Docker Build Args: The Ultimate Guide for Data Professionals

Security Implications and Risk Mitigation

While build arguments offer flexibility, they also introduce potential security risks that you need to understand to keep your Docker images secure.

Argument leakage vectors

Build arguments aren't as private as you might think. The values of build args can leak through several vectors that many developers overlook. First, the Docker image history exposes all your build args, meaning anyone with access to your image can run docker history to see the values you used during build time, including potentially sensitive information like API keys or tokens.

docker history flaskapp:dev

Image 4 - Docker history

Intermediate layers can also leak build arguments. Even if you use a multi-stage build and don't copy the layer containing sensitive information, the build arg values remain in the layer cache on the build machine. This means anyone with access to the Docker daemon on your build server could potentially extract this information.

CI/CD logs pose another significant risk. Most CI/CD systems log the entire build command, including any build args passed on the command line.

For example, this entire command, including API_KEY, will be visible in CI/CD logs:

docker build --build-arg API_KEY=secret123 -t myapp:latest .

These logs are often accessible to a broader group of developers or even stored in third-party systems, which increases the exposure surface of sensitive values.

Secure alternatives

For sensitive information, you should use Docker BuildKit's secrets feature instead of build args. BuildKit lets you mount secrets during the build process without persisting them in the final image or the history.

Here's an example Dockerfile that uses Docker BuildKit secrets:

FROM python:3.13-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .

# Use secret mount instead of build arg
RUN --mount=type=secret,id=api_key \
    cat /run/secrets/api_key > api_key.txt

EXPOSE 5000
CMD ["python", "app.py"]

To build this image with a secret, first create the api_key.txt file:

echo "my-secret-api-key" > api_key.txt

And then build the image with the secret:

DOCKER_BUILDKIT=1 docker build \
  --secret id=api_key,src=api_key.txt \
  -t flaskapp:secure .

Image 5 - Building images with BuildKit secrets

For configuration that needs to be available at runtime, environment variables remain the preferred approach. They can be set when starting a container without being baked into the image:

# Set environment variables at runtime
docker run -e API_KEY=secret123 -p 5000:5000 flaskapp:latest

To safely handle sensitive configuration in production, consider using container orchestration platforms like Kubernetes, which provide secure methods like Secrets for managing sensitive information. Docker Swarm also offers similar capabilities with its secrets management.

> Docker isn't the only containerization platform. Read about Podman to find out which tool is better-suited for you.

Remember, the rule of thumb is: use build args for build-time configuration that isn't sensitive, use BuildKit secrets for sensitive build-time needs, and use environment variables or orchestration platform secrets for runtime configuration.

Integration with Container Ecosystems

Now with the basics out of the way, let's explore how to leverage build arguments within broader Docker ecosystems and modern build tools.

Docker Compose

Docker Compose makes it easy to define and share build arguments across your multi-container applications.

If you want to follow along, here's a complete project structure for two simple Flask services:

flask-project/
├── docker-compose.yml         # Main compose configuration
├── .env                       # Environment variables for compose
├── web/
│   ├── Dockerfile             # Web service Dockerfile
│   ├── app.py                 # Flask application
│   └── requirements.txt       # Python dependencies
├── api/
│   ├── Dockerfile             # API service Dockerfile
│   ├── app.py                 # API Flask application  
│   └── requirements.txt       # API dependencies

I'll quickly go over the file contents one by one:

web/app.py

from flask import Flask, render_template

app = Flask(__name__)


@app.route("/")
def index():
    return render_template("index.html", title="Flask Web App")


@app.route("/about")
def about():
    return "About page - Web Service"


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000, debug=True)

web/requirements.txt

flask==3.1.0

web/Dockerfile

# Web service Dockerfile
ARG PYTHON_VERSION=3.13

FROM python:${PYTHON_VERSION}-slim

# Redeclare the build arg to use it after FROM
ARG PYTHON_VERSION
ARG ENVIRONMENT=production

# Set environment variables
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1
ENV FLASK_ENV=${ENVIRONMENT}

WORKDIR /app

# Copy and install requirements
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY . .

# Create a simple template folder and file if not exists
RUN mkdir -p templates && \
    echo "<html><body><h1>Hello from Web Service</h1><p>Python version: ${PYTHON_VERSION}</p><p>Environment: ${ENVIRONMENT}</p></body></html>" > templates/index.html

EXPOSE 5000

CMD ["python", "app.py"]

api/app.py

from flask import Flask, jsonify

app = Flask(__name__)


@app.route("/api/health")
def health():
    return jsonify({"status": "healthy"})


