Securie vs Lakera Guard
Lakera Guard is the runtime AI-safety specialist — it filters LLM input and output at the API gateway for prompt injection, jailbreaks, and PII leakage. Securie is a full PR-to-attestation platform whose llm-safety component (Llama Guard 4 + prompt-inj CI gate) covers a similar surface as one piece of a broader stack. This page is the honest comparison.
Teams comparing Securie and Lakera Guard are usually answering one of two distinct questions. The first: 'we have an AI-powered product, prompt injection is our top runtime risk, which tool is the specialist?' For that question, Lakera Guard is the right answer — they have shipped this product longer than anyone else in the category, the depth of their detection scope reflects that focus, and the procurement model (per-call) matches the runtime-only use case. The second question: 'we ship an AI-built app with multiple risk surfaces, which tool covers them all?' For that question, Lakera does not — it is purpose-built for runtime LLM I/O, not for code-level vulnerabilities (RLS, secrets, auth) which dominate the actual incident frequency on AI-built-app teams.
The honest comparison reflects this. Lakera is the AI-runtime-guard specialist; Securie is a full-stack platform that includes a similar runtime guard component. They overlap on the runtime-LLM-I/O surface, where Lakera is deeper. They diverge on every other surface, where Securie covers and Lakera does not. The procurement decision should match category to need: AI-runtime-only need → Lakera. Multi-surface AI-app need → Securie. Both → run both, with Lakera at the API gateway and Securie at PR + deploy time.
This page covers the tradeoff honestly without overclaiming Securie's depth on a slice where Lakera is structurally deeper.
Lakera Guard is the AI-runtime-guard specialist — purpose-built for filtering LLM I/O at the API gateway, deep coverage on prompt injection / jailbreaks / PII / hate-speech / safe-content classification. Securie is a full security platform whose llm-safety component covers the same surface (Llama Guard 4 wrapper + prompt-inj 0.90 CI gate + multimodal-guard + RAG-guard + MCP-guard) but as one piece among many. Pick Lakera if AI-runtime-guarding is your ONLY security concern. Pick Securie if you also need code scanning, RLS verification, leaked-secret detection, broken-auth specialists, and attestation across your stack.
Feature comparison
| Securie | Lakera Guard | |
|---|---|---|
| Prompt injection detection (runtime) | Llama Guard 4 ingress + egress filter on every Router::complete (factory builds real client when LLAMA_GUARD_URL set; StubLlamaGuard regex fallback in dev) | Lakera Guard's flagship — multilingual prompt-injection + jailbreak detection at API gateway latency // TODO: verify current detection-class scope |
| Prompt-inj CI-gate (build-time) | corpora/prompt-inj-corpus.jsonl + .github/workflows/prompt-inj-gate.yml + ≥0.90 resistance floor | Out of scope — Lakera Guard is runtime, not CI-build-time |
| Multimodal prompt-injection (images/PDFs) | multimodal-guard crate — images + PDF ingress check | Lakera Guard supports multimodal // TODO: verify current multimodal coverage |
| RAG poisoning detection | rag-guard crate with poisoning_score; corpus-integrity check | Out of primary scope — Lakera focuses on prompt-injection at I/O, not corpus integrity |
| MCP tool scope guarding | mcp-guard ScopeGuard + default catalog (git/filesystem/http with safe scopes) | Out of scope |
| Code SAST | Day-1 specialists (Supabase RLS, secrets, broken auth) + ~20 code-complete | Out of scope — Lakera is runtime AI-safety, not SAST |
| Supabase RLS specialist | Day-1 production-validated | Out of scope |
| Leaked-secret detection | secret_scanner specialist with live-key validation (11 providers) | Out of scope |
| Broken-auth (BOLA/BFLA/IDOR) | Day-1 production-validated specialist with intent-graph reasoning + sandbox | Out of scope |
| Audit attestation | Signed in-toto + DSSE + Sigstore-rekor (Ed25519, KMS-backed in production) | API-call audit logs; not cryptographically attested per scan |
Where the difference shows up in practice
A user submits a prompt-injection attempt inside a customer-support chatbot
Lakera Guard: Lakera Guard is in the request path. The prompt is classified before reaching the LLM. If the classification flags prompt injection, the request is blocked at the gateway and the user receives a refusal. Lakera's detection scope catches a wide range of prompt-injection variants including indirect (content retrieved from the web that contains injection text) and multimodal (images encoding instructions).
