Lakera Guard alternative — or the complement
Lakera Guard filters LLM input/output at runtime. Securie prevents the bugs that make runtime filtering necessary. Not mutually exclusive; here's when each makes sense.
Lakera Guard is a runtime safety layer for LLM applications — sitting between your application and the model, intercepting user inputs and model outputs, applying a classifier trained on prompt-injection and jailbreak patterns, and returning a block / allow decision per request. For high-volume LLM applications where untrusted user input reaches a powerful model (customer-support chatbots, AI coding assistants, agent-based systems with tool access), Lakera's runtime filter is a legitimate defense layer.
The tradeoff is architectural. Runtime filters catch symptoms; they do not fix the underlying bugs. If your application is vulnerable to prompt injection because the system prompt trusts user-supplied content without separating trust boundaries, no runtime filter will fix the design flaw — it will only intercept a subset of the attacks against it. The attacker's job becomes finding a payload that slips past the classifier, not re-architecting their attack.
Securie addresses the same problem from the opposite direction: at code-review time, before the application is deployed. The Securie prompt-injection specialist reads the LLM call site, models the trust boundary between system prompt and user input, and flags architectural bugs — a user input spliced into the system prompt without delimiters, a tool-scope that allows the model to access resources outside its intended scope, a RAG corpus loaded from an untrusted source. Fixing the bug at the source removes the entire class of exploit; the runtime filter becomes a defense-in-depth layer rather than a required stopgap.
This page is for teams deciding where on the defense spectrum their budget should sit. Both code-time and runtime defenses have a place; for most teams, code-time is the higher-leverage starting point because the fix is permanent.
Why people leave Lakera Guard
- Runtime filter — doesn't fix the underlying tool-scope or input-handling bugs
- Per-API-call billing scales fast with inference volume
- Narrow to LLM guardrails; doesn't cover AIBOM, vector DB security, etc.
Where Lakera Guard actually breaks down
Runtime filters have a detection ceiling
Example: Lakera Guard's classifier is trained on known prompt-injection patterns — explicit instruction hijacking, role-play jailbreaks, obfuscated system-prompt extraction. As the classifier improves, attackers develop payloads designed to evade it: unicode-obfuscated instructions, multi-turn setup patterns that look benign until the payload turn, indirect injections via document content that the classifier does not see as user input.
Impact: No public classifier reaches 100% detection. Independent benchmarks of LLM-safety classifiers in 2025 show detection rates of 85-95% on in-distribution attacks and materially lower on out-of-distribution novel attacks. The 5-15% miss rate means that at scale (millions of requests per day), some attacks reach the model regardless of filtering.
Per-API-call pricing scales with inference volume
Example: Lakera Guard's pricing structure is a base subscription plus per-API-call metering — each filter decision on an input and each filter decision on an output is a billable call. At 1 million daily requests × 2 filter calls per request × 30 days, the monthly bill scales quickly. Teams with high-volume LLM applications see the filter cost approach 10-40% of their LLM inference cost.
Impact: At LLM scale, the per-call cost makes the filter a material line item. Teams either reduce filtering depth (skip the output filter, sample rather than filter every request) to contain cost, or absorb the full metered cost. Either path introduces pressure the original decision to 'add runtime safety' did not anticipate.
Narrow to LLM guardrails — does not cover AIBOM, vector DB security, agent-tool-scope
Example: Lakera's scope is the prompt → model → response path. It does not produce an AI Bill of Materials (EU AI Act Article 11 documentation), does not detect RAG corpus poisoning upstream, does not model agent tool-scope boundaries, does not audit vector database access patterns. Teams with AI-feature-heavy applications often discover they need multiple AI-security products: Lakera for runtime, something else for AIBOM, something else for RAG hygiene.
Impact: The AI-security stack fragments. Each tool has its own billing, its own dashboard, its own integration surface. The operational cost of maintaining the stack rivals the technical cost of the attacks it defends against.
Symptom-treatment vs root-cause fix
Example: An application vulnerable to prompt injection typically has a design-level bug: user input is spliced into the system prompt without clear trust-boundary markers, or an agent has tool-scope access beyond what its authorization model justifies. Lakera Guard intercepts attempted exploits but does not prompt the developer to fix the underlying design. The bug remains; every future deploy carries the same architectural risk.
Impact: Code time defense compounds (every bug fixed is fixed forever); runtime defense does not (every new attack variant needs to be caught by the classifier). The architecture makes runtime-first strategies fragile over time as attack sophistication rises.
Why Securie instead
Fix the bug, not just the symptom
Securie closes the tool-scope-abuse + input-trust gaps at code-review time. Lakera filters the symptoms.
