AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents

AgentDojo is an open-source benchmarking framework developed by ETH Zurich researchers to evaluate the security of LLM-based agents against prompt injection attacks and various defense mechanisms. It provides a dynamic environment where agents perform tasks across different suites while being subjected to attacks (e.g., malicious instructions hidden in tool outputs or web content). The framework supports testing multiple defenses, such as tool filtering or input sanitization, and includes a benchmark script for running evaluations with different LLM models. Results are published in a dedicated registry. The project is designed to be extensible and is accompanied by a research paper presented at NeurIPS 2024. It can be installed via pip and includes documentation for setup and usage. 

https://github.com/ethz-spylab/agentdojo

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