Securie: Securie's llm-safety wraps every Router::complete call. Llama Guard 4 (real HTTP via LLAMA_GUARD_URL) classifies both the input and the output. The Llama Guard 4 baseline catches the well-known prompt-injection patterns; sophisticated variants (indirect injection through retrieved content, novel jailbreak formulations) may pass — Llama Guard 4 is not as deeply specialized as Lakera's purpose-built classifier. For mainstream prompt-injection coverage, Securie is sufficient; for cutting-edge variants, Lakera is deeper.
A new prompt-injection technique is discovered in the wild (academic paper or X thread)
Lakera Guard: Lakera updates their detection model with the new technique. The runtime API begins blocking the new variant within the model-update window. Customers benefit from the update with no code change — the API is centrally managed.
Securie: Securie's prompt-inj-corpus.jsonl is updated with the new technique. The CI gate now requires the new technique to be detected with ≥0.90 resistance. Any inference-router / specialists / agent-* PR that touches affected code must pass the updated corpus. Llama Guard 4 itself updates on Meta's release schedule; if Llama Guard 4 misses the new technique, Securie's corpus catches it as a regression but does not block the runtime call directly. The lag between technique-discovery and Securie-blocking is longer than Lakera's centrally-updated API.
An MCP tool with implicit filesystem write scope is invoked by an LLM agent
Lakera Guard: Out of scope. Lakera Guard's surface is LLM input/output classification, not MCP tool scope enforcement.
Securie: Securie's mcp-guard ScopeGuard checks every tool invocation against the registered scope catalog. If the MCP server is registered with `git/filesystem/http with safe scopes` (the R6-T5 default catalog) and the agent attempts a filesystem write outside the safe scope, the call is rejected. Tenant-pinned manifests via TrustedCatalog (pubkey-pinned) lock the scope to operator-approved values. This is a Securie-specific surface that complements but does not overlap Lakera's coverage.
A leaked OpenAI API key in source code
Lakera Guard: Out of scope — Lakera Guard is runtime AI-safety, not source-code secret scanning.
Securie: Securie's secret_scanner specialist detects the OpenAI key pattern and live-validates against the OpenAI API. If the key is active, the finding ships with 'Live key confirmed' + an auto-rotate PR (Indie tier and up). Validated exposure, not pattern-match possibility. Out-of-scope for Lakera; well-covered by Securie.
The deeper tradeoff
Lakera Guard and Securie have a fundamentally different product shape. Lakera is a runtime API — your application calls Lakera before passing user input to an LLM, and Lakera classifies the input as safe / prompt-injection / jailbreak / PII / hate-content / etc. The classification happens at API-gateway latency. The product is purpose-built for the runtime-AI-safety category, with the depth of detection that comes from focusing on one surface for years.
Securie's llm-safety component is one piece of a broader stack. The Router::with_safety_filter wrapper attaches Llama Guard 4 (real HTTP when LLAMA_GUARD_URL is set, StubLlamaGuard regex fallback in dev) to every LLM call inside Securie's pipeline. The same filter wraps prompt-injection detection at runtime, but the surface it covers is what Llama Guard 4 covers — not the broader detection scope Lakera has built specifically for prompt-injection.
The honest framing is that Securie's llm-safety is sufficient for a typical AI-app launch posture, with the 0.90 CI gate floor as an explicit guarantee against regression. The prompt-inj corpus + the gate creates a structural commitment: any change to specialists / agent-* / inference-router that drops the corpus's resistance below 0.90 fails the build. This is a different shape of guarantee than Lakera's runtime detection — it is a build-time control, not a runtime API.
For teams whose threat model is 'sophisticated user attempts prompt-injection at runtime, we need to detect and block,' Lakera's specialist depth is the answer. For teams whose threat model is 'we want a build-time gate ensuring our LLM stack does not regress against a known corpus, plus runtime filtering on top,' Securie's combination of CI gate + Llama Guard 4 is the answer.
