Secure Containers Without Breaking Functionality

Secure your containers without breaking what works

The challenge of container security

Container images often include extraneous components such as operating system packages, development tools, debugging utilities, and unused libraries. These elements increase the attack surface and create unnecessary complexity during audits, patching, and risk assessments. For example, a widely used NGINX image has been identified with 274 categorized vulnerabilities, highlighting the scale of the issue.

Traditional manual approaches to addressing this problem involve developers reviewing images and making subjective decisions about which components to remove. This method is inefficient, inconsistent across teams, and risky when applied to production environments. A component deemed unnecessary during static analysis might be critical under specific workloads, leading to potential outages that outweigh the benefits of reduction.

The solution: behavior-driven hardening

The solution lies in shifting from static analysis to behavior-driven hardening. While software bills of materials (SBOMs) and vulnerability scans reveal what is present in an image, runtime observations provide insights into what is actually executed. This distinction forms the basis of a runtime bill of materials (RBOM), which tracks components actively used during execution.

According to the content, “A component deemed unnecessary during static analysis might be critical under specific workloads, leading to potential outages that outweigh the benefits of reduction.”

Combining SBOM and RBOM data allows teams to differentiate between installed components and those in active use, enabling more precise hardening decisions.

The hardening process

Stage 1: Workload profiling

Profiling the workload under realistic conditions is essential. This goes beyond basic testing to include functional test suites, staging traffic, production-like loads, stress testing, and fuzzing. The goal is to establish a comprehensive baseline of the application’s behavior, including loaded binaries, configurations, and network interactions.

Stage 2: Component removal

Using the baseline to guide component removal requires careful analysis. An execution-path map identifies unutilized elements, allowing teams to decide which to eliminate. The intensity of removal varies based on the service’s risk profile: standard approaches target unused components, conservative methods focus on vulnerable packages, and aggressive strategies may apply to low-risk internal services. Explicit keep lists ensure critical components remain intact when justified.

Stage 3: Validation

Validating the hardened image is a mandatory step. Functional and integration tests must be rerun, and results compared to the original image. The unmodified version should remain as a rollback option. Validation serves as a release gate, preventing untested images from deployment.

Benefits and automation

The benefits of this structured approach include reduced exposure to threats and lower operational complexity without altering application logic. Automating this process within CI/CD pipelines enhances scalability. Tools like GitHub Actions, GitLab CI, and Jenkins can integrate profiling, execution-path tracking, image regeneration, validation, and drift detection.

Automated alerts notify teams of unexpected changes, such as new components appearing or existing ones disappearing. Fleet-level dashboards provide visibility into hardened images, remaining components, and decision logs.

Ongoing process and human oversight

Container hardening is an ongoing process. As dependencies evolve, baseline data and validation procedures must be regularly updated. Organizations can start by applying the method to a single service, measuring image size and functionality before and after hardening.

Automation does not replace human oversight. Security and engineering teams must still determine removal thresholds, manage exceptions, investigate validation failures, and approve compliance-critical releases. A one-size-fits-all approach is impractical; external-facing services may require stricter controls, while internal systems might allow more aggressive reduction.

Conclusion

Secure images should contain only what is necessary for workload execution, with all additions justified by operational requirements. The broader takeaway is that security improves through evidence-based, continuously validated steps rather than reactive vulnerability reporting.