Gate AI
A proxy that sits between your app and the LLM API, blocking prompt-injection attacks and stripping leaked secrets before they reach the model — with an audit trail for every request.
🔗 Visit Gate AI
Description
If you've ever left your front door open because you trusted everyone on the street, you'll recognize the problem AI agents have: they follow instructions from wherever those instructions come from — a webpage they were told to read, a file they were told to summarize, a support ticket they were told to answer — and they can't always tell your instructions apart from an attacker's hidden ones. Gate AI is a checkpoint that sits between your app and the AI model, reading every request and response and stopping anything that looks like a hijack attempt before it does damage.
Technically, Gate AI is a proxy layer for LLM/agent traffic (Anthropic, OpenAI, Google, xAI) that screens for prompt injection — both direct (a user pasting a malicious instruction) and indirect (an attacker hiding instructions inside a document, webpage, or tool output the agent later reads) — plus PII and credential leakage in both directions. It claims a 97.4% F1 score on public prompt-injection benchmarks (12,111 samples across 16 benchmark sets), alongside token compression (20%+ claimed savings) and prompt caching to offset the extra hop's latency and cost. Every blocked or flagged request is logged to an audit trail anchored on a blockchain for tamper-evidence — a feature aimed at regulated teams who need to prove, after the fact, exactly what an agent was and wasn't allowed to do.
💬 Our review
The short version: security proxies for AI agents are becoming a real category as agents get real permissions (file access, tool calls, payments), and Gate AI's benchmark numbers put it in the same conversation as established players like Lakera Guard — but it's a young, single-vendor product asking you to route all your AI traffic through it, and a self-reported F1 score is a big ask without independent verification.
Where Gate AI differentiates is the blockchain-anchored audit trail — useful if cryptographically provable logs of agent behavior matter to your compliance team, overkill if they don't — and the free tier (20,000 requests/month) makes it cheap to pilot before committing. The catch is the one every inline security proxy shares: it becomes a hard dependency in your request path, so its own uptime and latency now bound your app's. Enterprise pricing is fully opaque (contact sales only), and there's no visible open-source component or independent audit to check the injection-detection claims against, so treat the 97.4% figure as a vendor number until you've tested it on your own traffic. If you're already deep in one AI vendor's ecosystem, check whether that vendor's built-in moderation covers most of the need before adding a third party to the critical path.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Free : 20 000 requêtes/mois, rétention logs 30 jours. Pro : 20$/utilisateur/mois — 200 000 requêtes/mois, rétention 90 jours, scan prompt injection, redaction PII/PHI, compression tokens 20%+. Enterprise : sur devis.
Pros
Score F1 97.4% revendiqué sur 16 benchmarks publics (12 111 échantillons)
Compression de tokens intégrée (20%+ visé) qui compense en partie le coût du proxy
Audit trail immuable ancré blockchain — utile en contexte réglementé
Tier gratuit généreux pour piloter (20k requêtes/mois)
Cons
Aucun composant open-source ni audit indépendant visible pour vérifier les chiffres de détection
Devient une dépendance critique dans le chemin de requête
Tarif Enterprise totalement opaque
Produit jeune, pas de traction communautaire publique