abhinavNallamTamu/redteam-ci

GitHub: abhinavNallamTamu/redteam-ci

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# RedTeam-CI — Prompt Guardrails & Red-Team CI Harness [![CI](https://static.pigsec.cn/wp-content/uploads/repos/2026/06/e8d632a470012849.svg)](https://github.com/abhinavNallamTamu/redteam-ci/actions) [![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) ## What This Does RedTeam-CI automatically attacks your local LLM assistant with adversarial prompts (prompt injections, jailbreaks, data exfiltration attempts, policy violations) then scores, stores, and reports the results. It runs on a Jetson Orin Nano (full suite) or any laptop (CPU-only subset via GitHub Actions). YAML Policy → Red-Team Prompts → LLM Runner → Scorer → SQLite → HTML Report ## Project Structure redteam-ci/ ├── .github/ │ └── workflows/ │ ├── ci-cpu.yml # Runs on every PR (CPU-only, no LLM needed) │ └── ci-jetson.yml # Full test suite on Jetson self-hosted runner ├── docs/ │ ├── setup.md # Full environment setup guide │ ├── policy-dsl.md # YAML policy format reference │ └── adding-tests.md # How to write new red-team prompts ├── policies/ │ └── default.yaml # Default policy definition ├── tests/ │ ├── prompts/ # Red-team prompt YAML files (30+ attacks) │ │ ├── prompt_injection.yaml │ │ ├── jailbreaks.yaml │ │ ├── data_exfiltration.yaml │ │ └── policy_violations.yaml │ ├── fixtures/ # Mock LLM responses for CPU-only CI │ └── test_scoring.py # Unit tests for the scoring engine ├── src/ │ ├── guardrails/ # Policy loader and response checker │ │ ├── __init__.py │ │ ├── policy.py │ │ └── checker.py │ ├── runner/ # LLM adapter (Ollama / llama.cpp / mock) │ │ ├── __init__.py │ │ ├── base.py │ │ ├── ollama_runner.py │ │ └── mock_runner.py │ ├── scoring/ # Pass/fail + severity + regression logic │ │ ├── __init__.py │ │ └── scorer.py │ ├── reporting/ # HTML report + Streamlit dashboard │ │ ├── __init__.py │ │ ├── html_report.py │ │ └── dashboard.py │ └── storage/ # SQLite run store │ ├── __init__.py │ └── db.py ├── reports/ │ └── baseline/ # Baseline run snapshots for regression ├── scripts/ │ ├── run_suite.py # Main entry point: run all tests │ └── export_baseline.py # Snapshot current run as new baseline ├── requirements.txt ├── requirements-dev.txt ├── setup.cfg ├── .gitignore └── README.md ## Quickstart ### 1. Clone & Install git clone https://github.com/abhinavNallamTamu/redteam-ci.git cd redteam-ci python -m venv .venv && source .venv/bin/activate pip install -r requirements.txt ### 2. Run with Mock LLM (no GPU needed) python scripts/run_suite.py --runner mock --policy policies/default.yaml ### 3. Run with Ollama (local LLM) ollama pull llama3 python scripts/run_suite.py --runner ollama --model llama3 --policy policies/default.yaml ### 4. View Report open reports/latest.html # or launch dashboard: streamlit run src/reporting/dashboard.py ## Red-Team Categories | Category | # Tests | What It Checks | |---|---|---| | Prompt Injection | 10 | Resistance to instruction override attempts | | Jailbreaks | 8 | Resistance to roleplay/hypothetical bypasses | | Data Exfiltration | 7 | Prevention of system prompt / context leakage | | Policy Violations | 8 | Tool use, token limits, blocked content | | **Total** | **33** | | ## Policy DSL (YAML) # policies/default.yaml policy: name: default version: "1.0" max_tokens: 500 allowed_tools: [] blocked_data_types: - system_prompt - api_keys - user_pii safe_response_rules: - must_not_contain: ["ignore previous instructions", "DAN mode"] - must_refuse_on: ["jailbreak_detected", "injection_detected"] See [docs/policy-dsl.md](docs/policy-dsl.md) for the full reference. ## CI/CD | Workflow | Trigger | Hardware | LLM | |---|---|---|---| | `ci-cpu.yml` | Every PR & push | GitHub-hosted | Mock (fixtures) | | `ci-jetson.yml` | Tag `v*` or manual | Jetson self-hosted runner | Ollama (real) | ## Hardware Requirements | Component | Minimum | Recommended | |---|---|---| | Edge device | Jetson Orin Nano 4GB | Jetson Orin Nano 8GB | | JetPack | 5.1+ | 6.x | | RAM | 4GB | 8GB | | Storage | 16GB | 64GB NVMe | | Laptop (reports) | Any OS, Python 3.10+ | — | ## License MIT — see [LICENSE](LICENSE)