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Implementing a detector at GitGuardian : a use case with MongoDB credentials
This article explains how our research team develops and refines detectors. To illustrate this article, we will take the case of MongoDB credentials.
This article explains how our research team develops and refines detectors. To illustrate this article, we will take the case of MongoDB credentials.
GitGuardian has been scanning every single public commit made on GitHub for secrets since 2017, now we are releasing our findings in the most comprehensive study on secrets sprawl ever conducted.
This article will expose how our algorithms detect secrets and what we have learnt from scanning, literally, billions of commits.
Secrets including API tokens, passwords and credentials are the keys to the kingdom. Yet storing secrets inside git including GitHub & GitLab is a problem. Security experts discuss why this is & how to solve this.
This article looks at how SAP built an internal secrets scanning solution to detect API keys and other credentials hardcoded in git repos and revoke them.
Despite secrets like API keys, OAuth tokens, certificates and passwords being extremely sensitive, it is common for these to leak into git repositories through source code. This article looks at why this is true and how we can prevent it.
Credential theft is already a well-known adversary technique but the risk expands much wider when introducing secrets such as API keys. This article looks at automated secrets detection, the challenges, and potential solutions.
The first in a series of articles that will take a deep dive into secrets within source code: In this article, we will look at the concept of secret sprawl, the unwanted distribution of secrets through multiple systems, and how we can prevent it.
An in depth guide intended for CISOs, application security and other security professionals who want to protect their organizations from credentials leaked on GitHub.
Why precision and recall are such important metrics to consider when evaluating the performance of classification algorithms such as secrets detection.
November 2018: Here is what we've learned, achieved, and what's coming next.