@app.route("/api/data")
def get_data():
    return jsonify(
        {"message": "Hello from the API service", "items": ["item1", "item2", "item3"]}
    )


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8000, debug=True)

api/requirements.txt

flask==3.1.0

api/Dockerfile

ARG PYTHON_VERSION=3.13

FROM python:${PYTHON_VERSION}-slim

# Redeclare the build arg to use it after FROM
ARG PYTHON_VERSION
ARG ENVIRONMENT=production

# Set environment variables
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1
ENV FLASK_ENV=${ENVIRONMENT}

WORKDIR /app

# Copy and install requirements
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY . .

# Add metadata label based on build args
LABEL python.version="${PYTHON_VERSION}" \
      app.environment="${ENVIRONMENT}"

EXPOSE 8000

CMD ["python", "app.py"]

.env

PYTHON_VERSION=3.10
ENVIRONMENT=development

docker-compose.yml

services:
  web:
    build:
      context: ./web
      dockerfile: Dockerfile
      args:
        PYTHON_VERSION: ${PYTHON_VERSION:-3.13}
        ENVIRONMENT: ${ENVIRONMENT:-development}
    ports:
      - "5000:5000"
    depends_on:
      - api
    
  api:
    build:
      context: ./api
      dockerfile: Dockerfile
      args:
        PYTHON_VERSION: ${PYTHON_VERSION:-3.13}
        ENVIRONMENT: ${ENVIRONMENT:-production}
    ports:
      - "8000:8000"

In a nutshell, this is a really simple way of starting two applications in isolated environments, both at the same time, both with a specific set of build args.

You can use this command to run the multi-container setup:

docker compose up --build

Image 6 - Build args with Docker Compose

After a couple of seconds, you'll see both services running:

Image 7 - Build args with Docker Compose (2)

It's quite a verbose setup for applications as simple as these two, but you get the big picture—build arguments are set in the compose file and referenced in individual Dockerfiles.

On a side note, you can also pass variable values from your environment to build args, which is handy for CI/CD pipelines:

services:
  web:
    build:
      context: .
      args:
        - PYTHON_VERSION=${PYTHON_VERSION:-3.10}

And to override these values when running docker-compose, you can use environment variables:

PYTHON_VERSION=3.10 docker-compose up --build

This approach allows you to maintain consistent builds across your team while still offering flexibility when needed.

BuildKit features

BuildKit, Docker's modern build engine, supercharges your build process with features that make using build args that much more powerful. When you enable BuildKit (by setting DOCKER_BUILDKIT=1), you unlock a couple of new capabilities.

Inline cache improves build performance by leveraging the remote image's build cache. You can specify a source image to use as a cache with the --cache-from flag, and BuildKit will intelligently use that image's layers:

DOCKER_BUILDKIT=1 docker build \
  --build-arg PYTHON_VERSION=3.10 \
  --cache-from flaskapp:dev \
  -t flaskapp:latest .

Another valuable BuildKit feature is deterministic outputs, which ensure that your builds are reproducible. This is crucial when you want exactly the same image regardless of when or where it's built:

# Build with deterministic output
DOCKER_BUILDKIT=1 docker build \
  --build-arg PYTHON_VERSION=3.10 \
  --build-arg BUILD_DATE=2025-05-01 \
  --output type=docker,name=flaskapp:stable \
  .

BuildKit's secret redaction in logs protects sensitive information during builds. 

Unlike regular build args, which appear in build logs, BuildKit automatically hides the values of secrets:

# Create a file with the API key
echo "my-api-key-value" > ./api-key.txt

# Build with secret (value won't appear in logs)
DOCKER_BUILDKIT=1 docker build \
  --secret id=api_key,src=./api-key.txt \
  -t flaskapp:secure .

In your Dockerfile, you'd access this secret with:

RUN --mount=type=secret,id=api_key \
    cat /run/secrets/api_key > /app/config/api_key.txt

These BuildKit features work together with build args to create a more secure, efficient, and reproducible build system that can adapt to virtually any containerization workflow.

Best Practices for Enterprise Adoption

Implementing Docker build arguments across large teams requires standardized approaches, at least if you want to minimize confusion and maximize security and performance benefits.

When adopting build args in enterprise environments, start with proper argument validation. 

Always provide sensible default values for every build argument to ensure builds don't fail unexpectedly when arguments aren't passed. You should also add validation checks within your Dockerfile to verify that critical build args meet your requirements.

Here's an example that validates Python version:

ARG PYTHON_VERSION=3.9
# Validate Python version
RUN if [[ ! "$PYTHON_VERSION" =~ ^3\.[0-9]+$ ]]; then \
      echo "Invalid Python version: ${PYTHON_VERSION}. Must be 3.x"; \
      exit 1; \
    fi

Layer caching optimization is crucial for reducing build times in CI/CD pipelines. 

