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--- |
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license: mit |
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task_categories: |
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- text-classification |
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- token-classification |
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language: |
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- en |
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tags: |
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- security |
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- rl |
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- kubernetes |
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- terraform |
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- config-verification |
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- verifiers |
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- metadata-only |
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pretty_name: Security Verifiers E2 - Config Verification (Metadata) |
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size_categories: |
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- n<1K |
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configs: |
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- config_name: default |
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data_files: |
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- split: meta |
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path: data/meta-* |
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dataset_info: |
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features: |
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- name: section |
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dtype: string |
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- name: name |
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dtype: string |
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- name: description |
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dtype: string |
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- name: payload_json |
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dtype: string |
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- name: version |
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dtype: string |
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- name: created_at |
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dtype: string |
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splits: |
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- name: meta |
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num_bytes: 2380 |
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num_examples: 6 |
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download_size: 5778 |
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dataset_size: 2380 |
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--- |
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# 🔒 Security Verifiers E2: Security Configuration Verification (Public Metadata) |
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> **⚠️ This is a PUBLIC metadata-only repository.** The full datasets are hosted privately to prevent training contamination. See below for access instructions. |
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## Overview |
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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. |
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### Why Private Datasets? |
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**Training contamination** is a critical concern for benchmark integrity. If datasets leak into public training corpora: |
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- Models can memorize answers instead of learning to reason |
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- Evaluation metrics become unreliable |
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- Research reproducibility suffers |
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- True capabilities become obscured |
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By keeping evaluation datasets private with gated access, we: |
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- ✅ Preserve benchmark validity over time |
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- ✅ Enable fair model comparisons |
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- ✅ Maintain research integrity |
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- ✅ Allow controlled access for legitimate research |
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### Dataset Composition |
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The private E2 datasets include: |
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#### Kubernetes Configurations |
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- **Source**: Real-world K8s manifests from popular open-source projects |
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- **Scans**: KubeLinter, Semgrep, OPA/Rego policies |
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- **Violations**: Security misconfigurations, best practice violations |
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- **Severity**: Categorized (high/medium/low) based on tool outputs |
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#### Terraform Configurations |
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- **Source**: Infrastructure-as-code from real projects |
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- **Scans**: Semgrep, OPA/Rego policies, custom rules |
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- **Violations**: Security risks, compliance issues |
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- **Severity**: Weighted scoring for reward computation |
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### What's in This Repository? |
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This public repository contains: |
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1. **Sampling Metadata** (`sampling-*.json`): |
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- Source repository information |
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- File selection criteria |
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- Scan configurations |
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- Label distributions |
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- Reproducibility parameters |
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2. **Tools Versions** (`tools-versions.json`): |
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- KubeLinter version (pinned) |
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- Semgrep version (pinned) |
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- OPA version (pinned) |
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- Ensures reproducible scanning |
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3. **This README**: Instructions for requesting access |
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### Reward Components |
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E2 uses tool-grounded reward functions: |
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- **Detection Precision/Recall/F1**: Against ground-truth violations |
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- **Severity Weighting**: Higher reward for catching critical issues |
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- **Patch Delta**: Reward for proposed fixes that eliminate violations |
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- **Re-scan Verification**: Patches must pass tool validation |
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**Multi-turn performance**: Models achieve ~0.93 reward with tool calling vs ~0.62 without tools. |
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### Requesting Access |
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🔑 **To access the full private datasets:** |
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1. **Open an access request issue**: [Security Verifiers Issues](https://github.com/intertwine/security-verifiers/issues) |
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2. **Use the title**: "Dataset Access Request: E2" |
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3. **Include**: |
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- Your name and affiliation |
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- Research purpose / use case |
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- HuggingFace username |
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- Commitment to not redistribute or publish the raw data |
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**Approval criteria:** |
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- Legitimate research or educational use |
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- Understanding of contamination concerns |
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- Agreement to usage terms |
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We typically respond within 2-3 business days. |
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### Citation |
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If you use this environment or metadata in your research: |
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```bibtex |
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@misc{security-verifiers-2025, |
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title={Open Security Verifiers: Composable RL Environments for AI Safety}, |
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author={intertwine}, |
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year={2025}, |
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url={https://github.com/intertwine/security-verifiers}, |
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note={E2: Security Configuration Verification} |
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} |
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``` |
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### Related Resources |
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- **GitHub Repository**: [intertwine/security-verifiers](https://github.com/intertwine/security-verifiers) |
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- **Documentation**: See `EXECUTIVE_SUMMARY.md` and `PRD.md` in the repo |
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- **Framework**: Built on [Prime Intellect Verifiers](https://github.com/PrimeIntellect-ai/verifiers) |
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- **Other Environments**: E1 (Network Logs), E3-E6 (in development) |
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### Tools |
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The following security tools are used for ground-truth generation: |
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- **KubeLinter**: Kubernetes YAML linting and security checks |
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- **Semgrep**: Pattern-based static analysis for K8s and Terraform |
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- **OPA**: Policy-as-code validation with Rego |
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### License |
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MIT License - See repository for full terms. |
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### Contact |
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- **Issues**: [GitHub Issues](https://github.com/intertwine/security-verifiers/issues) |
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- **Discussions**: [GitHub Discussions](https://github.com/intertwine/security-verifiers/discussions) |
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--- |
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**Built with ❤️ for the AI safety research community** |
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