The broader procurement reality is that AI-runtime risk is one slice of an AI-built-app team's actual risk surface. Looking at incident frequency on AI-built apps in 2025-2026, the top causes are: leaked credentials in source code, missing RLS on Supabase tables, broken authorization on user-data routes, and middleware bypasses. Prompt injection at runtime is a real risk but materially less frequent in incident data. For most AI-app teams, the procurement question is not 'Lakera vs Securie' but 'do we need code scanning + RLS + secrets + auth coverage AND a runtime AI guard.' Securie answers that with one platform; Lakera answers the runtime slice with depth that complements but does not replace the code-scanning surface.
The pricing model also encodes the difference. Lakera's per-call meter scales with LLM traffic — high-traffic AI apps pay more, low-traffic apps pay less. Securie's flat tier is independent of LLM traffic, so the cost structure suits teams with bursty or growing AI usage where per-call meters compound unpredictably. For enterprise AI apps with material runtime traffic, Lakera may run higher cost than Securie's enterprise tier; for low-traffic dev / launch posture, the opposite.
Pricing
Free ($0) · Indie ($12) · Solo Founder ($49) · Startup ($299). Capped-envelope monthly.
Per-API-call or per-million-tokens pricing; Free tier with limits; Enterprise. // TODO: verify lakera.ai/pricing — pricing model has shifted between published tiers vs sales-led; refresh before publish.
Migration playbook
Step 1: Identify whether your risk surface is runtime-AI-only or multi-surface
What: Inventory: do you have a customer-facing LLM API, MCP server, AI agent consuming untrusted input? Do you ALSO have a typical web application with auth, database, secrets? The first question alone says Lakera; the combination says Securie or both.
Why: Lakera Guard is purpose-built for one surface. Securie covers many. The procurement decision starts with which surfaces matter on your stack.
Gotchas: Beware reasoning that 'we are an AI app so AI-runtime is the only risk' — incident data on AI-built apps shows code-level vulnerabilities (RLS, secrets, auth) dominate frequency. AI-runtime is real but not the only risk; the code-level surface is rarely the right thing to deprioritize.
Step 2: If runtime-AI-only: pick Lakera Guard
What: Integrate Lakera Guard at your API gateway. Run for two weeks; measure detection rate, false-positive rate on legitimate user input, latency overhead.
Why: Lakera is the specialist; for runtime-AI-only need, the depth wins.
Gotchas: Per-call pricing scales with traffic. Model your expected runtime traffic before committing to a tier — Lakera's economics shift materially between low-traffic launches and scaled production.
Step 3: If multi-surface: pick Securie or run both
What: Install Securie's GitHub App for PR-time + deploy-time + post-merge coverage of the full Ring 1 surface (RLS, secrets, auth, AI-features, supply chain, attestation). If your runtime-AI surface needs deeper specialist coverage than Llama Guard 4 provides, ALSO integrate Lakera Guard at the API gateway.
Why: The two-tool combination is the pattern for teams where AI-runtime is one risk surface among many. Lakera complements Securie on the AI-runtime layer without overlapping the code-scanning layer.
Gotchas: Verify with both vendors that running both is a supported configuration. They are complementary by design but if you discover a conflict at integration time, vendor support windows differ.
Step 4: Decide based on threat-model + traffic profile
What: After two weeks of evaluation, weigh: detection depth (Lakera deeper on runtime AI; Securie deeper on code surface), procurement model (per-call vs flat tier), and operational fit (does runtime API call latency fit your SLO budget; does PR-time scanning fit your CI budget).
Why: Both tools have honest scope. The decision is matching scope to need; vendor pitches do not substitute for measurement.
Gotchas: Threat-model exercises tend to weight the most-recently-discussed risk highest. Look at incident data, not headlines. AI-runtime is a real risk but historically not the highest-frequency one for AI-built apps.
Step 5: If running both: configure clear ownership boundaries
What: Lakera at API gateway = runtime AI-I/O. Securie at PR + deploy + post-merge = code surface + supply chain + attestation. Document the boundary so the two tools' findings are categorized consistently.
