Every developer knows the moment of deploying code, and all of a sudden, the database password has been pushed to the repository. The fast-tracked journey of secret scanning in GitLab secret scanning has become an important security control in the world of DevSecOps, especially in GitLab environments. This systematic scan detects and removes sensitive information, such as passwords, access tokens, API keys, etc, before they are leaked to potential attackers.
As development teams scale and the velocity of code changes increases, it’s nearly impossible to manually monitor and prevent secret leakage. This is where GitLab’s secret scanning capabilities come in, automating the detection process, making it simpler for teams to secure their applications without blocking development. The tool operates silently in the background while developers build features, checking every line of code for potential secrets.
Exposed secrets have a much wider impact than security concerns. This creates regulatory compliance challenges, service disruptions for consumers, and the challenging task of rotating compromised credentials across the organization. Organizations can spare themselves from these challenges by adopting best practices for secret scanning, thus conserving time and resources while preserving user loyalty.
In this blog post, we will look into the inner workings of GitLab’s secret scanning, how it is implemented in various cases, and practical steps on how to optimize the use of this security feature.
What is GitLab Secret Scanning?
GitLab secret scanning operates directly in the development environment to discover and flag exposed credentials. The tool scans the entire GitLab instance behind the scenes for sensitive data in code, commits, and merge requests. As soon as any developer pushes new code or opens a merge request, the scanner immediately goes to work looking for potential secrets that escaped into the codebase.
What makes GitLab’s secret scanning particularly useful is how it fits into the CI/CD pipeline. You don’t need to run separate security checks – the scanning happens automatically as part of your regular development process.
Why is Secret Scanning Crucial for GitLab Repositories?
Code repositories have been one of the top targets for attackers going for the low-hanging fruits to get access to the company’s systems. Secrets can remain in commit history even if the credentials are removed from the latest version of the code, such as when developers accidentally push them to GitLab repositories. Threat actors systematically check public repositories for such exposed secrets and often manage to find and exploit them within minutes of their exposure.
In large development teams, where code changes often occur, the risk multiplies. A single AWS key exposed to the public can lead attackers to your entire cloud infrastructure.
Most secret exposures are unintentional. Developers may accidentally check in credentials while testing new features, debugging issues, or setting up development environments. Even experienced developers sometimes inadvertently push configuration files containing real credentials in a hurry to fix urgent problems. Without automated scanning, these secrets can remain exposed for days or even months until someone notices.
The increase in automated attacks makes rapid identification critical. Bots scrape the public code repositories, searching for specific patterns that fit the shape of known credentials. If they discover a valid secret, it can immediately be used in attacks. An automated threat landscape means manual code reviews simply aren’t enough, but constant automated scanning is essential to keep up with the speed of potential attacks.
Types of Secrets Detected by GitLab
1. API Keys and Tokens
GitLab’s secret scanning detects a range of sensitive information that developers may mistakenly expose in their code. The scanning engine begins with API keys, among the most common types of secrets found in repos. Such keys easily make their way into code during testing or when developers need a quick and dirty/hacky solution. The scanner identifies generic API tokens that may not match to patterns but still include sensitive authentication information.
2. Database Credentials
Another large category that the scanner checks regularly is database credentials. The tool goes further than simply scanning for basic username and password combinations; it identifies full connection strings that frequently contain all of the information a threat actor would need to access services. The scanner knows how to read various database system formats, be it MySQL, PostgreSQL, Redis, or MongoDB. It can identify these credentials in a wide range of file types like code files, config files, documentation, and more.
3. Cloud Provider Secrets
Secrets related to cloud providers require extra care due to their general access to cloud resources. It scans for AWS Access Key Pairs, Google Cloud Service account keys, and Azure Storage Key. These credentials are especially risky as they have the ability to provide access to whole cloud infrastructures. The scanner is aware of the key formats themselves, as well as the configuration files where they’re usually found. It can find these secrets, be they in environment files, JSON configurations, or even directly in code.
4. Encryption Keys
Encryption keys are a third prominent category, as they secure sensitive information/data. The scanner can detect different kinds of cryptographic material such as private SSH keys, SSL/TLS certificates, and PGP private keys.
How GitLab Secret Detection Works?
