, Yanting Wang, Ying Chen +1
Retrieval-augmented generation (RAG) systems are widely deployed in real-world applications in diverse domains such as finance, healthcare, and cybersecurity. However, many studies…
AttnTrace: Contextual Attribution of Prompt Injection and Knowledge Corruption
Yanting Wang, Runpeng Geng, Ying Chen +1
Long-context large language models (LLMs), such as Gemini-2.5-Pro and Claude-Sonnet-4, are increasingly used to empower advanced AI systems, including retrieval-augmented generatio…
TracLLM: A Generic Framework for Attributing Long Context LLMs
Yanting Wang, Wei Zou, Runpeng Geng +1
Long context large language models (LLMs) are deployed in many real-world applications such as RAG, agent, and broad LLM-integrated applications. Given an instruction and a long co…
PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models
Wei Zou, Runpeng Geng, Binghui Wang +1
Large language models (LLMs) have achieved remarkable success due to their exceptional generative capabilities. Despite their success, they also have inherent limitations such as a…
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…