Docker Compose is used to define and manage containerized applications through configuration files such as compose.yaml and docker-compose.yml. These files can contain application settings, environment variables, database connection details, and other configuration information required to run containers.
When developers store passwords, API keys, access tokens, or database credentials directly in configuration files, these secrets may become exposed when the files are shared, uploaded to source-control repositories, or included in project archives. Such exposure can increase the risk of unauthorized access to the services that use those credentials.
In this use case, an Ubuntu virtual machine is used as the primary development and security-analysis environment, while Kali Linux provides a separate environment for authorized security testing. Docker and Docker Compose provide the controlled application environment.
Gitleaks is used to scan Docker Compose configuration files for potential hardcoded secrets.
DeepSeek-Coder, running locally through Ollama, assists in explaining detected findings and recommending appropriate remediation changes. Python processes the scan results and AI-generated recommendations, while Streamlit presents the findings and validation status through a dashboard.
Checkov and applicable Trivy checks are used to identify additional supported configuration-security issues and container-image vulnerabilities. Nmap is used only when network-service exposure assessment is required for a running laboratory application.
The project uses publicly available configuration examples and controlled secure and insecure configuration samples. Dummy credentials are used to demonstrate the detection and remediation workflow without exposing real secrets.
Application/Service: Docker Compose
Docker Compose is the target configuration technology in this use case. It defines application services, container settings, environment variables, networks, volumes, and other deployment parameters required to run containerized applications.
Docker Compose configuration files may contain hardcoded passwords, API keys, access tokens, and database credentials. If these files are shared or committed to a repository, sensitive values may become accessible to unauthorized users.
Secret-scanning tools can identify many known secret patterns, but they may also produce false positives or fail to identify unusual credential formats. Therefore, detected findings require validation.
Configuration files may also contain security weaknesses unrelated to hardcoded secrets, such as unnecessary port exposure or insecure container settings. These issues require separate configuration-security checks.
The proposed solution introduces automated secret scanning, AI-assisted finding analysis, remediation recommendations, configuration correction, additional security checks, and post-remediation validation.
Specific Attack: Hardcoded Secret Exposure Through Docker Compose Configuration Files
The controlled attack scenario evaluates whether hardcoded dummy credentials embedded in Docker Compose configuration files can be identified by secret-scanning tools.
The objective is to demonstrate how credentials can become exposed through insecure configuration practices and how the detected findings can be analysed and remediated.
Secret Detection and Secure Configuration Management
The primary security concept is Secret Detection and Secure Configuration Management combined with AI-Assisted Remediation and Security Validation.
The assessment identifies potentially exposed secrets in Docker Compose configuration files, validates the findings, and uses DeepSeek-Coder to assist in understanding the associated risks and recommending corrective changes.
Gitleaks provides the initial secret-detection results. Python processes the results and supplies sanitised findings to DeepSeek-Coder. The generated recommendations are reviewed before implementation. The corrected configuration is then scanned again to determine whether the original findings remain.
Additional supported configuration-security checks are performed separately because secret scanning alone cannot identify every possible configuration weakness.
The security concept defines the overall security strategy. The following are the specific defensive mechanisms implemented.
Gitleaks scans Docker Compose configuration files for potential hardcoded secrets using its available detection rules.
DeepSeek-Coder analyses the relevant configuration context and sanitised scan findings to explain the possible security implications and recommend corrective changes.
Hardcoded dummy credentials are removed from the corrected configuration and replaced with an appropriate configuration approach.
The project evaluates how sensitive values should be supplied without embedding them directly in configuration files. For production environments, an appropriate secret-management solution should be selected according to the application's requirements.
Checkov and applicable Trivy configuration checks are used to assess supported security settings. Trivy is also used to examine selected container images for known vulnerabilities.
Detected findings are reviewed against the relevant configuration content to distinguish genuine test secrets from false positives.
The corrected configuration is scanned again, and applicable security checks are repeated.
Gitleaks scans configuration files and repositories for potential hardcoded secrets.
DeepSeek-Coder is used through Ollama to analyse sanitised findings and suggest possible configuration improvements.
Ollama provides the local runtime for executing a compatible DeepSeek-Coder model.
Docker runs containers, while Docker Compose defines and manages the application's container configuration.
Checkov performs supported infrastructure-as-code security checks. Its applicability to Docker Compose depends on the installed version and supported configuration format.
Trivy scans container images for known vulnerabilities and supports selected configuration-security checks.
Nmap identifies network services exposed by an authorised target. It is used only when a controlled application is running and network exposure needs to be assessed.
Python processes scan results, organises findings, and coordinates the AI-assisted analysis workflow.
Streamlit provides a local dashboard for displaying findings, remediation status, and validation results.
Kali Linux provides a separate environment for authorised security testing and network-service discovery.
Ubuntu is the primary environment for configuration analysis, secret scanning, AI-assisted processing, and dashboard execution.
VirtualBox provides the isolated virtual-machine environment for the project.
Publicly available configuration examples and controlled secure and insecure samples provide the input for testing the secret-detection workflow.
The controlled laboratory provides an environment for evaluating hardcoded-secret exposure in Docker Compose configuration files.
Gitleaks identifies potential hardcoded secrets in the selected insecure configuration samples, providing findings for further investigation.
Python processes the scanner output into a structured format, allowing findings to be reviewed and compared consistently.
DeepSeek-Coder assists in explaining detected security issues and recommending corrective configuration changes, with all recommendations subject to manual review.
The corrected configuration samples demonstrate how hardcoded dummy credentials can be removed and replaced with a more appropriate configuration approach.
Post-remediation Gitleaks scans provide evidence of whether the originally detected dummy secrets remain in the corrected files.
Checkov and applicable Trivy checks provide additional configuration-security findings where the relevant formats and checks are supported.
Trivy container-image scanning identifies known vulnerabilities in selected images when image assessment is included in the laboratory.
Nmap provides information about network services exposed by the controlled application, where network-exposure assessment is required.
The Streamlit dashboard presents the findings, remediation status, and comparison between initial and final scan results.
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