toolsSource-backedReview first Safety · Privacy ✓

Lakera Guard

AI security platform for detecting prompt injection, unsafe content, data leakage, and LLM application abuse.

by Lakera·added 2026-04-27·

CLI

HarnessCLI

Command center

Source

Review first

Review safety and privacy notes before installing or copying commands.

Privacy notes

Install & copy

## How Lakera Guard compares

Lakera Guard is a runtime guardrail; related LLM-security tools in this directory work at different stages:

| Tool | Stage | Open source | Notable for |
| --- | --- | --- | --- |
| **Lakera Guard** | Runtime input/output filtering | No | Real-time prompt-injection and content detection via API |
| **NeMo Guardrails** | Runtime programmable rails | Yes | Define allowed flows/topics in a rails config |
| **Garak** | Pre-deployment scanning | Yes | Probe models for vulnerabilities before shipping |

Use Lakera Guard for managed runtime protection, NeMo Guardrails for self-hosted programmable rails, or Garak to scan for weaknesses before deployment.

## Editorial notes

Lakera Guard is relevant for teams that need runtime protection and policy checks around LLM application inputs and outputs.

## Disclosure

Editorial listing. No paid placement or affiliate link is used.

Trust & readiness

TrustReview first
Sourcesource-backed
Safety notesMissing
ReviewedYes

Community context

Related entries(4)
Related guides(3)
Community signals

Compare

Integrations & API

Contribute

Suggest a metadata change Claim this listing

Documentation Source repository Browse directory

Review first — review before installing

Open the source and read safety notes before installing.

Citation facts

Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.

Canonical URL: https://heyclau.de/entry/tools/lakera-guard
Source URLs: https://docs.lakera.ai, https://github.com/JSONbored/awesome-claude/blob/main/content/tools/lakera-guard.mdx, https://www.lakera.ai/ai-agent-security
Brand: Lakera Guard
Brand domain: lakera.ai
Brand asset source: brandfetch
Privacy notes: Lakera Guard inspects prompts and model outputs (sent to its API or self-hosted deployment) to detect injection, unsafe content, and data leakage; review what application traffic is sent for scanning and its data handling before routing production traffic.
Author: Lakera
Claim status: unclaimed
Last verified: 2026-04-27

Decision playbook

Review trust signals before you adopt

Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.

Required checks are still incomplete. Finish source and safety verification before adopting this resource.

Compare context

Selected

Current score

Baseline

—

Delta

No baseline selected

No major trust-signal divergence detected in the current selection.

Source and provenance checks

Complete

Confirm ownership and provenance before trusting install instructions.

Source link availableRequired
Open the canonical repository and verify ownership.
Done
Source provenance statusRequired
Marked as source-backed.
Done
Metadata reviewed
Registry metadata indicates a reviewed listing.
Done

Safety and privacy checks

Required checks missing

Validate risk disclosures before installation or API wiring.

Safety notes presentRequired
No safety notes listed.
Pending
Privacy notes presentRequired
Review data handling notes before connecting accounts or secrets.
Done
Trust level risk gateRequired
Trust level does not block evaluation.
Done

Package and install checks

Needs review

Check package metadata and artifact integrity signals.

Install payload available
Install or copy payload is available for review.
Done
Package verification flag
No package verification flag provided.
Pending
Checksum metadata
No checksum provided for downloaded artifact.
Pending

Compare-driven decision checks

Needs review

Use compare context to validate trade-offs before adoption.

Compare tray has multiple entries
Add at least one more entry to compare trust differences.
Pending
Baseline comparison available
No baseline peer selected yet.
Pending
Diverging trust signals identified
No major trust-signal divergence found.
Pending

Setup at a glance

Copy & paste

Copy-ready — paste the snippet to get started.

Install command

Not provided

Config snippet

Not provided

Copy snippet

Provided

Prerequisites

None

Platforms

1 listed

Install type

Copy & paste

Adoption plan

Balanced adoption plan

Current risk score 30/100. Use staged verification before broader rollout.

Risk 30

Adoption blockers

Safety notes are missing.

Pre-adoption checks

Validate source and review signals before any execution.

Confirm source provenanceRequired
Source URL/provenance metadata is present.
Done
Confirm metadata review state
Listing has review metadata.
Done
Verify install payload
Install/config payload exists and can be inspected.
Done

Security checks

Confirm safety, privacy, and package integrity signals.

Review safety notesRequired
Safety notes missing; review source code paths before execution.
Pending
Review privacy notesRequired
Privacy notes are present.
Done
Verify package integrity metadata
No package verification/checksum metadata.
Pending

Rollout

Adopt in controlled steps based on the selected plan.

