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
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
- 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.
Source citations
Add this badge to your README
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[](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.
| Field | AI security platform for detecting prompt injection, unsafe content, data leakage, and LLM application abuse. Open dossier | Open-source prompt testing and red-teaming framework for LLM outputs, regressions, evaluations, and security checks. Open dossier | 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 | Open-source LLMOps platform for prompt management, prompt versioning, evaluation, and observability across LLM applications. Open dossier |
|---|---|---|---|---|
| Trust | ||||
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety · Privacy ✓ | Safety · Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Brand | — | |||
| 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 |
|
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| Install | — | — | | — |
| Config | — | — | — | — |
| Citations | ||||
| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
Related resources
Promptfoo
Open-source prompt testing and red-teaming framework.
Related guides
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Auditing MCP Client Configuration Before Team Rollout
Audit MCP client configuration before sharing it with a team.
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