A Comparative Study of Software Secrets Reporting by Secret Detection Tools

Setu Basak, Jameson Cox, Bradley Reaves, and Laurie Williams

ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2023

Benchmarks run on nine secret detection tools show that no tool dominates both precision and recall, with errors traced to generic regexes and incomplete rulesets.

Abstract

Background: According to GitGuardian’s monitoring of public GitHub repositories, secrets sprawl continued accelerating in 2022 by 67% compared to 2021, exposing over 10 million secrets (API keys and other credentials). Though many open-source and proprietary secret detection tools are available, these tools output many false positives, making it difficult for developers to take action and teams to choose one tool out of many. To our knowledge, the secret detection tools are not yet compared and evaluated. Aims: The goal of our study is to aid developers in choosing a secret detection tool to reduce the exposure of secrets through an empirical investigation of existing secret detection tools. Method: We present an evaluation of five opensource and four proprietary tools against a benchmark dataset. Results: The top three tools based on precision are: GitHub Secret Scanner (75%), Gitleaks (46%), and Commercial X (25%), and based on recall are: Gitleaks (88%), SpectralOps (67%) and TruffleHog (52%). Our manual analysis of reported secrets reveals that false positives are due to employing generic regular expressions and ineffective entropy calculation. In contrast, false negatives are due to faulty regular expressions, skipping specific file types, and insufficient rulesets. Conclusions: We recommend developers choose tools based on secret types present in their projects to prevent missing secrets. In addition, we recommend tool vendors update detection rules periodically and correctly employ secret verification mechanisms by collaborating with API vendors to improve accuracy.

Citation (IEEE)

S. Basak, J. Cox, B. Reaves, and L. Williams, “A Comparative Study of Software Secrets Reporting by Secret Detection Tools,” in ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2023.

BibTeX

@inproceedings{bcrw23,
  author = {Basak, Setu and Cox, Jameson and Reaves, Bradley and Williams, Laurie},
  url = {https://doi.ieeecomputersociety.org/10.1109/ESEM56168.2023.10304853},
  booktitle = {{ACM/IEEE} International Symposium on Empirical Software Engineering and Measurement},
  date = {2023-10},
  doi = {10.1109/ESEM56168.2023.10304853},
  pages = {12},
  title = {A Comparative Study of Software Secrets Reporting by Secret Detection Tools},
}