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

Edoardo Debenedetti¹, Jie Zhang¹, Mislav Balunović^1,2,
Luca Beurer-Kellner^1,2, Marc Fischer^1,2, Florian Tramèr¹

¹ETH Zurich and ²Invariant Labs

Paper | Results.

Quickstart

pip install agentdojo

Warning

Note that the API of the package is still under development and might change in the future.

If you want to use the PI detector, you need to install the transformers extra:

pip install "agentdojo[transformers]"

FAQ

How do I run the benchmark?

To run the benchmark, you first need to install the package. Once this is done, the benchmark can be run with the benchmark script. Documentation on how to use the script can be obtained with the --help flag.

For example, to run the workspace suite on the tasks 0 and 1, with gpt-4o-2024-05-13 as the LLM, the tool filter as a defense, and the attack with tool knowlege, run the following command:

python -m agentdojo.scripts.benchmark -s workspace -ut user_task_0 \
    -ut user_task_1 --model gpt-4o-2024-05-13 \
    --defense tool_filter --attack tool_knowledge

To run the above, but on all suites and tasks, run the following:

python -m agentdojo.scripts.benchmark --model gpt-4o-2024-05-13 \
    --defense tool_filter --attack tool_knowledge

How do I create a new defense or model?

Check out the documentation on how to create your own pipeline.

How do I create a new attack?

Check out the documentation on attacks.

How can I use the Dojo to create my own benchmark?

Take a look at our in-depth documentation on how to create a new task suite.

Development set-up

Take a look at the development set-up docs.

Citing

If you use AgentDojo in your research, please consider citing our paper:

@inproceedings{
   debenedetti2024agentdojo,
   title={AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for {LLM} Agents},
   author={Edoardo Debenedetti and Jie Zhang and Mislav Balunovic and Luca Beurer-Kellner and Marc Fischer and Florian Tram{\`e}r},
   booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
   year={2024},
   url={https://openreview.net/forum?id=m1YYAQjO3w}
}