A Comparative Study of Software Secrets Reporting by Secret Detection Tools
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Abstract (arXiv)
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 open-source 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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jul 2023.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 2.
5 papers.
- 2025You Have Been LaTeXpOsEd: A Systematic Analysis of Information Leakage in Preprint Archives Using Large Language Modelscited by 1
- 2024Using AI/ML to Find and Remediate Enterprise Secrets in Code & Document Sharing Platformscited by 0
- 2025Argus: A Multi-Agent Sensitive Information Leakage Detection Framework Based on Hierarchical Reference Relationshipscited by 0
- 2026Separating Secrets from Placeholders: A Hybrid CNN-CodeBERT Framework for Three-Class Credential Leakage Detectioncited by 0
- 2026Secret Scanner Agent: Extracting Secrets and Access Context from Unstructured Documentscited by 0
- 2026Secret Scanner Agent: Extracting Secrets and Access Context from Unstructured Documents10 Jul 2026
- 2026Separating Secrets from Placeholders: A Hybrid CNN-CodeBERT Framework for Three-Class Credential Leakage Detection29 May 2026
- 2025Argus: A Multi-Agent Sensitive Information Leakage Detection Framework Based on Hierarchical Reference Relationships9 Dec 2025
- 2025You Have Been LaTeXpOsEd: A Systematic Analysis of Information Leakage in Preprint Archives Using Large Language Models4 Oct 2025
- 2024Using AI/ML to Find and Remediate Enterprise Secrets in Code & Document Sharing Platforms3 Jan 2024