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Tracking AI capabilities in cybersecurity is essential for understanding emerging impacts and risks. Our Frontier AI Cybersecurity Observatory provides a centralized platform that aggregates relevant benchmarks, enabling the community to more easily monitor and assess the evolving cybersecurity capabilities of AI systems.
## Submit your benchmark
Please follow the steps below to add your benchmark.
1. First you need to add your results in results.json. Under the top-level "results" key, you need to insert an entry that looks like this:
```jsonc
"Your Benchmark Name": {
"Metric Name 1": {
"Model / Agent Name": [value]
},
"Metric Name 2": {
"Model / Agent Name": [value]
}
}
```
Here, if you want, you can add multiple metric scores.
2. Then, add descriptive metadata in meta_data.py
```bash
LEADERBOARD_MD["Your Benchmark Name"] = """
Brief description of what the benchmark measures.
Paper: <paper URL>
Code: <repository URL>
"""
```
3. Lastly, please open a pull request. You need to commit your changes and open a PR against this repository. We will review and merge submissions. If you have any questions, please contact Yujin Potter at yujinyujin9393@gmail.com.
## Paper & Blog
Paper: https://arxiv.org/abs/2504.05408
Blog: https://rdi.berkeley.edu/frontier-ai-impact-on-cybersecurity/
## Survey
We're also launching an expert survey on this topic. We invite all AI and security researchers and practitioners to take the survey here: https://berkeley.qualtrics.com/jfe/form/SV_3Ozd2BPCEvRea1w
## Citation
Please consider to cite the report if the resource is useful to your research:
```BibTex
@article{guo2025sok,
title={{Frontier AI's Impact on the Cybersecurity Landscape}},
author={Guo, Wenbo and Potter, Yujin and Shi, Tianneng and Wang, Zhun and Zhang, Andy and Song, Dawn},
journal={arXiv preprint arXiv:2504.05408},
year={2025}
}
```