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attack-surface.md

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Attack Surface Analysis for fabric8io/fabric8-pipeline-library

Attack Surface: 1. Code Injection in Pipeline Scripts (Groovy)

  • Description: Attackers inject malicious Groovy code into the pipeline definition, exploiting the library's reliance on Groovy scripting for core functionality.
  • How fabric8-pipeline-library Contributes: The library's core design centers around Groovy scripts. Its helper functions are Groovy methods, and the entire pipeline execution flow is typically defined in Groovy. This creates a primary attack vector for code injection.
  • Example: An attacker modifies a Jenkinsfile using the library to include a sh step that executes a reverse shell, or they misuse a fabric8-pipeline-library function like openshift.apply() with malicious parameters to deploy a compromised resource.
  • Impact: Complete system compromise. The attacker gains control of the build/deployment environment, potentially accessing secrets, source code, and production systems.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Strict Code Review: Mandatory, thorough code reviews for all pipeline changes (Groovy scripts), with a focus on security.
    • Pipeline-as-Code with Immutability: Treat pipeline definitions as immutable artifacts. Use version control and prevent direct modification of running pipelines.
    • Least Privilege: Run pipeline steps (and the library's functions) with the minimum necessary permissions. Avoid highly privileged service accounts.
    • SCM Access Control: Strong access controls on the source code management system (e.g., GitHub, GitLab) to prevent unauthorized script modifications. Require multi-factor authentication.
    • Static Analysis: Employ static analysis tools that can detect potentially malicious patterns in Groovy scripts (e.g., suspicious system calls, network connections, misuse of library functions).
    • Input Validation: Rigorously validate all parameters passed to fabric8-pipeline-library functions, even if those parameters originate from within the pipeline itself. Use allow-lists where possible.

Attack Surface: 2. Dependency Vulnerabilities (Direct and Transitive impacting the library)

  • Description: Vulnerabilities in the fabric8-pipeline-library's direct dependencies, or in the transitive dependencies pulled in by the library, are exploited.
  • How fabric8-pipeline-library Contributes: The library itself has dependencies, and these dependencies may have vulnerabilities. The library's code is the execution context for these vulnerabilities.
  • Example: A vulnerable version of a library used by fabric8-pipeline-library for interacting with Kubernetes is exploited to gain unauthorized access to the cluster.
  • Impact: Varies depending on the vulnerability, but can range from information disclosure to complete system compromise of the build/deployment environment.
  • Risk Severity: High (potentially Critical depending on the specific vulnerability)
  • Mitigation Strategies:
    • Dependency Scanning: Regularly scan the fabric8-pipeline-library and its dependencies using tools like OWASP Dependency-Check, Snyk, etc. This should be part of the build process.
    • Software Bill of Materials (SBOM): Maintain an SBOM to track all dependencies and their versions.
    • Prompt Patching: Apply security updates to the fabric8-pipeline-library and its dependencies as soon as they are available.
    • Vulnerability Database Monitoring: Stay informed about newly discovered vulnerabilities in the library and its dependencies.

Attack Surface: 3. Improper Secrets Management (within the library's context)

  • Description: Secrets used by the fabric8-pipeline-library are exposed or mishandled, leading to potential compromise.
  • How fabric8-pipeline-library Contributes: The library's functions often require secrets (e.g., for interacting with Kubernetes, container registries, etc.). If these secrets are not handled securely within the pipeline scripts that use the library, they become vulnerable.
  • Example: A Kubernetes API token is hardcoded in a Groovy script that uses fabric8-pipeline-library functions, or a registry credential is passed as a plain-text parameter to a library function.
  • Impact: Compromise of sensitive resources accessed by the library (e.g., Kubernetes clusters, container registries).
  • Risk Severity: High (potentially Critical depending on the exposed secrets)
  • Mitigation Strategies:
    • Secrets Management System: Use a dedicated secrets management solution. The pipeline scripts using the fabric8-pipeline-library should reference secrets, never store them directly.
    • Least Privilege: Grant the pipeline (and thus the library) only the minimum necessary access to secrets.
    • Log Redaction: Implement mechanisms to prevent secrets from being written to pipeline logs, especially when using fabric8-pipeline-library functions that might output sensitive information.
    • Secure Secret Retrieval: Ensure that the way the pipeline retrieves secrets (and how the library accesses them) is secure (TLS, strong authentication).

Attack Surface: 4. Abuse of Kubernetes API Access (via library functions)

  • Description: An attacker leverages the fabric8-pipeline-library's functions for interacting with the Kubernetes API to perform unauthorized actions.
  • How fabric8-pipeline-library Contributes: The library provides a direct interface to the Kubernetes API. If the pipeline's service account has excessive permissions, or if the pipeline script is compromised, the library's functions become tools for malicious actions.
  • Example: An attacker injects code into a pipeline script to use fabric8-pipeline-library's openshift.apply() or similar functions to deploy a malicious pod or modify existing deployments.
  • Impact: Compromise of the Kubernetes cluster and any applications running on it.
  • Risk Severity: High (potentially Critical depending on the cluster's role)
  • Mitigation Strategies:
    • Kubernetes RBAC: Implement strict Role-Based Access Control (RBAC) in Kubernetes to limit the permissions of the service account used by the pipeline and accessed by the library.
    • Network Policies: Use network policies to restrict network access within the cluster, limiting the impact of compromised pods deployed via the library.
    • Pod Security Policies/Standards: Enforce security policies to prevent the deployment of insecure pods via the library's functions.
    • Kubernetes Auditing: Enable and monitor Kubernetes audit logs to detect suspicious activity initiated through the library.
    • Least Privilege (Crucial): The service account used by the pipeline, and therefore available to the fabric8-pipeline-library, should have the absolute minimum permissions required.

Attack Surface: 5. YAML/Configuration Injection (affecting library usage)

  • Description: If the pipeline configuration that utilizes the fabric8-pipeline-library is dynamically generated or accepts user input, an attacker might inject malicious YAML, leading to unintended library function calls or parameter manipulation.
    • How fabric8-pipeline-library Contributes: While the library itself doesn't generate YAML, it consumes it. If the YAML that configures how the library is used is compromised, the library's behavior can be altered maliciously.
    • Example: An attacker injects a configuration snippet that changes the parameters passed to a fabric8-pipeline-library function, causing it to deploy a malicious image or interact with a compromised service.
    • Impact: Similar to code injection, allowing attackers to execute arbitrary commands or manipulate the pipeline's behavior through the library.
    • Risk Severity: Critical
    • Mitigation Strategies:
      • Avoid Dynamic Generation: Minimize or eliminate dynamic generation of pipeline configurations that use the fabric8-pipeline-library.
      • Strict Input Validation: If dynamic generation is unavoidable, rigorously sanitize and validate all user-supplied input that influences how the fabric8-pipeline-library is configured. Use a whitelist approach.
      • Treat Configuration as Code: Apply the same security principles to pipeline configurations as to the code itself, especially when those configurations directly affect how the fabric8-pipeline-library is used.