ModelRed

Free

ModelRed: Automated AI red teaming and security scoring for LLMs, provider-agnostic and CI/CD ready.

#AI security#red teaming#vulnerability assessment#large language models#LLMs#AI systems#security probe#OpenAI#Anthropic#Google#AWS#Azure#CI/CD pipelines#team governance#developer SDKs#comprehensive logging#free and paid tiers

Type

Saas

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About ModelRed

ModelRed is a cloud-based, provider-agnostic platform for AI security testing, red teaming, and vulnerability assessment focused on large language models (LLMs) and AI systems. It automates security probe execution across 10,000+ attack vectors, applies detector-based verdicts, and generates ModelRed Scores with detailed reports. With integrations for OpenAI, Anthropic, Google, AWS, Azure, and custom REST endpoints, ModelRed fits into CI/CD pipelines, offers team governance, developer SDKs, comprehensive logging, and flexible free and paid tiers to help organizations proactively uncover and remediate AI weaknesses.

Key Features

Automated Threat Probe execution against AI models

10,000+ evolving attack vectors with versioned Probe Packs

Detector-based verdicts to confirm attack success and vulnerabilities

ModelRed security scoring (ModelRed Score) and detailed reporting

Provider-agnostic integrations: OpenAI, Anthropic, Google, AWS, Azure

Custom REST API endpoint support for proprietary models

CI/CD pipeline integration for automated security gating

Team governance for roles, permissions, and collaboration

Developer SDK for extending and integrating ModelRed

Comprehensive test data capture and logging for auditability

Pros & Cons

Pros

Automates red teaming across 10,000+ attack vectors
Provider-agnostic with support for OpenAI, Anthropic, Google, AWS, Azure, and custom endpoints
Integrates seamlessly into CI/CD pipelines for automated security gates
Provides standardized ModelRed Score for easy benchmarking
Offers a free tier to start baseline security testing

Best For

Security teams: Continuously red team production LLMs to detect jailbreaks, prompt injection, and data exfiltration risks.MLOps/AI platform engineers: Integrate automated AI security gates into CI/CD to block risky model releases.Enterprises adopting GenAI: Assess vendor and in-house models across providers with standardized security scoring.Compliance and risk officers: Generate audit-ready reports demonstrating ongoing AI security controls and testing cadence.Startups building AI products: Use the free tier to baseline model security and upgrade as usage scales.Red team consultants: Leverage versioned probe packs and logging to run structured client assessments.Government and regulated orgs: Test for policy evasion, content safety, and data leakage across restricted domains.Product managers: Prioritize fixes using detector-based verdicts and ModelRed Scores tied to business impact.DevSecOps: Automate regression testing of model defenses after prompt guard or detector updates.AI research labs: Evaluate robustness of new model variants against evolving attack vectors.

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