Broader AI-security surface
Prompt injection + tool scope + RAG + AIBOM + vector DB + agent behavior — Securie covers the full stack; Lakera covers runtime.
Code-time defense compounds
Every bug Securie fixes is fixed forever. Runtime guards need to catch every new variant.
Feature matrix — Lakera Guard vs Securie
| Area | Lakera Guard | Securie |
|---|---|---|
| Defense timing | Runtime (request-intercept) | Code-review time (pre-deploy) |
| Bug fix model | Symptom intercept; underlying code unchanged | Root-cause fix via merge-ready patch |
| Detection ceiling | Classifier-bound; 85-95% in-distribution, lower on novel attacks | Architectural — detects the bug, not just specific payloads |
| AIBOM / EU AI Act docs | Not covered | Auto-generated per build; auditor-consumable |
| Prompt injection detection | Runtime classifier | Code-time specialist modeling trust boundaries |
| Tool-scope abuse | Indirect (filters outputs) | Dedicated specialist modeling agent tool boundaries |
| RAG corpus poisoning | Not covered | Dedicated specialist scanning retrieval corpora |
| Vector DB security | Not covered | Coverage for Pinecone, Qdrant, Chroma access patterns |
| Pricing model | Subscription + per-API-call | Subscription; no per-call metering |
| Scope | LLM guardrails narrow | Full AI-feature security + application-layer security |
| Deployment modes | SaaS intercept; self-hosted agent roadmap | SaaS + Customer-VPC + air-gapped |
The deeper tradeoff
The honest picture is that code-time and runtime defenses both have a role, and the right question is sequencing. Code-time defense is where the cost curve is most favorable: fixing a prompt-injection design bug during PR review is near-free, and the fix applies to every request for the rest of the application's lifetime. Runtime defense is where the cost curve inverts: every request costs, attack variants compound, and the classifier must keep pace with adversarial research to maintain detection quality.
For teams building new AI-native applications, Securie's recommendation is to start with code-time defense — the architectural bugs detected by a specialist fleet during code review are the highest-leverage fixes, and many applications that were runtime-filter-reliant discover after a Securie pass that their trust-boundary design is actually sound and the runtime filter is defending against risk that does not structurally exist.
For teams running AI features at massive scale (LLM inference volumes in the millions of requests per day, user-generated input reaching a powerful model on every request), runtime defense is an appropriate defense-in-depth layer on top of clean architecture. Lakera Guard is a reasonable choice for that role — their classifier research and their enterprise support are solid. The mistake is treating the runtime filter as a substitute for code-time fixes; it is defense-in-depth, not primary defense.
For teams between those two extremes — most teams — the pragmatic stack is Securie at code-time, possibly supplemented by a lightweight runtime filter at the inference boundary if cost permits. The Securie specialists will remove the entire class of exploits that make runtime filtering necessary; the remaining risk (novel attacks the architectural analysis did not anticipate) is what runtime catches. That layered stack is materially cheaper over time than runtime-filter-first.
Pricing
Lakera Guard: $99/mo + per-API-call. Securie: $0 during early access.
Migration path
- If shipping AI features: run Securie pre-deploy AND Lakera at runtime — defense in depth
- If budget-constrained: Securie-first, since it eliminates whole classes of bugs
Extended migration playbook
Step 1: Audit your LLM trust boundaries at code level
What: Run Securie's prompt-injection specialist on every LLM call site in your application. For each site, the specialist reports: where is user input spliced into the system prompt? Is there a clear delimiter? Is the system prompt protected from user-controlled strings? Are tool calls constrained to the agent's authorized scope?
Why: Many runtime-filter users discover at audit time that their trust boundaries are already sound — the filter was defending against a risk class that does not architecturally exist in their app. Removing the unnecessary filter recovers the metered cost and simplifies the request path.
Gotchas: Trust boundaries look clean on simple calls and leak on complex agent loops. Audit every call site, not just the obvious ones. Tool-scope leaks in particular tend to be subtle — an agent with filesystem access it does not need, but never actually uses in happy-path testing.
Step 2: Fix the architectural bugs Securie surfaces
What: Merge the auto-fix PRs Securie opens for each finding. Typical fixes: add explicit delimiter markers around user input, move system-prompt content to a separate role, tighten tool-scope to the minimal set of actions, add an input-schema validation at the tool-call boundary.
Why: Each fix removes a class of exploit permanently. After the sweep, the remaining attack surface for runtime filtering is narrower — often narrow enough that a cheaper runtime option is sufficient.
Gotchas: Some fixes require architectural changes (moving user input out of the system prompt changes the model's behaviour). Validate with your product team that the fixed behaviour still satisfies the user-facing feature.
