threat-modeling.md

Threat Model Analysis for fabric8io/fabric8-pipeline-library

Threat: Arbitrary Code Execution via Groovy Script Injection (within library functions)

• Threat: Arbitrary Code Execution via Groovy Script Injection (within library functions)

  • Description: An attacker exploits a vulnerability in how a fabric8-pipeline-library function handles user-provided input. Many library functions are essentially wrappers around Groovy scripts. If a function takes user input (e.g., a branch name, a tag, a configuration value) and uses that input unsafely within a Groovy script (e.g., via string interpolation or dynamic code evaluation), an attacker could inject malicious Groovy code. This code would then be executed with the privileges of the pipeline. This is distinct from injecting into the user's Jenkinsfile; this is about exploiting the library's own code.
  • Impact:
    • Complete compromise of the Jenkins agent.
    • Access to all secrets available to the pipeline.
    • Unauthorized access to the Kubernetes/OpenShift cluster (with the pipeline's service account).
    • Ability to deploy malicious applications or modify existing deployments.
    • Data exfiltration.
  • Affected Component: Any fabric8-pipeline-library function that accepts user-provided input as a parameter and uses that input within a Groovy script without proper sanitization or validation. This requires careful code review of the library itself to identify specific vulnerable functions. Potentially vulnerable areas include functions that:
    • Construct shell commands (sh steps).
    • Interact with the Kubernetes/OpenShift API based on user input.
    • Perform dynamic code evaluation.
    • Use string interpolation with user-provided values.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Library Code Audit: Thoroughly audit the source code of the fabric8-pipeline-library (and any custom forks or extensions) to identify and remediate vulnerable functions. Focus on how user input is handled.
    • Input Validation (within the library): Modify the library's code to implement strict input validation and sanitization for all user-provided parameters. Prefer parameterized commands over string interpolation.
    • Contribute Security Fixes: If vulnerabilities are found, contribute patches back to the upstream fabric8-pipeline-library project.
    • Use a Fork (with caution): As a temporary measure, maintain a fork of the library with security fixes applied, but actively work to get those fixes merged upstream.
    • Restrict Usage of Vulnerable Functions: If a vulnerable function cannot be immediately fixed, restrict its usage or provide clear warnings to users about the risks.
    • Jenkins Script Security Plugin: Even though this threat is within the library, the Script Security plugin can still help. If the library uses approved methods, the plugin can limit the damage.

Threat: Privilege Escalation via Kubernetes/OpenShift API Abuse (through library functions)

• Threat: Privilege Escalation via Kubernetes/OpenShift API Abuse (through library functions)

  • Description: The fabric8-pipeline-library provides numerous functions that interact directly with the Kubernetes/OpenShift API. If the pipeline's service account has excessive permissions, these functions can be used (intentionally or unintentionally) to perform actions beyond the intended scope of the pipeline. The threat here is that the library itself provides the mechanism for this abuse, even if the user's Jenkinsfile is not directly malicious. For example, a seemingly innocuous function to list pods could be used to exfiltrate sensitive data if the service account has overly broad read permissions.
  • Impact:
    • Unauthorized access to sensitive data within the cluster (secrets, config maps, etc.).
    • Modification or deletion of existing deployments (even those unrelated to the current application).
    • Deployment of malicious applications.
    • Potential to gain cluster-admin privileges (if the service account is misconfigured).
  • Affected Component: All fabric8-pipeline-library functions that interact with the Kubernetes/OpenShift API. This includes, but is not limited to:
    • openshift.withCluster() and kubernetes.withCluster()
    • Functions for deploying, scaling, updating, and deleting resources (deployments, services, pods, etc.).
    • Functions for retrieving information from the cluster (e.g., getting pod logs, listing secrets).
    • Functions that interact with custom resources.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Principle of Least Privilege (Service Account): This is the primary mitigation. The pipeline's service account should have only the absolute minimum permissions required to perform its tasks. Use specific RBAC roles and role bindings, scoped to the appropriate namespaces and resources. Never use cluster-admin.
    • Library Function Auditing: Understand the permissions required by each fabric8-pipeline-library function used in the pipeline. Ensure the service account's permissions are aligned with these requirements and no more.
    • Kubernetes/OpenShift Admission Controllers: Implement admission controllers to enforce security policies and prevent unauthorized resource modifications, even if the service account has the necessary permissions. This adds a layer of defense.
    • Network Policies: Use Kubernetes Network Policies to restrict network access within the cluster, limiting the blast radius of a compromised pod or service account.

Threat: Secret Exposure via Library Function Misuse

• Threat: Secret Exposure via Library Function Misuse

  • Description: A fabric8-pipeline-library function, if used incorrectly, could inadvertently expose secrets. This is less about a vulnerability within the library and more about how the user employs the library. For example, a function that retrieves a secret from Kubernetes might be used in a way that logs the secret's value, or a function that interacts with an external service might be passed a secret in a way that exposes it in transit.
  • Impact:
    • Compromise of credentials, allowing attackers to access other systems or services.
    • Data breaches.
    • Reputational damage.
  • Affected Component: Any fabric8-pipeline-library function that handles secrets, interacts with external services using credentials, or generates logs/outputs that might contain sensitive information. This requires careful consideration of how each function is used within the pipeline.
  • Risk Severity: High
  • Mitigation Strategies:
    • Secure Secret Handling Practices: Follow best practices for handling secrets within the pipeline:
      • Use the Jenkins Credentials plugin or Kubernetes Secrets.
      • Never hardcode secrets.
      • Use the Jenkins maskPasswords feature.
    • Careful Function Usage: Thoroughly understand how each fabric8-pipeline-library function handles secrets and ensure it is used correctly. Read the documentation carefully.
    • Code Review: Review all uses of fabric8-pipeline-library functions that handle secrets to ensure they are not exposing sensitive information.
    • Log Auditing: Regularly audit pipeline logs to ensure they do not contain any exposed secrets.

Threat: Dependency Vulnerabilities (within the library)

• Threat: Dependency Vulnerabilities (within the library)

  • Description: The fabric8-pipeline-library itself has dependencies (other Groovy libraries, Jenkins plugins, Kubernetes client libraries, etc.). These dependencies may contain known security vulnerabilities. An attacker could exploit these vulnerabilities to compromise the pipeline, even if the fabric8-pipeline-library code itself is secure.
  • Impact:
    • Varies depending on the specific vulnerability, but could range from denial of service to arbitrary code execution (on the Jenkins agent or within the Kubernetes cluster).
  • Affected Component: The fabric8-pipeline-library itself and all of its transitive dependencies.
  • Risk Severity: High
  • Mitigation Strategies:
    • Regular Updates: Keep the fabric8-pipeline-library and all of its dependencies up to date. This is crucial. Subscribe to security advisories for the library and its key dependencies.
    • Dependency Scanning: Use software composition analysis (SCA) tools to automatically identify and track dependencies and their known vulnerabilities. Integrate this into the CI/CD pipeline.
    • Vulnerability Management Process: Establish a clear process for promptly addressing identified vulnerabilities in dependencies. This may involve updating the library, applying patches, or finding workarounds.