Fabraix
An adversarial verification platform that runs offensive attack simulations against AI agents to find and block vulnerabilities before real attackers do, with custom pricing on request.
🔗 Visit Fabraix
Description
An AI agent that passes normal testing can still have a blind spot nobody thought to probe — a prompt injection path, a tool it can be tricked into misusing — and the only reliable way to find that is to attack it the way a real adversary would before it ships. Fabraix builds automated red-teaming agents that continuously hunt for exactly that kind of vulnerability in customer-facing AI systems.
It combines offensive attack simulation with runtime defense, positioning itself as an adversarial verification layer rather than a one-time pentest: instead of a report handed over after a single engagement, it's meant to run continuously against production agent deployments and flag exploitable weaknesses as they're found. Pricing isn't published on the site — it's a custom-quote model, which is typical for security assessment products where scope varies heavily by the size and complexity of what's being tested.
💬 Our review
The short version: Fabraix is a red-teaming-as-a-service platform for AI agents specifically, which fills a gap that generic pentesting firms don't cover well — most traditional security testing wasn't built to probe prompt injection or agent tool-misuse patterns.
The lack of public pricing makes it hard to comparison-shop without reaching out directly, and as an ongoing adversarial-testing service rather than a one-time audit, the cost model likely scales with how much agent surface area needs continuous probing. For a team shipping a customer-facing AI agent with real permissions (payments, data access, tool execution), continuous red-teaming is a meaningfully different risk posture than a single pre-launch pentest — the tradeoff is committing to an ongoing relationship rather than a fixed one-time cost.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Tarification sur devis uniquement, contact commercial requis.
Pros
Spécialisé dans le red-teaming d'agents IA, pas un pentest générique
Simulation d'attaque offensive continue plutôt qu'un audit ponctuel
Défense runtime combinée à la vérification adversariale
Approche taillée pour les failles spécifiques aux agents (prompt injection, abus d'outils)
Cons
Tarification non publique, comparaison difficile
Modèle de service continu, pas un audit ponctuel à coût fixe
Nécessite un agent IA déjà en production ou proche du lancement pour être pertinent