Step 3: Decide on residual runtime defense
What: After code-time fixes, assess the remaining risk. For low-volume or internal LLM features, the residual risk may be acceptable without runtime filtering. For high-volume customer-facing LLM features, add a runtime filter — Lakera is one reasonable choice; lighter-weight alternatives (self-hosted classifier, Llama Guard) are also viable.
Why: The runtime filter should defend against the residual risk after architectural fixes, not against every possible attack. Right-sizing the filter to the residual risk profile keeps cost proportional to risk.
Gotchas: Do not remove runtime filtering mid-incident. If you are currently running Lakera and experiencing active attacks, keep it on during the code-time fix window and reassess after the architectural changes have bedded in.
Step 4: Review the stack quarterly
What: Each quarter, check: did the runtime filter catch anything the code-time specialists missed? If yes, that is a gap Securie's specialists should extend to; file it with us. If no, consider whether the runtime filter is still earning its cost.
Why: The defense stack should evolve with the threat model. A runtime filter that never catches anything is expensive defense-in-depth; a runtime filter that catches novel attacks is justifying its cost.
Gotchas: Runtime-filter vendors publish 'blocked attempts' dashboards. Not all blocked attempts are real attacks — many are false positives on legitimate user input. Review the specific attacks, not the count.
Pick Securie if…
You want to prevent LLM security bugs at code review time.
Stay with Lakera Guard if…
You run LLM features at massive scale and need real-time runtime filtering that can't wait for code review.
Common questions during evaluation
Does Securie replace Lakera Guard?
Not directly — they operate at different layers. Securie replaces the need for Lakera in cases where the architectural bugs that require runtime filtering are fixed at code-review time. For applications where residual runtime risk remains (high-volume customer-facing LLM features), Lakera is a reasonable defense-in-depth layer on top of Securie-fixed code.
What about Llama Guard 4 as a cheaper runtime option?
Meta's Llama Guard 4 is a strong open-weight alternative to commercial runtime filters. For teams with GPU capacity (H100 or better), self-hosting Llama Guard 4 avoids per-call metering while providing comparable detection quality for in-distribution attacks. Securie uses Llama Guard 4 internally for its offensive-swarm output filtering per CLAUDE.md principle #9.
How does Securie detect prompt injection without runtime?
Securie's prompt-injection specialist reads the LLM call site at code-review time and models the trust boundary. It flags: user input spliced into system prompts without delimiters, untrusted document content loaded into context without quarantine, tool calls with scope beyond the agent's authorization. These are architectural bugs; fixing them removes the class of exploit at the source.
Does Securie handle agent tool-scope abuse?
Yes. The tool-scope-abuse specialist models agent frameworks (langchain-style tool registries, OpenAI Assistants, Anthropic tool calling) and flags tools whose permissions exceed the agent's documented purpose. Typical finding: an agent described as 'customer support' with filesystem-write tool access, where the tool is never required for customer support but could be exploited via prompt injection.
Can I use both Securie and Lakera?
Yes, and it is a reasonable layered defense for high-risk applications. Securie fixes the architectural bugs at code review; Lakera catches novel runtime attacks the architecture did not anticipate. The Lakera bill should drop after Securie bedds in — fewer attacks reach the filter because fewer architectural vulnerabilities exist.
What about EU AI Act conformity? Lakera markets on this.
EU AI Act conformity requires Article 11 technical documentation — model card, risk management, human oversight, post-market monitoring — not just runtime filtering. Securie generates Article 11 documentation per build (AIBOM, model card, risk-management summary) and publishes transparency reports. Lakera's contribution to EU AI Act compliance is the runtime-monitoring component (part of post-market monitoring); the documentation layer requires separate tooling.
Verdict
Runtime LLM filtering is a legitimate defense-in-depth layer, and Lakera Guard is a reasonable choice when you need that layer. The mistake is treating the filter as primary defense — a classifier sitting in front of a poorly-architected LLM application is intercepting a small fraction of attackers while the underlying design flaw remains.
For teams building new AI-native applications, the higher-leverage path is code-time architectural review via Securie's specialist fleet. Fix the trust boundaries, constrain the tool scopes, quarantine the RAG corpus — the resulting application has a materially smaller attack surface and the runtime filter (if still needed) becomes defense-in-depth against novel attacks rather than primary defense against architectural bugs.
For high-volume production AI features, the pragmatic stack is Securie at code time plus a lightweight runtime filter at the inference boundary. Lakera Guard is a reasonable runtime component; Llama Guard 4 self-hosted is a cheaper alternative with comparable in-distribution detection. The right sequencing is fix-then-filter, not filter-while-ignoring-the-bug.