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Security Hardening

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ancoleman
security-hardening

Reduces attack surface across OS, container, cloud, network, and database layers using CIS Benchmarks and zero-trust principles. Use when hardening production infrastructure, meeting compliance requirements, or implementing defense-in-depth security.

Overview

Publisherancoleman
Repositoryai-design-components
Skill namesecurity-hardening
Stars
523
Forks
73
Bundled files
13
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 13 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ancoleman on GitHub. Read the source before you install it.

Installation

Install the Security Hardening AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ancoleman/ai-design-components.git /tmp/ai-design-components
mkdir -p .claude/skills
cp -r /tmp/ai-design-components/skills/security-hardening .claude/skills/security-hardening
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Security Hardening in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Security Hardening on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Security Hardening is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Security Hardening

Purpose

Proactive reduction of attack surface across infrastructure layers through systematic configuration hardening, least-privilege enforcement, and automated security controls. Applies industry-standard CIS Benchmarks and zero-trust principles to operating systems, containers, cloud configurations, networks, and databases.

When to Use This Skill

Invoke this skill when:

  • Hardening production infrastructure before deployment
  • Meeting compliance requirements (SOC 2, PCI-DSS, HIPAA, FedRAMP)
  • Implementing zero-trust security architecture
  • Reducing container or cloud misconfiguration risks
  • Preparing for security audits or penetration tests
  • Automating security baseline enforcement
  • Responding to vulnerability scan findings

Hardening Layers

Security hardening applies across five infrastructure layers:

Layer 1: Operating System (Linux)

  • Kernel parameter tuning (sysctl)
  • SSH configuration hardening
  • User and group management
  • File system permissions and mount options
  • Service minimization
  • SELinux/AppArmor enforcement

Layer 2: Container

  • Minimal base images (Chainguard, Distroless, Alpine)
  • Non-root container execution
  • Read-only root filesystems
  • Seccomp and AppArmor profiles
  • Resource limits and capabilities dropping
  • Pod Security Standards enforcement

Layer 3: Cloud Configuration

  • IAM least privilege and MFA enforcement
  • Network security groups and NACL configuration
  • Encryption at rest and in transit
  • Public access blocking
  • Logging and monitoring enablement
  • CSPM (Cloud Security Posture Management) integration

Layer 4: Network

  • Default-deny network policies
  • Network segmentation and micro-segmentation
  • TLS/mTLS enforcement
  • Firewall rule minimization
  • DNS security (DNSSEC, DNS filtering)

Layer 5: Database

  • Authentication and authorization hardening
  • Connection encryption (SSL/TLS)
  • Audit logging enablement
  • Network isolation and access control
  • Role-based permissions with least privilege

Core Hardening Principles

1. Default Deny, Explicit Allow

Start with all access denied, explicitly permit only required operations. Apply default-deny firewall rules and network policies, then allow specific traffic.

2. Least Privilege Access

Grant minimum permissions required for operation. Use RBAC, IAM policies with specific resources, and database roles with limited permissions (no DELETE or DDL unless required).

3. Defense in Depth

Implement multiple overlapping security controls: network firewalls, authentication, authorization, audit logging, and encryption working together.

4. Minimal Attack Surface

Remove unnecessary components, services, and permissions. Use minimal container base images, disable unused services, and drop all Linux capabilities unless required.

5. Fail Securely

On error or misconfiguration, default to secure state. Authentication failures deny access, missing configurations use restrictive defaults, and monitoring failures trigger immediate alerts.

Hardening Priority Framework

Prioritize hardening efforts based on exposure and data sensitivity:

Critical Priority: Internet-Facing Systems

Apply immediately:

  • Container hardening (minimal images, non-root, read-only)
  • Network segmentation (DMZ, WAF, DDoS protection)
  • TLS termination and certificate management
  • Rate limiting and authentication
  • Real-time monitoring and alerting

Tools: Trivy, Falco, ModSecurity, Cloudflare

High Priority: Systems with Sensitive Data

Apply before production:

  • Encryption at rest (AES-256, KMS-managed keys)
  • Strict access controls (RBAC, least privilege)
  • Comprehensive audit logging
  • Database connection encryption
  • Regular vulnerability scanning

Tools: Checkov, Prowler, Lynis, OpenSCAP

Standard Priority: Internal Systems

Apply systematically:

  • OS hardening (CIS Benchmarks)
  • Service minimization
  • Patch management automation
  • Configuration management
  • Basic monitoring

Tools: Ansible, Puppet, kube-bench, docker-bench-security

CIS Benchmark Integration

CIS (Center for Internet Security) Benchmarks provide industry-standard hardening guidance.