Why: Tool-overlap on the same risk produces noise. Explicit boundaries prevent that.
Gotchas: Re-evaluate the boundary if either tool ships new categories. Lakera occasionally extends into code-adjacent surfaces (PII detection in source); Securie occasionally extends runtime AI surfaces. Quarterly boundary review keeps the configuration coherent.
When to pick Lakera Guard
Your application's primary risk surface is the runtime LLM I/O — you have a customer-facing chatbot, an AI agent that consumes user input, an MCP server exposed to untrusted callers, or a public LLM-powered API. Prompt injection / jailbreaks / PII-leak / hate-content classification at the API-gateway latency is the question, and you need the depth of a specialist tool to answer it.
When to pick Securie
Your application has multiple risk surfaces — code-level vulnerabilities (Supabase RLS, leaked secrets, broken auth) AND runtime AI risk — and you want one platform covering all of them. Securie's llm-safety component handles the AI-runtime slice; the rest of the stack covers everything else. The procurement model also differs: Securie is $0-299/mo capped-envelope; Lakera is per-call.
Bottom line
Lakera wins on AI-runtime-guard depth — they have shipped this product longer, the category is their primary product, and their detection surface for prompt-injection variants is materially broader. Securie wins on full-stack coverage — for teams shipping AI-built apps, the runtime LLM guard is one risk surface among several (RLS misconfiguration, leaked secrets, broken auth are all higher-frequency than prompt-injection on a typical AI-app codebase). The honest answer depends on whether AI-runtime guarding is the question or one of several questions.
FAQ
Can Lakera Guard and Securie run together?
Yes — many production stacks should. Lakera Guard at the API gateway as the deep runtime guard; Securie at PR + deploy time as the full-stack scanner. The two cover different layers of the same application. The choice between them depends on whether AI-runtime is your only concern (Lakera alone) or one of several (Securie alone, or Lakera + Securie if you want the deepest possible runtime AI-safety + the broader stack).
Does Securie's Llama Guard 4 wrapper match Lakera Guard's depth?
Honestly, no — not on prompt-injection alone. Lakera Guard is the specialist tool for that exact surface and has shipped it longer; their detection-class scope (prompt injection variants, jailbreak patterns, indirect prompt injection through retrieved content, multimodal vectors) is materially broader than the Llama Guard 4 baseline that Securie wraps. The honest framing: Securie's llm-safety is sufficient for the typical AI-app launch posture (covers the well-known prompt-injection patterns with the 0.90 CI gate floor), but a team whose threat model includes sophisticated runtime attacks should treat Lakera as the deeper specialist. Securie's value is the integration with the rest of the stack, not best-in-class on this one slice.
What about the prompt-injection CI gate? Does Lakera have one?
Lakera Guard is a runtime API — it does not (at the time of writing) ship a build-time CI gate that fails the build below a resistance threshold. Securie's prompt-inj-corpus.jsonl + .github/workflows/prompt-inj-gate.yml is an explicit build-time control: any PR that touches inference-router, specialists, agent-*, or llm-safety must keep the corpus's resistance ≥0.90, or the build fails. The two controls are complementary — Lakera blocks at runtime, Securie's gate blocks at merge.
How does the procurement model compare?
Lakera Guard is typically priced per API call or per-million-tokens; the meter is your runtime LLM traffic. Securie is per-tenant capped-envelope subscription ($0/$12/$49/$299/Enterprise). For low-traffic AI apps the cost models differ materially: Lakera scales with your AI traffic, Securie is fixed. For high-traffic AI apps, Lakera's per-call cost may exceed Securie's flat tier; for low-traffic apps the inverse. Model your specific traffic before deciding.
We have an MCP server. Does Lakera cover MCP-tool-scope abuse?
Lakera Guard's primary surface is LLM input/output. MCP tool scope abuse is a different surface — the LLM may issue valid-looking tool calls that exceed the intended scope. Securie's mcp-guard crate ships with a default catalog (git / filesystem / http with safe scopes per R6-T5) and supports tenant-pinned manifests (TrustedCatalog with pubkey-pinning). For MCP-specific risk, Securie's coverage is the better fit; Lakera complements at the LLM-I/O layer.