The security of source code is a crucial aspect of software development. Leaks of confidential data from repositories can pose significant risks to organizations. Effectively addressing such vulnerabilities requires the use of scanning tools.
In this article, we will cover:
- Consequences of hardcoding secrets that lead to data leaks.
- Overview of popular open solutions for detecting secrets.
- How to choose a tool to find secrets in source code.
- Recommendations on how to implement them.
After reading, you will gain insight into the capabilities of modern automated code-scanning solutions for detecting critical vulnerabilities.
Why It Is Important to Detect Hardcoded Credentials
There’s no need to tell why code security is crucial for software development companies.. Compromising confidential data grants malicious actors access to information systems and organizational infrastructure, posing threats such as theft of sensitive information and financial losses.
Here are some examples of secrets that can cause data breach:
- system passwords;
- API access keys;
- user authentication tokens;
- data encryption keys;
- database credentials;
- configuration files with security settings.
Such data must be securely protected and kept out of public access. However, during the software development process, developers may accidentally or unknowingly include hardcoded secrets directly in the source code, committing them along with other changes to public repositories. This poses serious security risks.
Consequences of Hardcoded Secret Leaks
The potential damage from leaks of hardcoded secrets from source code is difficult to overstate. Such errors lead to:
- unauthorized access by malicious actors to company systems and data;
- fraudulent transactions and theft of money from organization accounts;
- unavailability of services, failures in the operation of web applications;
- loss of reputation and trust of customers.
According to statistics, the detection of confidential data leaks takes an average of 207 days. During this time, malicious actors can cause significant harm to companies using compromised secrets.
Open Solutions for Discovering Secrets
There are numerous open source tools designed to detect secrets in source code. Let’s look at seven popular solutions.
TruffleHog
Actively used for discovering secrets in GitHub repositories, TruffleHog boasts an extensive database of 600+ patterns for detecting various types of hidden data. The TruffleHog Secrets Detection solution stands out for its high scanning speed and accuracy in identifying hardcoded secrets.
Gitleaks
A command-line utility for static code analysis to detect secrets, Gitleaks is well-suited for scanning both local and remote Git repositories. Gitleaks offers the advantage of easy integration into the DevOps pipeline, working seamlessly with public and private Git repositories.
Git-Secrets
Distinguished by its ability to integrate into CI/CD pipelines for real-time scanning of developers’ commits, Git-Secrets helps prevent secrets from entering the code before being pushed to the repository. It can be used for both pre-commit and post-commit checks, reducing the risk of compromising hidden data.
Scan
Chosen for its compatibility with continuous integration and delivery systems, Scan is an ideal tool for regular vulnerability scanning. It checks commits in GitLab, Bitbucket, and GitHub for hard-coded passwords, tokens, and keys.
Whispers
Applied to search for various types of confidential data in program source texts, Whispers supports various file formats, including XML, YAML, Dockerfile, and more. It is praised for its plugin support, extending scanning capabilities.
Detect-secrets
Utilized to block secret data before it enters source code repositories, Detect-secrets addresses the issue at the input stage, preventing hidden data from appearing in the code initially. It integrates seamlessly with continuous software integration systems.
Git-all-secrets
Combining the capabilities of several open tools to discover hidden data, Git-all-secrets increases the likelihood of detecting and extracting data from the code. It relies on solutions such as TruffleHog and Repo-supervisor. The advantage lies in the consolidation of multiple scanning tools, reducing the likelihood of false-negative results.
Key Criteria for Selecting Tools
When choosing a solution for automated secrets detection, it’s essential to consider the following criteria:
- Vulnerability Detection Accuracy: Assess the tool’s effectiveness in identifying hard-coded passwords, tokens, API access keys in the code, configuration files, and other sources.
- Regular Scanning Capabilities: Evaluate the frequency of system checks. Ideally, the tool should allow for daily or more frequent scans of repositories and commits.
- Performance and Scalability: Consider how quickly the tool can analyze large volumes of data. Speed is crucial for sizable projects with hundreds of thousands of lines of code.
- Ease of Deployment in IT Infrastructure: Evaluate the integration capabilities of the scanning system with other tools and services, including code repositories, CI/CD systems, and monitoring.
The closer the match to these criteria, the more effectively the system will work to protect against leaks of hidden data in the source code.
Recommendations for Tool Implementation
To maximize the benefits of implementing solutions for hidden data detection, consider the following recommendations:
- Configure regular scanning of source code repositories: Preferably, run checks for each commit.
- Automatically integrate the scanner with continuous integration and delivery systems: Ensure seamless integration with the development workflow.
- Define rules for detecting the most critical types of hidden data: Create a set of rules tailored to the most critical types of hidden data for your infrastructure.
- Mandate developers to address identified vulnerabilities during code review: Make it mandatory for developers to fix identified vulnerabilities during code review before merging code into the main branch.
- Experiment with multiple open source tools and choose the optimal one for your needs: Test different open-source tools and select the one that best aligns with your goals.
Combining automated checks with developer involvement in the vulnerability detection process significantly enhances the security of the source code against leaks of confidential data. By choosing suitable software, configuring integration with the infrastructure, and establishing search rules, you can significantly reduce the risks of information leakage from source code repositories. Automation of security assurance processes should become an integral part of a modern SDLC (Software Development Life Cycle).
