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Lakera Guard

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Verdict

A real-time API that inspects prompts and model output for prompt injection, jailbreaks, system-prompt extraction and PII leakage, sitting between your application and any LLM.

Where it wins, where it doesn't

Pros

  • Model-agnostic — sits in front of hosted APIs or local models alike
  • Covers direct and indirect prompt injection, jailbreaks, system-prompt extraction and PII leakage
  • Filters are updated from a large, real-world attack dataset (Gandalf)

Cons

  • Closed-source commercial service — an external dependency on the request path
  • Detection-based: a high block rate is not a guarantee
  • Headline accuracy and latency numbers are vendor-published, not independently measured
Ideal forTeams shipping LLM features to production without an in-house security teamAgent and RAG pipelines exposed to untrusted inputOrganisations already standardised on Check Point

Key Features

  • ✦Real-time request and response scanning
  • ✦Single chat-completions-style API endpoint
  • ✦Prompt injection and jailbreak detection
  • ✦PII and data-leakage detection
  • ✦Self-hosted deployment option

Editorial note

Lakera Guard is a detection layer, not a guarantee. It scores every request in and every response out, blocks what crosses a threshold, and is model-agnostic — it works whether the model runs on a hosted API or on your own hardware. Its filters are trained partly on data from Gandalf, Lakera's public prompt-injection game, which has collected millions of real attack attempts. Check Point acquired Lakera in 2025 (reported around $300 million); the product now also ships as Check Point AI Guardrails, while the standalone Guard API and its free tier still exist. The detection-rate, latency and language-count figures Lakera publishes (98%+, sub-50ms, 100+ languages) are the vendor's own — treat them as a claim, not a measured result. The real trade-off is architectural: you add a network hop and an external dependency to every LLM call, in exchange for a maintained, continuously updated filter you do not have to build or keep current yourself.

Frequently Asked Questions

Who is Lakera Guard for?↓
Lakera Guard is a fit for teams shipping LLM features to production without an in-house security team, Agent and RAG pipelines exposed to untrusted input and Organisations already standardised on Check Point.
What are the drawbacks of Lakera Guard?↓
The trade-offs we record are: Closed-source commercial service — an external dependency on the request path, Detection-based: a high block rate is not a guarantee and Headline accuracy and latency numbers are vendor-published, not independently measured.
What does Lakera Guard do well?↓
Model-agnostic — sits in front of hosted APIs or local models alike, Covers direct and indirect prompt injection, jailbreaks, system-prompt extraction and PII leakage and Filters are updated from a large, real-world attack dataset (Gandalf).

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