Automated CIS Scanning

Docker CIS Benchmark:

bash
docker run --rm -it \
  --net host \
  --pid host \
  --cap-add audit_control \
  -v /var/lib:/var/lib:ro \
  -v /var/run/docker.sock:/var/run/docker.sock:ro \
  -v /etc:/etc:ro \
  docker/docker-bench-security

Kubernetes CIS Benchmark:

bash
kubectl apply -f https://raw.githubusercontent.com/aquasecurity/kube-bench/main/job.yaml
kubectl logs job/kube-bench

Linux CIS Benchmark:

bash
# Using Lynis
lynis audit system --quick

# Using OpenSCAP
oscap xccdf eval --profile xccdf_org.ssgproject.content_profile_cis \
  /usr/share/xml/scap/ssg/content/ssg-ubuntu2004-ds.xml

Key CIS Controls Mapping

CIS ControlHardening ActionLayer
4.1 Secure ConfigurationApply hardening baselinesAll layers
5.1 Account ManagementEnforce least privilege, MFAOS, Cloud
6.1 Access ControlRBAC, network policiesAll layers
8.1 Audit Log ManagementEnable comprehensive loggingAll layers
13.1 Network MonitoringDeploy IDS/IPS, flow logsNetwork
3.1 Data ProtectionEnable encryption at rest/transitCloud, Database

For detailed CIS control mapping, see references/cis-benchmark-mapping.md.

Container Base Image Selection

Choose base images based on security requirements and compatibility needs:

Use CaseRecommended BaseSizeCVEsTrade-off
Production appsChainguard Images~10MB0Minimal, zero CVEs
Minimal LinuxAlpine~5MBFewSmall, auditable
CompatibilityDistroless~20MBFewNo shell, harder debug
DebuggingDebian slim~80MBMoreHas debugging tools
Legacy appsUbuntu~100MBManyFull compatibility

Production recommendation: Chainguard Images or Distroless for production, Alpine for development.

Verification and Auditing

Hardening must be verified continuously, not just at implementation.

Automated Security Scanning

Container vulnerability scanning:

bash
# Trivy: Comprehensive vulnerability and misconfiguration scanner
trivy image --severity HIGH,CRITICAL myapp:latest

# Grype: Fast vulnerability scanner
grype myapp:latest

Infrastructure as Code scanning:

bash
# Checkov: Multi-cloud IaC scanner
checkov -d terraform/ --framework terraform

# Terrascan: Policy-as-code scanner
terrascan scan -t terraform -d terraform/

Kubernetes security scanning:

bash
# Kubesec: Security risk analysis
kubesec scan k8s/deployment.yaml

# Polaris: Configuration validation
polaris audit --format=pretty

# Trivy K8s scanning
trivy k8s --report summary cluster

Cloud security posture:

bash
# Prowler: AWS security assessment
prowler aws --services s3 iam ec2

# ScoutSuite: Multi-cloud security audit
scout aws --services s3 iam ec2

Continuous Verification Pipeline

Integrate security scanning into CI/CD:

yaml
# GitHub Actions example
name: Security Hardening Verification

on:
  push:
    branches: [main]
  schedule:
    - cron: '0 0 * * *'  # Daily scan

jobs:
  container-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Build image
        run: docker build -t myapp:test .

      - name: Scan with Trivy
        uses: aquasecurity/trivy-action@master
        with:
          image-ref: 'myapp:test'
          severity: 'CRITICAL,HIGH'
          exit-code: '1'  # Fail on findings

  iac-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Scan IaC with Checkov
        uses: bridgecrewio/checkov-action@master
        with:
          directory: terraform/
          framework: terraform
          soft_fail: false

Compliance Reporting

Generate compliance reports from scan results:

bash
# Generate CIS compliance report
kube-bench run --json > cis-report.json

# Generate vulnerability report
trivy image --format json --output vuln-report.json myapp:latest

# Aggregate reports for compliance dashboard
python scripts/generate-compliance-report.py \
  --cis cis-report.json \
  --vulns vuln-report.json \
  --output compliance-dashboard.html

Automation Tools

Hardening Automation

  • Ansible/Puppet/Chef: Configuration management for OS hardening
  • Terraform/Pulumi: Infrastructure as Code with security modules
  • Cloud Custodian: Cloud resource policy enforcement
  • OPA/Gatekeeper: Kubernetes policy enforcement
  • Kyverno: Kubernetes-native policy management

Scanning Tools

  • Trivy: Universal vulnerability and misconfiguration scanner
  • Checkov: IaC security and compliance scanner
  • Falco: Runtime security monitoring
  • Prowler: AWS security assessment tool
  • ScoutSuite: Multi-cloud security auditing
  • Lynis: Linux security auditing
  • docker-bench-security: Docker CIS benchmark scanner
  • kube-bench: Kubernetes CIS benchmark scanner

Monitoring Tools

  • Falco: Runtime threat detection for containers
  • Sysdig: Container security and monitoring
  • Wazuh: Host and endpoint security monitoring
  • OSSEC: Host-based intrusion detection

