mitigations.md

Mitigation Strategies Analysis for fabric8io/fabric8-pipeline-library

Mitigation Strategy: Automated Dependency Scanning and Management (Library-Specific)

• Description:

  1. Pre-Execution Scan: Before the fabric8-pipeline-library Groovy scripts are executed, run a Software Composition Analysis (SCA) tool. This scan should target the library itself and its transitive dependencies. This is distinct from scanning application code or container images built by the pipeline.
  2. Dependency Definition Files: The SCA tool needs to be configured to analyze the dependency definition files used by the library (likely Maven's pom.xml or Gradle's build.gradle if you're using a custom build of the library, or the equivalent files within the library's source repository).
  3. Fail Fast: If the SCA tool detects vulnerabilities in the fabric8-pipeline-library or its dependencies that exceed your defined severity thresholds, prevent the pipeline from executing. This is a crucial "shift-left" approach.
  4. Library Updates: Establish a process (ideally automated) to regularly update the fabric8-pipeline-library to the latest stable version. This ensures you're using a version with the latest security patches. This might involve pulling updates from the official repository or managing a custom fork.
  5. Dependency Pinning Review (Library-Specific): If you pin the version of the fabric8-pipeline-library or its internal dependencies, have a scheduled review process to update these pinned versions.

• Threats Mitigated:

  • Vulnerable fabric8-pipeline-library Dependencies (Severity: High to Critical): Exploitation of vulnerabilities within the library itself or its dependencies can lead to arbitrary code execution within the pipeline context, potentially compromising the build environment or the Kubernetes cluster.
  • Supply Chain Attacks on the Library (Severity: High to Critical): A compromised upstream dependency of the fabric8-pipeline-library could inject malicious code that is then executed as part of your pipeline.

• Impact:

  • Vulnerable Dependencies: Significantly reduces the risk of using a compromised version of the library.
  • Supply Chain Attacks: Provides early detection of compromised library dependencies, limiting the attacker's window of opportunity.

• Currently Implemented:

  • Example: Not implemented. Dependency scanning is focused on application code and container images, not the library itself.

• Missing Implementation:

  • Example: No pre-execution SCA scan of the fabric8-pipeline-library. No automated process for updating the library or reviewing pinned versions of the library or its dependencies.

Mitigation Strategy: Secure Pipeline Code (Groovy Script Hardening)

• Description:

  1. Code Reviews (Library-Specific Focus): Mandatory code reviews for all changes to pipeline definitions that use the fabric8-pipeline-library. The review should specifically focus on:
     • Groovy Code Security: Scrutinize all Groovy code for potential injection vulnerabilities, especially where user input or external data is used.
     • Library API Usage: Ensure the fabric8-pipeline-library APIs are used correctly and securely. Avoid using deprecated or insecure methods.
     • Input Sanitization: If the pipeline takes any input (environment variables, parameters) that are used within the fabric8-pipeline-library calls or Groovy scripts, rigorously sanitize and validate that input before it's used. Use whitelisting whenever possible.
     • Avoid Dynamic Code Generation: Minimize or eliminate dynamic Groovy code generation based on untrusted input. This is a high-risk practice.
  2. Least Privilege within Groovy: Even within the Groovy scripts, strive for least privilege. Avoid using overly permissive operations or accessing resources that aren't strictly necessary.
  3. Safe API Usage: Consult the fabric8-pipeline-library documentation for secure usage patterns. Identify and avoid any documented insecure practices.

• Threats Mitigated:

  • Pipeline Code Injection (Severity: Critical): Attackers injecting malicious Groovy code through input parameters, environment variables, or compromised repositories can gain control of the pipeline's execution.
  • Improper Library API Usage (Severity: Medium to High): Using the fabric8-pipeline-library APIs incorrectly can lead to unintended behavior, security vulnerabilities, or data leaks.

• Impact:

  • Pipeline Code Injection: Significantly reduces the risk of malicious code execution within the pipeline.
  • Improper API Usage: Improves the overall security and reliability of the pipeline by ensuring the library is used as intended.

• Currently Implemented:

  • Example: Code reviews are mandatory, but there's no specific checklist or focus on Groovy code security or fabric8-pipeline-library API usage.

• Missing Implementation:

  • Example: No formal security checklist for code reviews that specifically addresses Groovy code and fabric8-pipeline-library interactions. Input validation is not consistently applied to all pipeline parameters used within Groovy scripts.

Mitigation Strategy: Secure Secrets Integration with the Library

• Description:

  1. Secrets Management Integration: Modify the fabric8-pipeline-library Groovy scripts to retrieve secrets exclusively from a dedicated secrets management solution (Kubernetes Secrets, HashiCorp Vault, etc.). Do not hardcode secrets or pass them as plain text environment variables.
  2. Library-Specific Secret Handling: Examine how the fabric8-pipeline-library itself handles secrets internally. If it stores secrets in temporary files or logs, identify ways to mitigate this (e.g., using secure temporary directories, disabling verbose logging).
  3. Least Privilege for Secret Retrieval: The code within the fabric8-pipeline-library that retrieves secrets should have the minimum necessary permissions to access only the required secrets.

• Threats Mitigated:

  • Secrets Exposure within the Pipeline (Severity: High to Critical): Improper handling of secrets within the fabric8-pipeline-library's Groovy scripts can lead to secrets being exposed in logs, environment variables, or temporary files.
  • Credential Theft from Pipeline (Severity: High): Attackers gaining access to the pipeline's execution environment could steal secrets if they are not properly managed.

• Impact:

  • Secrets Exposure: Eliminates hardcoded secrets and reduces the risk of accidental exposure within the pipeline's context.
  • Credential Theft: Limits the impact of a compromised pipeline by ensuring secrets are retrieved securely and only when needed.

• Currently Implemented:

  • Example: Kubernetes Secrets are used, but the specific Groovy code that interacts with them hasn't been thoroughly reviewed for security best practices.

• Missing Implementation:

  • Example: A thorough review of the fabric8-pipeline-library's internal secret handling mechanisms is needed. Ensure that the Groovy code retrieving secrets adheres to least privilege principles.