1 citations · 1 across the 4 of their papers we have counts for
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EvoSafeHarness: Evolving Model- and Domain-Specific Harnesses for Securing Agents
Nanxi Li, Yingzi Ma, Yulong Cao +4
Large Language Model (LLM) agents are turning language into real-world effects, making safety necessary against both indirect prompt injections and direct harmful requests. System-…
Mitigating Indirect Prompt Injection via Instruction-Following Intent Analysis
Mintong Kang, Chong Xiang, Sanjay Kariyappa +3
Indirect prompt injection attacks (IPIAs), where large language models (LLMs) follow malicious instructions hidden in input data, pose a critical threat to LLM-powered agents. In t…
SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning
Kaiwen Zhou, Ahmed Elgohary, A S M Iftekhar +1
The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring…
FATH: Authentication-based Test-time Defense against Indirect Prompt Injection Attacks
Jiongxiao Wang, Fangzhou Wu, Wendi Li +5
Large language models (LLMs) have been widely deployed as the backbone with additional tools and text information for real-world applications. However, integrating external informa…
AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs
Xiaogeng Liu, Peiran Li, Edward Suh +7
In this paper, we propose AutoDAN-Turbo, a black-box jailbreak method that can automatically discover as many jailbreak strategies as possible from scratch, without any human inter…