Run in isolated sandbox firstRequired
Use a constrained sandbox and observe behavior across multiple tasks.
Pending
Roll out graduallyRequired
Roll out to a small cohort before wider usage.
Pending
Set monitoring and fallback
Define rollback path and monitor errors after adoption.
Pending

Evidence readiness

Evidence readiness matrix · balanced

Missing required evidence: Safety notes. Risk score 31.

Risk 31

Source provenance

Present

Source repository/provenance is listed.

Required in this preset

Metadata review

Present

Review metadata is present.

Required in this preset

Safety notes

Missing

Safety notes are missing.

Required in this preset

Privacy notes

Present

Privacy notes are present.

Optional in this preset

Package integrity

Missing

Package integrity metadata is missing.

Optional in this preset

Install payload

Present

Install payload is available.

Required in this preset

Required gaps: Safety notes

Decision timeline

Decision timeline · balanced

Blocking gaps: Review safety notes. Risk 28.

Risk 28

triage

Confirm source provenanceRequired

Source/provenance metadata is available.

Done

triage

Check metadata review statusRequired

Review metadata is available.

Done

verify

Review safety notesRequired

Safety notes are missing.

Pending

verify

Review privacy notes

Privacy notes are available.

Done

verify

Validate package integrity metadata

Package integrity metadata is missing.

Pending

rollout

Verify install payload and commandsRequired

Install payload is available.

Done

Blockers: Review safety notes

Safety & privacy surface

1 privacy note across 1 risk area. Review closely: third-party handling.

1 area

PrivacyThird-party handlingLakera Guard inspects prompts and model outputs (sent to its API or self-hosted deployment) to detect injection, unsafe content, and data leakage; review what application traffic is sent for scanning and its data handling before routing production traffic.

Disclosure: editorial

Privacy notes

Lakera Guard inspects prompts and model outputs (sent to its API or self-hosted deployment) to detect injection, unsafe content, and data leakage; review what application traffic is sent for scanning and its data handling before routing production traffic.

Schema details

Install type: copy
Troubleshooting: No

Skill and platform metadata

Retrieval sources

https://docs.lakera.aihttps://github.com/NVIDIA/NeMo-Guardrailshttps://docs.garak.ai/

Tool listing metadata

Website: https://www.lakera.ai/ai-agent-security
Pricing: paid
Disclosure: editorial
Application category: SecurityApplication
Operating system: Web

Full copyable content

## How Lakera Guard compares

Lakera Guard is a runtime guardrail; related LLM-security tools in this directory work at different stages:

| Tool | Stage | Open source | Notable for |
| --- | --- | --- | --- |
| **Lakera Guard** | Runtime input/output filtering | No | Real-time prompt-injection and content detection via API |
| **NeMo Guardrails** | Runtime programmable rails | Yes | Define allowed flows/topics in a rails config |
| **Garak** | Pre-deployment scanning | Yes | Probe models for vulnerabilities before shipping |

Use Lakera Guard for managed runtime protection, NeMo Guardrails for self-hosted programmable rails, or Garak to scan for weaknesses before deployment.

## Editorial notes

Lakera Guard is relevant for teams that need runtime protection and policy checks around LLM application inputs and outputs.

## Disclosure

Editorial listing. No paid placement or affiliate link is used.

About this resource

How Lakera Guard compares

Lakera Guard is a runtime guardrail; related LLM-security tools in this directory work at different stages:

Tool	Stage	Open source	Notable for
Lakera Guard	Runtime input/output filtering	No	Real-time prompt-injection and content detection via API
NeMo Guardrails	Runtime programmable rails	Yes	Define allowed flows/topics in a rails config
Garak	Pre-deployment scanning	Yes	Probe models for vulnerabilities before shipping

Use Lakera Guard for managed runtime protection, NeMo Guardrails for self-hosted programmable rails, or Garak to scan for weaknesses before deployment.

Editorial notes

Lakera Guard is relevant for teams that need runtime protection and policy checks around LLM application inputs and outputs.

Disclosure

Editorial listing. No paid placement or affiliate link is used.

#security #guardrails

Source citations

Source methodology →

Add this badge to your README

Show that Lakera Guard is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.

[![Listed on HeyClaude](https://heyclau.de/badge/tools/lakera-guard.svg)](https://heyclau.de/entry/tools/lakera-guard)

How it compares

Lakera Guard side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

1 trust signal differ across this comparison (Submitter).

Next steps differ across entries — use the actions in the table below to copy install commands and source links per resource.

