Comparison · Strix vs Lakera Guard
Strix vs Lakera Guard: defend the model, govern the agent.
Lakera Guard defends the model from adversarial prompts and content-policy violations. Strix governs what your AI agent does after the model responds. Different layers of the same stack — most production AI deployments need both.
Answers the question: “Should I pick Lakera Guard or Strix to secure my AI agents?”
Execution control for AI systems
Intercept, evaluate, sign every state-changing action.
Real-time security for LLM applications — prompt injection, PII, content filtering
The bottom line
Both products exist for a reason. Here's when each is the right call.
- You need to govern what AI agents do — block, intercept, or approve actions in real time.
- Your auditor wants cryptographically signed evidence of every AI agent action.
- You need single-use, revocable execution tokens for human approval of high-risk actions.
- You need policy-decision evidence that does not depend on a SaaS vendor staying available.
- Your compliance program requires EU AI Act Article 12 / 14 / 28 mapping backed by signed records.
- Your primary concern is prompt injection, jailbreaks, or adversarial input against the model itself.
- You need PII detection and redaction on inbound or outbound LLM traffic.
- You need content-policy enforcement on what the model is allowed to say.
- Your threat model is 'the model misbehaves,' not 'the agent executes the wrong action.'
- You want the deepest adversarial-input research lab in the category.
Feature-by-feature
Each row is a specific capability. We've tried to be honest — there are categories where the other side wins.
| Capability | Strix | Lakera Guard |
|---|---|---|
Layer of the stack | Action layer — governs what the agent executes after the model responds | Input / output layer — defends the model from adversarial prompts and content violations |
Primary threat addressed | Unauthorized or unsafe agent actions; missing audit trail | Prompt injection, jailbreaks, PII leak, content policy violation |
Three-state decisions (ALLOW / DENY / INTERCEPT) | Built in; INTERCEPT triggers human approval for high-risk actions | Block / allow on content matches; not designed for human-in-the-loop approval flows |
Cryptographically signed evidence | Ed25519 signatures, public JWKS, third-party verifiable | Detection logs; signing is not a built-in primitive |
Single-use execution tokens | HMAC-signed, atomic redemption, revocable | Not part of Lakera's scope |
Prompt-injection detection | Not in scope — Strix governs the agent's actions, not its inputs | Best-in-class; multi-language, multi-modality, continuously updated |
PII detection / redaction | Public-redaction lint on Strix surfaces (operational); not a customer-facing PII surface | First-party PII detection on inbound and outbound LLM traffic |
Content-policy filtering | Not in scope | First-party — customizable content policies, severity levels |
Public verification API | /api/public/verify is unauthenticated, rate-limited, public | Detection results are private to the customer org |
Compliance mapping | NIST AI RMF, EU AI Act Art. 12/14/28, AARM mapped end-to-end | Compliance-relevant; mapping is the customer's job to deliver |
Adversarial research depth | Not in scope | Industry-leading prompt-injection research and dataset |
Open-source verifier | @strixgov/verifier on npm — offline, no Strix account | Detection runs against Lakera's hosted service |
Multi-framework support | Anthropic, OpenAI, LangChain, CrewAI middleware on roadmap | REST + SDK across most LLM stacks |
When to use which
Concrete scenarios. If your situation looks like one of these, the recommendation should be obvious.
My chatbot is getting jailbroken via prompt injection — users are extracting system prompts.
That's exactly Lakera Guard's threat model. Strix governs what the agent does, not how the model behaves under adversarial inputs.
My AI agent is calling tools that move money / change records / send emails, and I need a real-time approval gate plus signed evidence.
Strix's three-state decisions + execution tokens + signed evidence are exactly this. Lakera Guard would let the request through to the model and then to the tool.
We're deploying a production agent and we need both prompt-injection defense and execution governance.
Run them at different layers. Lakera Guard at the model boundary (input + output). Strix at the action layer (what the agent does next). Different threats; both real; both need a control.
My priority is preventing PII leakage in LLM outputs.
Lakera's PII detection on inbound and outbound is purpose-built. Strix's PII discipline is operational (lint, redaction in public surfaces) — not a runtime detection product.
My EU AI Act audit needs a record of every action my AI agent took, signed by my organization, verifiable by the auditor without me sending them an export.
Strix's signed evidence + public verifier matches that need directly. Lakera's detection logs would supplement the audit, not satisfy the action-level record-keeping requirement.
Common questions
Doesn't Lakera Guard cover everything I need for AI security?+
Depends on your threat model. If your concern is 'what the model says' (prompt injection, jailbreaks, PII, content), Lakera Guard is purpose-built for that. If your concern is 'what the agent does' (tool execution, irreversible actions, auditor-grade evidence), that's a different problem — at the action layer, not the prompt layer.
Can I build prompt-injection defense into Strix?+
Not as a primary path. Strix's policy engine evaluates capability + actor + intent + context — it's not a model-output classifier. You could add a 'prompt was screened' field to the policy context, but the screening itself is Lakera's domain.
Why publish a comparison if the products are at different layers?+
Because evaluators ask the question. The honest answer is 'different layers; pick by threat model; many teams need both.' The category is too noisy to leave the question unanswered.
Does Strix produce signed evidence of Lakera Guard detections?+
Not today. If the agent's policy context included a Lakera detection result, Strix's evidence record would include that field signed. The natural integration is: Lakera detects, the result becomes part of the Strix policy decision context, and the Strix evidence record carries the detection result cryptographically signed.
What about Protect AI, HiddenLayer, Calypso AI?+
All distinct from Strix. Protect AI and HiddenLayer focus on model security (adversarial ML, supply-chain). Calypso AI is enterprise LLM control (prompt firewall, content filter, model access). None ship cryptographically signed action-level evidence with a public verifier.
Production governance. Zero bypasses. One evidence trail.
Strix is running in production today — 127 capabilities defined, every decision recorded. See the governance kernel in action in 15 minutes.
Currently in private beta — limited spots available.
npx @strixgov/verifier@latest 5686