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SPECIFICTION
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Red Team LLM Lab

github.com

WindowsSelf-hosted
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Spec score 6.6 #28 of 29 in AI Security Testing Tools

Spec sheet

Purpose
Red Team LLM Lab is an open-source framework for testing large language model robustness against security attacks.Read 7 Oct 2026 · github.com
Intended users
The project names security researchers, secure AI application developers, educators, and organizations conducting internal security audits as intended users.Read 7 Oct 2026 · github.com
Platforms
The setup instructions show a virtual environment command for Windows and describe running the lab with Python scripts; they do not list supported operating systems as a formal platform matrix.Read 7 Oct 2026 · github.com
Integrations
Support for OpenAI and Anthropic API testing is listed on the project roadmap.Read 7 Oct 2026 · github.com
Support
The repository directs users to GitHub Issues for bugs or feature requests and Discussions for conversation.Read 7 Oct 2026 · github.com
License
The project states that it is licensed under the MIT License.Read 7 Oct 2026 · github.com
Analysis modules
Its modules include a prompt analyzer with risk scoring, a jailbreak simulator, a honeypot orchestrator, a backdoor tester, and a master dashboard.Read 7 Oct 2026 · github.com
Attack testing
It includes prompt injection testing, jailbreak simulation with 14+ techniques, honeypot agents, backdoor detection, prompt analysis, and RAG poisoning tests.Read 7 Oct 2026 · github.com
Backdoor detection
Its backdoor tester checks for hidden triggers, including Unicode triggers.Read 7 Oct 2026 · github.com
Defense testing
The framework tests input validation and sanitization, pattern-based injection detection, and output filtering for information leaks.Read 7 Oct 2026 · github.com
Honeypots
It provides intentionally vulnerable honeypot agents for studying attack patterns.Read 7 Oct 2026 · github.com
Jailbreak simulation
It tests guardrail effectiveness using 14 or more jailbreak techniques.Read 7 Oct 2026 · github.com
Model setup
The installation instructions download a quantized Phi-3 Mini model, and the project is built with llama-cpp-python.Read 7 Oct 2026 · github.com
Notable limits
The roadmap also lists a web UI, Docker support, GPU acceleration, function-calling tests, and CI/CD examples as planned items.Read 7 Oct 2026 · github.com
Prompt injection
It tests for vulnerabilities to prompt manipulation and includes static prompt analysis with risk scoring.Read 7 Oct 2026 · github.com
RAG security
It can test retrieval-augmented generation poisoning attacks.Read 7 Oct 2026 · github.com
Reporting
It saves JSON logs with metadata and provides security posture scoring, category-based vulnerability analysis, and automated recommendations.Read 7 Oct 2026 · github.com
Requirements
The stated prerequisites are Python 3.8 or higher, 4GB RAM minimum (8GB recommended), and about 4GB disk space for models.Read 7 Oct 2026 · github.com
Security guidance
The project recommends isolated or offline test environments, authorization, careful handling of results, and responsible vulnerability disclosure.Read 7 Oct 2026 · github.com

Where Specifiction places it

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