- Detection Mechanism and Pattern Matching – The secret detection system of GitLab finds potential secrets within your code via pattern matching. A scanner goes through your repository files and looks for signs of text that match other known secret formats. It performs heuristics with regular expressions and other known patterns to identify things that resemble passwords, API keys, or other credentials. The system scans both content and names of files, since developers sometimes include sensitive information in filenames as well when they are debugging.
- Built-in Detection Rules and Patterns – The built-in detection rules are based on actual formats of credentials used in the wild. The rules cover several varieties of secrets, from simple forms like password, to more complex multi-line key formats. The scanner is aware of secret formats from big cloud providers, commonly used development tools, and frequently used services developers rely on. GitLab updates these rules on a regular basis to detect new types of secrets once they are created.
- Scanning Scope and Limitations – Scanning occurs at various stages within the pipeline. When developers push new code, the scanner only looks at changed files. It looks at all the files operated on in merge requests. You can also run complete repository scans to scan your whole codebase. The scanner registers what it has already checked, so it won’t be doing work it doesn’t need to do.
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Read GuideGitLab Secret Scanning Benefits
GitLab secret scanning offers several benefits. Let’s discuss some of them.
1. Enhanced Security via Early Detection
Secret scanning fundamentally changes the way teams manage sensitive data in their code. The scanner captures exposed credentials at the earliest point in the process, the exact moment a developer attempts to commit any sensitive data. This early warning system stops secrets from reaching the repository and subsequently being left in the commit history. Addressing these issues during the commit process saves teams the difficult, time-consuming work of cleaning up exposed secrets after they are pushed to the repository.
2. Time Savings with Automation
Automated secret scanning unlocks a lot of efficiency for development teams. This saves hours of time for developers who would have had to manually scour lines of code for sensitive data. When secrets are discovered, the scanner gives precise file locations and line numbers that contain secrets. This precision saves developers the hassle of time-consuming manual searches, enabling them to remediate security issues rapidly without interrupting their workflow.
3. Enhanced Compliance Readiness
Secret scanning becomes crucial for organizations that have stringent security requirements as it provides detailed tracking capabilities. The system keeps detailed logs of every secret it finds, including when it found them and how it handled them. Capturing these logs also serves as proof of active security measures taken to prevent credentials from being disclosed and is useful in security audits.
Managing GitLab Secret Scanning Results
- The GitLab security dashboard is the control center for managing detected secrets. Security engineers can see all the secrets across your projects that were detected on the dashboard, which provides insight as to what security teams can track and manage any potential exposures. These include the projects with the highest number of security findings, how quickly teams remediate detected secrets, and trends in secret detection over time. The dashboard moves things around so we know what needs attention now and what can hold off.
- The secret scanning reports give rich information on each detected secret. Each report provides the precise location of the secret, the kind of secret detected, and when it was discovered. The reports also show the snippet of actual code in which the secret appears, making it easy for security engineers to verify the detection.
- GitLab maintains a history of all detected secrets on a file-by-file, commit-by-commit basis, so anyone can see how secrets end up in the code in the first place and how well teams are removing them.
Challenges Associated with GitLab Secret Scanning
When it comes to implementing and scaling GitLab Secret Scanning, companies face various challenges. Let’s discuss a few of them.
1. Impact on Performance For Large Repositories
The system might not perform well when scanning on big/large codebases. In repositories containing thousands of files and long commit histories, scanning them requires significant computing power. The scanner needs to read and analyze each of the files, which means it’s a performance bottleneck for CI/CD pipelines if it’s not optimized.
Monorepos with many projects being type-checked, have a particularly painful time of it, where history and files from multiple projects are collected in a single place.
2. Handling Historical Code
In GitLab environments, scanning legacy code comes with some special challenges. Some secrets were leaked a long time ago but exist through the git history, they can be found in old commits. Finding such historical secrets is a brittle process as rewriting git history can affect other developers. When teams first enable scanning, they usually find hundreds of secrets that were stored in their repos prior to scanning being enabled, this leads to a backlog of security issues to fix.
3. Coverage Limitations
A few pieces of repositories are tricky to examine well. Binary files, encrypted content and compressed archives usually cannot be scanned properly. Secrets may be hiding in custom file formats unfamiliar to the scanner. Certain development frameworks create files that frequently lead to false positives, putting teams in the position of having to strike a balance between catching legitimate secrets and not generating false positives. Coverage gaps require careful management to remain secure.
4. Scaling Challenges
As organizations scale, secret scanning at scale becomes more challenging. As teams work across different projects, this increases the number of code changes that must be scanned. The system needs to accommodate this increased load while still returning relatively fast response times and naturally.
