The official implementation of the paper "AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?"
See the codeHao Li, Ruoyao Wen, Shanghao Shi, Ning Zhang, Yevgeniy Vorobeychik, Chaowei Xiao.
The official implementation of the paper "AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?".
AgentDyn is a dynamic, open-ended agent security benchmark featuring 60 challenging open-ended user tasks and 560 injection test cases across the Shopping, GitHub, and Daily Life scenarios. It is built on top of the AgentDojo framework. A huge thanks to the AgentDojo team for their admirable contribution to the community!
pip install -e .
For adaptability, we support an evaluation script same as AgentDojo's. Documentation on how to use the script can be obtained with the --help flag.
For example, to run the shopping suite , with gpt-4o-2024-08-06 as the LLM, the tool filter as a defense, and the attack with important_instructions, run the following command:
python -m agentdojo.scripts.benchmark -s shopping \
--model GPT_4O_2024_08_06 \
--defense tool_filter --attack important_instructions
To run with external defenses integrated in this repo layout, you can directly use:
Before running, please export your API key, through:
AgentDyn supports shopping,github, and dailylife suites, as well as the original four suites from AgentDojo (banking,slack, travel and workspace).
We evaluate the following models in our paper: GPT_4O_MINI_2024_07_18, GPT_4O_2024_08_06, GEMINI_2_5_FLASH, GEMINI_2_5_PRO, LLAMA_3_3_70B, QWEN3_235B, GPT_5_1_2025_11_13, GPT_5_MINI_2025_08_07.
Other models supported by AgentDojo are also compatible.
In addition to the original defenses in AgentDojo, we provide support for PIGuard and PromptGuard2. We also support directly invoking external defenses in this workspace: CaMeL, Progent, DRIFT,.
The complete list of defenses supported in our paper includes: repeat_user_prompt, spotlighting_with_delimiting, tool_filter, transformers_pi_detector, piguard_detector, prompt_guard_2_detector, camel, progent, drift.
To review the results reported in our paper, please refer to the log files in the (runs/).
If you find this work useful in your research or applications, we appreciate that if you can kindly cite:
@articles{AgentDyn,
title={AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?},
author={Hao Li and Ruoyao Wen and Shanghao Shi and Ning Zhang and Yevgeniy Vorobeychik and Chaowei Xiao},
journal = {arXiv},
eprint = {2602.03117},
year={2026}
}
Python
56.3%
Jupyter Notebook
43.3%
The official implementation of the paper "AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?"
See the codeHao Li, Ruoyao Wen, Shanghao Shi, Ning Zhang, Yevgeniy Vorobeychik, Chaowei Xiao.
The official implementation of the paper "AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?".
AgentDyn is a dynamic, open-ended agent security benchmark featuring 60 challenging open-ended user tasks and 560 injection test cases across the Shopping, GitHub, and Daily Life scenarios. It is built on top of the AgentDojo framework. A huge thanks to the AgentDojo team for their admirable contribution to the community!
pip install -e .
For adaptability, we support an evaluation script same as AgentDojo's. Documentation on how to use the script can be obtained with the --help flag.
For example, to run the shopping suite , with gpt-4o-2024-08-06 as the LLM, the tool filter as a defense, and the attack with important_instructions, run the following command:
python -m agentdojo.scripts.benchmark -s shopping \
--model GPT_4O_2024_08_06 \
--defense tool_filter --attack important_instructions
To run with external defenses integrated in this repo layout, you can directly use:
Before running, please export your API key, through:
AgentDyn supports shopping,github, and dailylife suites, as well as the original four suites from AgentDojo (banking,slack, travel and workspace).
We evaluate the following models in our paper: GPT_4O_MINI_2024_07_18, GPT_4O_2024_08_06, GEMINI_2_5_FLASH, GEMINI_2_5_PRO, LLAMA_3_3_70B, QWEN3_235B, GPT_5_1_2025_11_13, GPT_5_MINI_2025_08_07.
Other models supported by AgentDojo are also compatible.
In addition to the original defenses in AgentDojo, we provide support for PIGuard and PromptGuard2. We also support directly invoking external defenses in this workspace: CaMeL, Progent, DRIFT,.
The complete list of defenses supported in our paper includes: repeat_user_prompt, spotlighting_with_delimiting, tool_filter, transformers_pi_detector, piguard_detector, prompt_guard_2_detector, camel, progent, drift.
To review the results reported in our paper, please refer to the log files in the (runs/).
If you find this work useful in your research or applications, we appreciate that if you can kindly cite:
@articles{AgentDyn,
title={AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?},
author={Hao Li and Ruoyao Wen and Shanghao Shi and Ning Zhang and Yevgeniy Vorobeychik and Chaowei Xiao},
journal = {arXiv},
eprint = {2602.03117},
year={2026}
}
Python
56.3%
Jupyter Notebook
43.3%