95 citations · 118 across the 21 of their papers we have counts for
3 papers · 1 filter
How Vulnerable Are AI Agents to Indirect Prompt Injections? Insights from a Large-Scale Public Competition
Mateusz Dziemian, Maxwell Lin, Xiaohan Fu +28
LLM based agents are increasingly deployed in high stakes settings where they process external data sources such as emails, documents, and code repositories. This creates exposure…
Breaking Agent Backbones: Evaluating the Security of Backbone LLMs in AI Agents
Julia Bazinska, Max Mathys, Francesco Casucci +4
AI agents powered by large language models (LLMs) are being deployed at scale, yet we lack a systematic understanding of how the choice of backbone LLM affects agent security. The…
SeCodePLT: A Unified Platform for Evaluating the Security of Code GenAI
Yuzhou Nie, Zhun Wang, Yu Yang +7
Existing benchmarks for evaluating the security risks and capabilities (e.g., vulnerability detection) of code-generating large language models (LLMs) face several key limitations:…