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}
}
```