Field	Lakera Guard AI security platform for detecting prompt injection, unsafe content, data leakage, and LLM application abuse. Open dossier	Promptfoo Open-source prompt testing and red-teaming framework for LLM outputs, regressions, evaluations, and security checks. Open dossier	Snyk Agent Scan Security scanner from Snyk for discovering local AI agent components, including MCP servers and Agent Skills, and checking them for prompt injection, tool poisoning, tool shadowing, toxic flows, malware payloads, credential handling, and hardcoded secrets. Open dossier	Agenta Open-source LLMOps platform for prompt management, prompt versioning, evaluation, and observability across LLM applications. Open dossier
Next stepsDiffers	Open dossier API JSON Open LLM Open source Newsletter Claim listing	Open dossier API JSON Open LLM Open source Newsletter Claim listing	Open dossier API JSON Open LLM Open source Newsletter Claim listing	Open dossier API JSON Open LLM Open source Newsletter Claim listing
Trust
Review status	ReviewedMaintainer reviewed	ReviewedMaintainer reviewed	ReviewedMaintainer reviewed	ReviewedMaintainer reviewed
Package trust	Package not verified	Package not verified	Package not verified	Package not verified
Source provenance	Source-backed	Source-backed	Source-backed	Source-backed
SubmitterDiffers	—	—	—	oktofeesh1
Install risk	Review first	Review first	Review first	Review first
Notes	Safety · Privacy ✓	Safety · Privacy ✓	Safety ✓ Privacy ✓	Safety ✓ Privacy ✓
Brand	Lakera Guard	Promptfoo	—	Agenta
Category	tools	tools	tools	tools
Source	source-backed	source-backed	source-backed	source-backed
Author	Lakera	Promptfoo	Snyk	Agenta
Added	2026-04-27	2026-04-27	2026-06-18	2026-06-03
Platforms	CLI	CLI	CLI	CLI
Source repo	—	—	—	—
Safety notes	— missing	— missing	✓Scanning MCP configuration files can execute the commands declared for stdio MCP servers so Agent Scan can retrieve tool descriptions. Interactive scans ask for consent before starting each stdio MCP server; review the command and arguments before approving. Use `--dangerously-run-mcp-servers` only in trusted environments after verifying every MCP server command. CLI output is marked experimental upstream, so avoid building brittle production parsers around issue codes or output fields without Snyk guidance. Sandbox untrusted third-party MCP configs, skills, plugins, or agent workspaces before scanning.	✓Agenta can manage and deploy prompt or configuration changes, so production updates should go through review and rollback controls. Webhooks and GitHub automations tied to prompt or deployment changes should be scoped to trusted repositories and guarded workflows. Evaluation and online monitoring results should support, not replace, domain review for high-risk application behavior.
Privacy notes	✓Lakera Guard inspects prompts and model outputs (sent to its API or self-hosted deployment) to detect injection, unsafe content, and data leakage; review what application traffic is sent for scanning and its data handling before routing production traffic.	✓Promptfoo sends your prompts and test inputs to the model providers you configure to run evals and red-team probes; review which providers are used and keep secrets out of test cases.	✓Agent Scan can discover local agent configuration paths, MCP server names, commands, arguments, tool names, descriptions, prompts, resources, skill text, and local component inventories. The README states skills, agent applications, tool names, and descriptions are shared with Snyk for validation. Control-server bootstrap can send redacted CLI args, OS and Python details, hostname, username, shell, locale, timezone, working directory, home directory, executable path, and readable home-directory metadata when enabled. Review Snyk Agent Scan terms, enterprise deployment guidance, and organization policy before scanning customer data, proprietary skills, or sensitive agent configurations.	✓Prompt records, variants, test sets, traces, model inputs and outputs, feedback, annotations, and evaluation results may be stored in Agenta. Hosted Agenta use sends that data to Agenta Cloud; self-hosted deployments still require retention, access-control, and backup policies. Review Agenta's sensitive-data redaction and retention guidance before sending production, customer, or regulated data.
Prerequisites	— none listed	— none listed	Python 3.10 or newer, through uv/uvx or another supported Python package execution path. A Snyk account and `SNYK_TOKEN` environment variable for scan verification. Permission to inspect the local user's AI agent configuration files, MCP server configs, and Agent Skill directories. A sandbox, VM, container, or disposable environment when scanning untrusted MCP configs.	LLM application, prompt workflow, or agent workflow whose prompts and configurations need shared management. Access to Agenta Cloud or a reviewed self-hosted Agenta deployment. Provider credentials and a release policy for test sets, traces, prompt versions, and production deployment approvals.
Install	—	—	`uvx snyk-agent-scan@latest`	—
Config	—	—	—	—
Citations	Source repositorygithub.com 2026-07-13T15:19:32-07:00 Documentationdocs.lakera.ai Websitelakera.ai Source methodology →	Source repositorygithub.com 2026-07-13T15:19:32-07:00 Documentationpromptfoo.dev Source methodology →	Source repositorygithub.com 2026-07-13T15:19:32-07:00 Documentationgithub.com Source methodology →	Source repositorygithub.com 2026-07-13T15:19:32-07:00 Documentationagenta.ai Submitted by oktofeesh12026-06-03 Source methodology →
Claim	Unclaimed	Unclaimed	Unclaimed	Unclaimed