GitLab Secret Scanning Best Practices
1. Regular Review Schedule
All development teams require a systematic method for secret scanning. Scanning reports offer more granularity than an manual process can provide. A review of them on a weekly basis can help pick up potential problems. The security team should define a process for what happens when a secret has been detected, who will review the alert, and how quickly each type of secret needs to be dealt with. Security teams need this cadence to match the development velocity, busy teams who ship code daily might require daily reviews, whereas smaller teams may get away with once per week.
2. Baseline Configuration Setup
If you as a company want to avoid future headaches, get your GitLab secret scanner setup right. Your base config should encompass all the important file types and places where secrets may crop up. Detection patterns should be regularly reviewed and updated by teams to detect new types of secrets that might be created as part of their development process. The scanning configuration should be in version control and undergo the same review process as other critical security settings.
3. Team Training Protocol
Practices for keeping secrets safe is imperative knowledge for developers. In addition to just enabling the scanner, teams should also learn how different types of secrets get leaked and how to prevent common pitfalls. Frequent training sessions reinforce security awareness and enable teams to mount a strong response when the scanner identifies issues. These sessions are most effective if they are done using material from the internal repositories with concrete examples that demonstrate high-risk patterns to avoid which may result in secret leakage.
4. Response Plan Development
A clear, consistent response plan to detected secrets ensures that there’s no panicking when problems occur. Teams should lay out precisely what to do when different sorts of secrets spill out. That plan should include interim actions to revoke compromised credentials as well as long-term remediation such as updating deployment processes. The response plan should contain contact information for key team members and external services that may need to be notified of any exposed secrets.
How SentinelOne Can Help?
When it comes to protecting your codebase, SentinelOne offers powerful tools to enhance and expand GitLab’s secret scanning capabilities. Here’s how SentinelOne can make a difference:
SentinelOne Singularity™ Cloud Native Security is an agentless CNAPP solution that eliminates false positives and takes rapid action against alerts. It supercharges your offensive security and team’s efficiency with its Verified Exploit Paths™. You can outsmart attackers with its cutting-edge Offensive Security Engine™ and safely simulate attacks on your cloud infrastructure to detect critical vulnerabilities. You’ll even learn about weaknesses and security gaps you weren’t previously aware of, even the ones that stay hidden, unknown, or undetectable.
SentinelOne Singularity™ Cloud Native Security can identify more than 750+ secret types hardcoded across code repositories. It will keep them from leaking out. You will be able to stay on top of the latest exploits and CVEs and quickly determine if any of your cloud resources are affected.
By integrating directly into your GitLab CI/CD pipelines, SentinelOne automates secret scanning at every stage of the development process. This ensures that sensitive information never makes it into production, safeguarding your applications from potential breaches.
In addition to secret scanning, SentinelOne provides comprehensive protection by identifying container vulnerabilities, infrastructure misconfigurations, and compliance issues. This all-in-one approach gives your team a clearer picture of your security posture across the entire development lifecycle.
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Get a DemoConclusion
In today’s software development, secret scanning in GitLab environments is an essential element of securing a company’s workloads. As discussed in the blog, a single leaked secret can cause devastating financial loss and security incidents. The speed of modern development combined with the complexity of applications makes manual secret detection almost impractical, resulting in a requirement for automated scanning solutions.
Implementing proper secret scanning practices does more than prevent an “incident” – it changes how teams work with sensitive information. GitLab’s secret scanning capabilities, made more powerful through SentinelOne’s advanced capabilities, help GitLab development teams to build secure applications and enable them to keep building at the speed required. These tools work together to intercept potential exposures before they become security incidents, shielding organizations from the inordinate cost and time involved in responding to compromised credentials.
FAQs
GitLab Secret Scanning is an automated security feature that checks your code repositories for exposed credentials like API keys, passwords, and other sensitive data before they become public.
Yes, GitLab Secret Scanning works on both private and public repositories, but the feature availability depends on your GitLab subscription tier.
When GitLab finds a secret, it creates an alert in the security dashboard and can automatically block merge requests containing the detected secret.
Immediately revoke the exposed credential, remove it from the repository, and rotate any related access keys or tokens that might be compromised.
Repositories should be scanned on every commit and merge request, with full repository scans running at least weekly for comprehensive coverage.