collaborators

5 papers

cs.CR2025

CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities

Yuxuan Zhu, Antony Kellermann, Dylan Bowman +13

Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlight…

cs.MA2025

Teams of LLM Agents can Exploit Zero-Day Vulnerabilities

Yuxuan Zhu, Antony Kellermann, Akul Gupta +4

LLM agents have become increasingly sophisticated, especially in the realm of cybersecurity. Researchers have shown that LLM agents can exploit real-world vulnerabilities when give…

cs.CR2025

Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents

Qiusi Zhan, Richard Fang, Henil Shalin Panchal +1

Large Language Model (LLM) agents exhibit remarkable performance across diverse applications by using external tools to interact with environments. However, integrating external to…

cs.AI2024

Voice-Enabled AI Agents can Perform Common Scams

Richard Fang, Dylan Bowman, Daniel Kang

Recent advances in multi-modal, highly capable LLMs have enabled voice-enabled AI agents. These agents are enabling new applications, such as voice-enabled autonomous customer serv…

cs.CR2024

LLM Agents can Autonomously Exploit One-day Vulnerabilities

Richard Fang, Rohan Bindu, Akul Gupta +1

LLMs have becoming increasingly powerful, both in their benign and malicious uses. With the increase in capabilities, researchers have been increasingly interested in their ability…