Quick Reference: Common Hardening Tasks

Harden SSH Access

bash
# Edit /etc/ssh/sshd_config.d/hardening.conf
PermitRootLogin no
PasswordAuthentication no
PermitEmptyPasswords no
MaxAuthTries 3
X11Forwarding no
ClientAliveInterval 300
ClientAliveCountMax 2

# Restart SSH
systemctl restart sshd

Harden Container Image

dockerfile
# Use minimal base
FROM cgr.dev/chainguard/python:latest

# Non-root user
USER nonroot

# Read-only filesystem
COPY --chown=nonroot:nonroot app /app
WORKDIR /app

# Drop all capabilities
ENTRYPOINT ["python", "-m", "app"]

Harden Kubernetes Pod

yaml
securityContext:
  runAsNonRoot: true
  runAsUser: 65534
  seccompProfile:
    type: RuntimeDefault
  allowPrivilegeEscalation: false
  readOnlyRootFilesystem: true
  capabilities:
    drop: ["ALL"]

Harden AWS S3 Bucket

hcl
resource "aws_s3_bucket_public_access_block" "secure" {
  bucket = aws_s3_bucket.data.id

  block_public_acls       = true
  block_public_policy     = true
  ignore_public_acls      = true
  restrict_public_buckets = true
}

resource "aws_s3_bucket_server_side_encryption_configuration" "secure" {
  bucket = aws_s3_bucket.data.id

  rule {
    apply_server_side_encryption_by_default {
      sse_algorithm = "aws:kms"
    }
  }
}

Harden Network with Default Deny

yaml
# Kubernetes NetworkPolicy: deny all ingress
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: default-deny-ingress
  namespace: production
spec:
  podSelector: {}
  policyTypes:
  - Ingress

Harden Database Access

sql
-- PostgreSQL hardening
REVOKE ALL ON DATABASE app FROM PUBLIC;
REVOKE ALL ON SCHEMA public FROM PUBLIC;

CREATE ROLE app_user WITH LOGIN;
GRANT CONNECT ON DATABASE app TO app_user;
GRANT SELECT, INSERT, UPDATE ON app.orders TO app_user;

-- Force SSL connections
ALTER SYSTEM SET ssl = on;
-- In pg_hba.conf: hostssl all all 0.0.0.0/0 scram-sha-256

Detailed Hardening Guides

For layer-specific hardening guidance:

  • OS hardening: See references/linux-hardening.md
  • Container hardening: See references/container-hardening.md
  • Cloud hardening: See references/cloud-hardening.md
  • Network hardening: See references/network-hardening.md
  • Database hardening: See references/database-hardening.md

For automation scripts:

  • Python automation: See scripts/harden-linux.py
  • Container host setup: See scripts/harden-container-host.sh
  • Compliance reporting: See scripts/generate-compliance-report.py
  • Infrastructure scanning: See scripts/scan-infrastructure.sh

For working examples:

  • Linux configurations: See examples/linux/
  • Kubernetes manifests: See examples/kubernetes/
  • Terraform modules: See examples/terraform/

Integration with Related Skills

  • auth-security: Authentication and authorization patterns complement hardening
  • secret-management: Secure secrets handling is essential for hardening
  • kubernetes-operations: Pod security and RBAC hardening
  • infrastructure-as-code: Security scanning in IaC pipelines
  • building-ci-pipelines: Automated security scanning integration
  • observability: Security monitoring and alerting
  • compliance-frameworks: Mapping hardening to compliance requirements

Anti-Patterns to Avoid

❌ Hardening only at deployment

  • Hardening is continuous; scan and verify regularly

❌ Applying all controls blindly

  • Prioritize based on risk and exposure

❌ No verification

  • Always verify hardening is applied and effective

❌ Security through obscurity

  • Obscurity is not security; use proven controls

❌ Hardening without testing

  • Test hardening changes don't break functionality

❌ Manual hardening at scale

  • Automate hardening for consistency and repeatability

Getting Started

  1. Assess current posture: Run CIS benchmark scans
  2. Prioritize: Internet-facing → sensitive data → internal
  3. Apply baseline hardening: OS, container, cloud basics
  4. Automate: Use scripts and IaC for consistency
  5. Verify continuously: Integrate scanning into CI/CD
  6. Monitor: Deploy runtime security monitoring
  7. Iterate: Review and improve hardening regularly

For step-by-step implementation, start with references/linux-hardening.md or references/container-hardening.md based on infrastructure type.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Security Hardening AI skill do?

Reduces attack surface across OS, container, cloud, network, and database layers using CIS Benchmarks and zero-trust principles. Use when hardening production infrastructure, meeting compliance requirements, or implementing defense-in-depth security.

Why use Security Hardening on TypingMind?

Because you install it once and use it with any model. Security Hardening is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Security Hardening in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ancoleman/ai-design-components/tree/main/skills/security-hardening. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Security Hardening?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Security Hardening?

As many as you like. As long as a model supports skills, you can use Security Hardening with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Security Hardening AI skill free?

Yes. It is published on GitHub by ancoleman under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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