Guozhen AIGlobal AI field notes and model intelligence
Back to AI decision guides

AI security

LLM security tools comparison: Lakera Guard vs Promptfoo vs NeMo Guardrails vs Garak

Compare LLM security tools for prompt injection, jailbreaks, data leakage, insecure tool use, guardrails, red teaming, and vulnerability scanning: Lakera Guard, Promptfoo, NVIDIA NeMo Guardrails, and Garak.

Updated 2026-06-1110 min readAdvanced
Read LLM red teaming guide Read LLM guardrails guide

Best for

  • Security teams reviewing LLM apps, agents, RAG systems, and chatbots
  • Developers adding prompt-injection tests and guardrails to CI
  • Enterprise teams evaluating AI security vendors
  • Product teams handling customer data, tools, or regulated workflows

Not for

  • Assuming a single guardrail tool makes an LLM app safe
  • Skipping app-level authorization and backend validation
  • Testing only the base model while ignoring tools, retrieval, and user permissions

Comparison

Choose by workflow, not brand

OptionBest forStrengthsTradeoffsUse when
Lakera GuardManaged runtime protection and threat detection for GenAI applications and agentsFocused on real-time visibility, control, threat detection, and enterprise AI security.SaaS fit, latency, cost, and data handling need procurement review.You want a managed protection layer in front of production LLM traffic.
PromptfooAutomated LLM red teaming, evals, and CI/CD testingDeveloper workflow for red-team tests against apps, agents, prompts, and workflows.Requires teams to write, maintain, and act on test cases.Security testing should run before deployment and during regression checks.
NVIDIA NeMo GuardrailsProgrammable input, output, and dialog rails in LLM applicationsOpen-source Python package for adding configurable guardrails to conversational systems.Requires engineering integration and policy design.You need app-level guardrail logic you can inspect and customize.
GarakOpen-source vulnerability scanning and red-team assessmentProbes LLM systems for hallucination, data leakage, prompt injection, toxicity, jailbreaks, and other failures.Scanner results still need triage, reproduction, and mitigation work.You want a security scanner style workflow for LLM weaknesses.

Protect the whole system

LLM security is not only model safety. The system includes prompts, retrieval, tools, user permissions, logs, secrets, output handling, and human review.

  • Test indirect prompt injection through documents and webpages.
  • Validate tool arguments and permissions outside the model.
  • Log security-relevant traces without exposing sensitive data broadly.

Combine prevention and testing

Runtime filters catch known patterns. Red-team tests discover workflow-specific failures. Guardrails encode policy. Vulnerability scanners broaden coverage.

  • Run red-team tests before every major prompt, model, or tool change.
  • Use runtime controls for high-risk production traffic.
  • Review failed tests with product, engineering, and security together.

Tie findings to fixes

A security report that does not change prompts, permissions, routing, guardrails, or UI warnings is theater. The useful output is a prioritized remediation backlog.

  • Classify findings by exploitability and business impact.
  • Add regression tests for fixed vulnerabilities.
  • Escalate issues involving PII, credentials, payments, or account actions.

Decision Rules

A practical checklist

01

Use Lakera Guard for managed runtime protection and threat detection.

02

Use Promptfoo for automated red-team and eval tests in development workflows.

03

Use NeMo Guardrails for programmable application guardrails.

04

Use Garak for open-source LLM vulnerability scanning and broad probing.

Related Guides

Continue the decision path

Read LLM red teaming guide

Design adversarial testing before launch.

Open

Read LLM guardrails guide

Add policy controls around model behavior and tool use.

Open

LLM red teaming guide

Design security tests for LLM apps and agents.

Open

LLM guardrails guide

Add policy controls to inputs, outputs, and tool use.

Open

AI agent evaluation guide

Evaluate agent traces, tools, routes, and safety behavior.

Open

Chinese Archive

Aligned deeper reading

AI agent archive

Chinese agent implementation and safety notes.

Open

AI product archive

Chinese AI product security and rollout materials.

Open

Topic Hubs

Explore the wider search cluster

Topic hub

RAG and models

Plan RAG systems, local LLM deployment, model APIs, cloud AI platforms, vector databases, evaluation, observability, rate limits, and cost optimization.

Open

Topic hub

Security and governance

Compare AI security controls, governance frameworks, compliance automation, data privacy, vendor questionnaires, red teaming, SIEM, SOAR, XDR, CNAPP, DSPM, DLP, PAM, GRC, and risk tooling.

Open

Industry Pages

See this guide in a buyer workflow

Industry page

Cybersecurity AI

Compare AI cybersecurity tools for SOC analysts, SIEM, SOAR, XDR, CNAPP, exposure management, DSPM, DLP, PAM, email security, AI GRC, vendor risk, red teaming, and compliance automation.

Open

Industry page

IT operations AI

Compare AI tools for ITSM, AIOps, SaaS management, LLM observability, gateways, rate limits, fallback routing, enterprise search, knowledge management, and IT governance.

Open

FAQ

Common questions

What are LLM security tools?

LLM security tools help test, monitor, or control risks such as prompt injection, jailbreaks, data leakage, insecure tool use, toxic output, and policy violations.

Do guardrails stop prompt injection?

Guardrails can reduce risk, but they do not remove the need for permissions, backend validation, retrieval hygiene, red-team tests, and monitoring.

What is the difference between red teaming and guardrails?

Red teaming finds failures through adversarial tests. Guardrails enforce controls at runtime or in the application. Production systems usually need both.

Source Links

Primary references used for this guide

Reference

Lakera Guard docs

Lakera documentation for Guard runtime controls and AI threat detection.

Open

Reference

Promptfoo LLM red teaming

Promptfoo documentation for LLM red teaming and security testing.

Open

Reference

NVIDIA NeMo Guardrails docs

NVIDIA documentation for NeMo Guardrails.

Open

Reference

Garak LLM vulnerability scanner

Garak site for the open-source LLM vulnerability scanner.

Open

Reference

Garak GitHub

NVIDIA Garak repository and vulnerability scanner description.

Open

Build your own evaluation note

The strongest decision is always local to your workflow. Save the vendor links, define a representative task, record the exact prompt or command, and compare the final evidence instead of the marketing claim.

Return to the AI learning map