Order your Dockerfile instructions to maximize cache hits by placing rarely changing content (like dependency installation) before frequently changing content (like application code). 

When using build args that might change often, place them after stable operations to avoid invalidating the entire cache:

FROM python:3.13-slim

WORKDIR /app

# Dependencies change less frequently - good cache utilization
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Build args that might change often come AFTER stable operations
ARG BUILD_NUMBER
ARG COMMIT_SHA
# Now these args won't invalidate the dependency cache

Metadata management through build args improves traceability and compliance. Use build args to inject build-time metadata as labels:

ARG BUILD_DATE
ARG VERSION
ARG COMMIT_SHA
ARG BUILD_URL

LABEL org.opencontainers.image.created="${BUILD_DATE}" Can you please provide me with access to this document?
      org.opencontainers.image.version="${VERSION}" \
      org.opencontainers.image.revision="${COMMIT_SHA}" \
      org.opencontainers.image.url="${BUILD_URL}"

These standardized labels follow the Open Container Initiative (OCI) specification, which means your images will be compatible with different container management tools.

Documentation is often overlooked, but it is critical for enterprise adoption. 

Create a clear README section that documents all available build args, their purpose, default values, and usage examples. Include which build args are required versus optional, and document any interactions between different arguments. Consider adding this information to your CI/CD pipeline configuration files as comments as well, to help developers understand what values they need to provide:

# Example documented CI configuration
build_job:
  script:
    # BUILD_NUMBER: Unique identifier for this build (required)
    # ENVIRONMENT: Target deployment environment [dev|staging|prod] (required)
    # PYTHON_VERSION: Python version to use (optional, default: 3.9)
    - docker build
      --build-arg BUILD_NUMBER=${CI_PIPELINE_ID}
      --build-arg ENVIRONMENT=${DEPLOY_ENV}
      --build-arg PYTHON_VERSION=${PYTHON_VERSION:-3.9}
      -t myapp:${CI_PIPELINE_ID} .

For truly enterprise-grade adoption, consider implementing a build argument schema that can be versioned and validated. This approach lets you evolve your build configuration over time while maintaining backward compatibility.

Comparative Analysis: Build Args vs. Environment Variables

If you want to create secure and flexible Docker images that follow best practices, you must understand when to use build arguments and when to use environment variables.

Here's a table you can reference at any time:

The key difference lies in their timing and purpose. Build args configure the build process itself, while environment variables configure the running application. For example, you'd use a build arg to select which base image to use (ARG PYTHON_VERSION=3.10), but you'd use an environment variable to tell your application which port to listen on (ENV PORT=8080).

From a security perspective, build args pose a higher risk for sensitive information because they remain in the image history. 

Consider this workflow for handling different types of configuration:

# Build-time configuration (non-sensitive)
ARG PYTHON_VERSION=3.13
FROM python:${PYTHON_VERSION}-slim

# Build-time secrets (use BuildKit secrets)
RUN --mount=type=secret,id=build_key \
    cat /run/secrets/build_key > /tmp/key && \
    # Use the secret for build operations
    rm /tmp/key

# Runtime configuration (non-sensitive)
ENV LOG_LEVEL=info

# Runtime secrets (should be injected at runtime, not here)
# This would be provided at runtime with:
# docker run -e API_KEY=secret123 myimage

In practice, a common pattern is to convert selected build args to environment variables when they need to be available at runtime:

ARG APP_VERSION=1.0.0
# Make the version available at runtime
ENV APP_VERSION=${APP_VERSION}

For enterprise applications, the separation allows for different teams to manage different aspects of the application lifecycle. DevOps teams can modify build args for image construction, while operators can adjust environment variables during deployment without rebuilding images.

Summing up Docker Build Args

Docker build args aren't just another technical feature - they're your go-to tool for creating images that work consistently across your team and environments. 

I've walked through how to use them in simple Python projects, all while keeping your sensitive information safe. You've also seen how to integrate build args with tools like Docker Compose.

Think of build args as the foundation of a flexible containerization strategy. Start small by parameterizing your Python version in your next project. Once that feels comfortable, try adding conditional logic for different environments. Before you know it, you'll create sophisticated multi-stage builds that your team can customize without modifying the Dockerfile.

Ready to dive deeper? These courses from DataCamp are your best next stop:

• Introduction to Docker
• Intermediate Docker
• Containerization and Virtualization Concepts
• Containerization and Virtualization with Docker and Kubernetes
• CI/CD for Machine Learning