Build arguments feel temporary, but Docker records them in build metadata and image layers. That means anyone with access to the image can inspect history, caches, or metadata and recover exposed values. The risk is not only disclosure at build time, but long lived persistence across environments, which makes secret removal much harder after publication.
Why This Matters for Security Teams
Docker build arguments are often treated as harmless placeholders, but they can become a secret leakage path when teams use them for passwords, tokens, API keys, or certificate material. The issue is not limited to the moment of the build. Values can persist in build history, cached layers, logs, and downstream artifacts, which makes exposure broad and difficult to fully reverse once an image is published. The NIST Cybersecurity Framework 2.0 is useful here because the risk sits across identify, protect, detect, and recover activities, not just one build step.
Security teams often underestimate this because build arguments look separate from runtime configuration, and developers may assume that a temporary variable disappears when the build completes. In practice, that assumption breaks when images are shared across registries, pipelines, and environments, or when build output is retained for troubleshooting. This creates an identity and credential governance problem as much as a container security problem, especially when non-human identities, CI systems, and deployment automation all touch the same secrets. In practice, many security teams encounter this only after a published image has already been reused across multiple environments.
How It Works in Practice
Docker build arguments are passed into the build process at image creation time. They are useful for non-sensitive parameters such as version numbers or package repository mirrors, but they are a poor choice for secrets because build systems may expose them indirectly. Values can appear in image metadata, command traces, layer diffs, cache records, or CI logs depending on how the build is configured. Once an image is built and pushed, those records may travel with it even if the original source file is later corrected.
Practical risk increases when teams confuse build-time configuration with secret handling. A secure build process usually separates these concerns by using secret mounts, short-lived tokens, or external secret managers rather than passing sensitive data as arguments. This is also where non-human identity governance matters: the build system, registry, and deployment agent each act as a distinct identity and should have only the minimum permissions needed to complete the workflow. The OWASP Non-Human Identity Top 10 is relevant because machine identities often become the path through which these secrets are created, stored, or reused.
- Use build arguments only for non-sensitive configuration values.
- Pass secrets through dedicated secret mechanisms, not Dockerfile ARG instructions.
- Limit registry and CI permissions so build outputs are not broadly readable.
- Scan images, build logs, and caches for accidental secret retention.
- Rotate any secret that may have been exposed during a prior build.
The operational goal is to prevent secrets from becoming part of the image supply chain at all, rather than trying to clean them up after publication. These controls tend to break down when legacy pipelines reuse cached layers across shared runners because old metadata can survive long after the original build job has finished.
Common Variations and Edge Cases
Tighter build controls often increase pipeline complexity and developer friction, requiring organisations to balance secret safety against speed and maintainability. That tradeoff is especially visible in teams that build many images per day or rely on older Docker workflows where rebuilds are expensive.
Best practice is evolving for multi-stage builds, BuildKit secret mounts, and external secret brokers, but there is no universal standard for every environment yet. A build argument may still be acceptable for a harmless version string, release label, or feature flag, but the line becomes risky when the value can grant access to data, infrastructure, or third-party services. The key test is whether the value would be damaging if it were recoverable from image metadata months later.
This risk is highest in shared CI/CD environments, regulated workloads, and agent-driven deployment flows where multiple non-human identities interact with the same artifact. If an AI agent or automation script can trigger builds, fetch secrets, and publish images, then the governance model must account for both the build process and the machine identity behind it. In those environments, secret exposure is often a supply chain issue rather than a single misconfigured Dockerfile.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | PR.DS | Secret leakage through image metadata is a data security and protection issue. |
| OWASP Non-Human Identity Top 10 | CI systems and registries are non-human identities that often mishandle build secrets. | |
| NIST AI RMF | Agentic automation in builds raises governance and accountability concerns. |
Treat build agents and pipelines as identities with least privilege and secret lifecycle controls.
