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AI Security Freemium

ModelRed

Web Application 4.3/5 WebAPIPython SDK

What is ModelRed?

Bulletproof your AI models with adaptive red teaming. Hunt down vulnerabilities in LLMs with 10,000+ evolving attack vectors.

ModelRed is an AI security and red teaming platform that helps organizations identify vulnerabilities in LLMs, agents, RAG pipelines, and custom AI systems. It uses 10,000+ evolving attack vectors to catch jailbreaks, prompt injections, data leaks, and unsafe behavior before deployment. The platform integrates with major AI providers like OpenAI, Anthropic, Google, AWS, and Azure, and offers a developer-first approach with CI/CD gates, version-controlled attack patterns, and reproducible verdicts. It provides a single 0-10 security score and supports team governance with shared probe packs.

Key Features

10,000+ evolving attack vectors
Red team any AI system (LLMs, agents, RAG, custom APIs)
CI/CD gates that fail builds on high-risk findings
Version-controlled attack patterns
Reproducible verdicts from dedicated LLM detectors
Single 0-10 security score tracking over time
Compare results across models, providers, and versions
Export findings to Slack, Jira, or ticketing systems
Team governance with private, shared, or public probe packs
Zero-setup integration—just point to your AI endpoint
Audit trails and compliance reporting
Python SDK available
Works with OpenAI, Anthropic, Google, AWS, Azure, HuggingFace, and more
Local model support via Ollama
Multi-turn manipulation and conversation attack testing

Use Cases

Security teams test LLM-powered chatbots for jailbreaks and prompt injections before production deployment, reducing the risk of harmful outputs by 90%.
AI developers integrate ModelRed into their CI/CD pipeline to automatically fail builds when high-risk vulnerabilities are found, ensuring only secure models reach production.
Compliance officers generate audit trails and security reports for AI systems, demonstrating adherence to internal policies and regulatory requirements.
Product teams compare security scores across different model providers and versions, selecting the most robust AI for their application.
DevOps engineers use the Python SDK to automate red teaming tests in staging environments, catching data leakage and unsafe behavior early in the development cycle.
Research teams evaluate custom fine-tuned models for bias amplification and fairness violations, iterating on training data to improve model safety.
Customer support teams deploy AI agents that have been vetted against tool misuse and unauthorized function calls, preventing unintended actions in live systems.
AI securityred teamingLLM vulnerabilitiesjailbreak detectionprompt injectiondata leakageCI/CD securityAI governance
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Frequently Asked Questions

What does ModelRed do?

Bulletproof your AI models with adaptive red teaming. Hunt down vulnerabilities in LLMs with 10,000+ evolving attack vectors.

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