BLAFS: Bloat-Aware Filesystem for Container Debloating

Introduction

Most “container image slimming” projects start as a cost exercise and end as a security one: fewer packages means fewer CVEs to triage, faster pulls, and less blast radius when something goes wrong.

BLAFS (Bloat-Aware Filesystem), introduced in February 2025, approaches debloating from a different angle: it observes what files a container actually touches at runtime, then removes the rest. The goal is smaller images and faster deployments without hand-curating minimal base images for every workload.

Where BLAFS pays off (and where it doesn’t)

• Best fit: large fleets with slow cold-starts, bandwidth-constrained clusters, or strict vulnerability budgets.
• Good fit: workloads with predictable execution paths (APIs, workers) where “used files” converge quickly.
• Be cautious: highly dynamic plugins/extensions, “debug shells in prod,” or workloads that load code paths only during rare incidents.
• Rollout style: treat it like a performance/security optimization—stage it, measure, and keep a rollback path.

Intelligent File Detection

• Runtime analysis monitors actual file access patterns during container execution to identify truly unused files.
• Usage tracking tracks which files are opened, read, or executed by container processes.
• Dependency analysis understands file dependencies to ensure critical files are never removed.
• Multi-stage analysis performs analysis across multiple execution scenarios to capture all potential file usage.

Automated Debloating

1. File removal automatically removes files that are never accessed during container runtime.
2. Safe debloating ensures that removing files won’t break container functionality through dependency checks.
3. Incremental debloating supports iterative debloating where containers can be optimized over multiple runs.
4. Rollback capability maintains the ability to restore removed files if needed for debugging or troubleshooting.

Container Size Reduction

• Significant savings typically reduces container image sizes by 20-60% depending on the base image and workload.
• Faster pulls smaller images download faster, reducing container startup time and network bandwidth usage.
• Storage efficiency reduces storage requirements in container registries and on cluster nodes.
• Cost optimization lowers storage and transfer costs, especially important for large-scale deployments.

Security Benefits

• Attack surface reduction fewer files mean fewer potential vulnerabilities and attack vectors.
• Compliance improvement helps meet security compliance requirements for minimal container footprints.
• CVE reduction removing unused packages and libraries eliminates vulnerabilities in unused components.
• Audit simplification smaller containers are easier to audit and validate for security compliance.

Performance Impact

• Faster deployments smaller images deploy faster, improving CI/CD pipeline performance.
• Reduced memory smaller filesystem images consume less memory during container execution.
• Improved cache efficiency smaller images improve Docker layer caching and registry performance.
• Network optimization reduced image transfer times improve cluster scaling and pod scheduling performance.

Kubernetes Integration

• Admission controller can be deployed as a Kubernetes admission controller to automatically debloat images.
• Image scanning provides pre-deployment scanning and debloating of container images.
• Policy enforcement enables policies for automatic debloating based on organizational requirements.
• Monitoring provides metrics and reporting on debloating effectiveness and container size reductions.

Use Cases

• Production optimization optimizes production container images for smaller size and improved performance.
• CI/CD pipeline integration automatically debloats images as part of the build and deployment process.
• Multi-architecture support debloats images across different architectures for consistent optimization.
• Legacy container modernization helps modernize legacy containers by removing unnecessary dependencies.

Getting Started

# Install BLAFS tools
curl -L https://github.com/blafs/blafs/releases/latest/download/install.sh | bash

# Analyze a container image
blafs analyze my-image:latest

# Debloat an image
blafs debloat my-image:latest -o my-image-debloated:latest

# Verify debloated image
docker run my-image-debloated:latest

Deploy BLAFS as a Kubernetes admission controller:

apiVersion: v1
kind: ConfigMap
metadata:
  name: blafs-config
  namespace: kube-system
data:
  policy.yaml: |
    autoDebloat: true
    minSizeReduction: 20
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: blafs-admission
  namespace: kube-system
spec:
  replicas: 1
  selector:
    matchLabels:
      app: blafs-admission
  template:
    metadata:
      labels:
        app: blafs-admission
    spec:
      containers:
      - name: blafs
        image: blafs/admission-controller:latest
        volumeMounts:
        - name: config
          mountPath: /etc/blafs
      volumes:
      - name: config
        configMap:
          name: blafs-config

Technical Details

• Filesystem layer operates at the filesystem layer to track and manage file access.
• Kernel integration integrates with container runtime kernels for efficient file tracking.
• Compatibility works with Docker, containerd, and other OCI-compliant container runtimes.
• Multi-format support supports various container image formats and distributions.

Summary

BLAFS addresses a critical need in the Kubernetes ecosystem, providing automated container optimization that reduces bloat without manual effort, resulting in more efficient, secure, and cost-effective container deployments.