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Enterprise Layer Intelligence
Categories/Cybersecurity/Fabraix
Fabraix

Fabraix

Find security vulnerabilities and logic flaws in AI agents through continuous red-teaming

Pricing model

Free Tier

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Fabraix to test and secure autonomous AI systems against real-world adversarial attacks before deploying them. It runs blackbox red-teaming campaigns across multi-turn interactions, voice, text, and browser interfaces to uncover hidden flaws. The service probes customer support bots, coding assistants, and financial agents for prompt injections, sandbox escapes, credential leaks, and policy drift. You can run one-off penetration tests or integrate automated tests directly into your deployment pipeline to continuously verify agent safety and protect your systems from emerging exploit methods.

What Fabraix does

Simulate blackbox cyberattacks against autonomous software agentsDetect prompt injection vulnerabilities across multi-turn user sessionsAudit coding bots for unauthorized shell execution risksTest web-browsing agents on dynamically created adversarial websitesTrack security regressions automatically within CI/CD pipelinesGenerate vulnerability pentest reports with step-by-step reproduction flowsExecutes autonomous multi-turn adversarial attack campaigns against AI agentsConducts pure blackbox testing requiring no internal access or model weightsTests multi-modal inputs including voice, text, images, and browser environmentsDeploys parallel testing environments running thousands of concurrent simulationsSurfaces exploits like sandbox escapes, data exfiltration, and prompt injectionsIntegrates into CI/CD pipelines to re-test agents before production updates shipGenerates vulnerability reports with reproduction steps and remediation guidance

Pricing breakdown

Pricing model: Free tier

Tutorials & Demos

Frequently asked

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Fabraix, Nyx, AI red teaming, AI security testing, LLM penetration testing, prompt injection, jailbreak testing, AI agent security, adversarial verification, autonomous agent testing, blackbox security testing, model safety, AgentHarm, multi-agent security