17 citations · 22 across the 6 of their papers we have counts for
7 papers
The Imitation Game: Using Large Language Models as Chatbots to Combat Chat-Based Cybercrimes
Yifan Yao, Baojuan Wang, Jinhao Duan +4
Chat-based cybercrime has emerged as a pervasive threat, with attackers leveraging real-time messaging platforms to conduct scams that rely on trust-building, deception, and psycho…
Meta-SecAlign: Training LLMs against Prompt Injection for Robust Agents
Sizhe Chen, Arman Zharmagambetov, David Wagner +1
Prompt injection attacks, where untrusted data contains an injected prompt to manipulate the system, have been listed as the top security threat to AI agents. By fine-tuning on sim…
WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks
Ivan Evtimov, Arman Zharmagambetov, Aaron Grattafiori +2
Autonomous UI agents powered by AI have tremendous potential to boost human productivity by automating routine tasks such as filing taxes and paying bills. However, a major challen…
RAFP: Identifying LLM Lineages via Rare-Region Fingerprints
Yun-Yun Tsai, Jia Hao Liang, Chuan Guo +2
Large language models (LLMs) are increasingly released under restricted licenses, creating a growing need for robust model ownership verification. Existing fingerprinting methods a…
AdvPrefix: An Objective for Nuanced LLM Jailbreaks
Sicheng Zhu, Brandon Amos, Yuandong Tian +2
Many jailbreak attacks on large language models (LLMs) rely on a common objective: making the model respond with the prefix ``Sure, here is (harmful request)''. While straightforwa…
SecAlign: Defending Against Prompt Injection with Preference Optimization
Sizhe Chen, Arman Zharmagambetov, Saeed Mahloujifar +3
Large language models (LLMs) are becoming increasingly prevalent in modern software systems, interfacing between the user and the Internet to assist with tasks that require advance…
AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs
Anselm Paulus, Arman Zharmagambetov, Chuan Guo +2
Large Language Models (LLMs) are vulnerable to jailbreaking attacks that lead to generation of inappropriate or harmful content. Manual red-teaming requires a time-consuming search…