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intertwine-ai
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security-verifiers-e2-metadata

Tasks:
Text Classification
Token Classification
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security
rl
kubernetes
terraform
config-verification
verifiers
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metadata
license: mit
task_categories:
  - text-classification
  - token-classification
language:
  - en
tags:
  - security
  - rl
  - kubernetes
  - terraform
  - config-verification
  - verifiers
  - metadata-only
pretty_name: Security Verifiers E2 - Config Verification (Metadata)
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: meta
        path: data/meta-*
dataset_info:
  features:
    - name: section
      dtype: string
    - name: name
      dtype: string
    - name: description
      dtype: string
    - name: payload_json
      dtype: string
    - name: version
      dtype: string
    - name: created_at
      dtype: string
  splits:
    - name: meta
      num_bytes: 2380
      num_examples: 6
  download_size: 5778
  dataset_size: 2380

🔒 Security Verifiers E2: Security Configuration Verification (Public Metadata)

⚠️ This is a PUBLIC metadata-only repository. The full datasets are hosted privately to prevent training contamination. See below for access instructions.

Overview

E2 is a tool-grounded configuration auditing environment for Kubernetes and Terraform. This repository contains only the sampling metadata that describes how the private datasets were constructed.

Why Private Datasets?

Training contamination is a critical concern for benchmark integrity. If datasets leak into public training corpora:

  • Models can memorize answers instead of learning to reason
  • Evaluation metrics become unreliable
  • Research reproducibility suffers
  • True capabilities become obscured

By keeping evaluation datasets private with gated access, we:

  • ✅ Preserve benchmark validity over time
  • ✅ Enable fair model comparisons
  • ✅ Maintain research integrity
  • ✅ Allow controlled access for legitimate research

Dataset Composition

The private E2 datasets include:

Kubernetes Configurations

  • Source: Real-world K8s manifests from popular open-source projects
  • Scans: KubeLinter, Semgrep, OPA/Rego policies
  • Violations: Security misconfigurations, best practice violations
  • Severity: Categorized (high/medium/low) based on tool outputs

Terraform Configurations

  • Source: Infrastructure-as-code from real projects
  • Scans: Semgrep, OPA/Rego policies, custom rules
  • Violations: Security risks, compliance issues
  • Severity: Weighted scoring for reward computation

What's in This Repository?

This public repository contains:

  1. Sampling Metadata (sampling-*.json):

    • Source repository information
    • File selection criteria
    • Scan configurations
    • Label distributions
    • Reproducibility parameters

  2. Tools Versions (tools-versions.json):

    • KubeLinter version (pinned)
    • Semgrep version (pinned)
    • OPA version (pinned)
    • Ensures reproducible scanning

  3. This README: Instructions for requesting access

Reward Components

E2 uses tool-grounded reward functions:

  • Detection Precision/Recall/F1: Against ground-truth violations
  • Severity Weighting: Higher reward for catching critical issues
  • Patch Delta: Reward for proposed fixes that eliminate violations
  • Re-scan Verification: Patches must pass tool validation

Multi-turn performance: Models achieve ~0.93 reward with tool calling vs ~0.62 without tools.

Requesting Access

🔑 To access the full private datasets:

  1. Open an access request issue: Security Verifiers Issues
  2. Use the title: "Dataset Access Request: E2"
  3. Include:
    • Your name and affiliation
    • Research purpose / use case
    • HuggingFace username
    • Commitment to not redistribute or publish the raw data

Approval criteria:

  • Legitimate research or educational use
  • Understanding of contamination concerns
  • Agreement to usage terms

We typically respond within 2-3 business days.

Citation

If you use this environment or metadata in your research:

@misc{security-verifiers-2025,
  title={Open Security Verifiers: Composable RL Environments for AI Safety},
  author={intertwine},
  year={2025},
  url={https://github.com/intertwine/security-verifiers},
  note={E2: Security Configuration Verification}
}

Related Resources

Tools

The following security tools are used for ground-truth generation:

  • KubeLinter: Kubernetes YAML linting and security checks
  • Semgrep: Pattern-based static analysis for K8s and Terraform
  • OPA: Policy-as-code validation with Rego

License

MIT License - See repository for full terms.

Contact

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