There is no single best AI security tool, because AI systems need protection at several layers: testing before release, guardrails at runtime, scanning of model files, and an inventory of what you run. Pick one tool per layer that fits your stack and license needs, then add governance on top.
Safeguard publishes this guide and sells a competing product, so we describe other tools only from their own documentation.
Key takeaways
- Group tools by layer first. Comparing a red teaming scanner with a guardrail library is comparing different jobs.
- Several widely used options are open source, so you can try them before you buy anything.
- Check maintenance status before adopting a library. One well-known guardrail toolkit is now archived.
- Tools find problems. Policy, ownership and review still decide what happens next.
- Start from a risk list such as the OWASP Top 10 for LLM Applications, then map each risk to a tool.
What should an AI security tool actually cover?
Start with the OWASP Top 10 for LLM Applications 2025, which names ten risks: prompt injection, sensitive information disclosure, supply chain, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation and unbounded consumption.
No one product covers all ten well. A practical stack has four parts:
- Test the application with adversarial inputs before release.
- Guard inputs, outputs and tool calls at runtime.
- Scan model files and dependencies before you load them.
- Inventory models, datasets and agents so you know what is deployed.
What are the main AI security tools by layer?
The table lists tools by layer. Every cell comes from each project's own page, fetched as of 19 October 2026. A blank in "Limits" means the page did not state one, not that none exists.
| Tool | Layer | License | What its page says it does | Limits stated on the page |
|---|---|---|---|---|
| garak | Test | Apache-2.0 | LLM vulnerability scanner that probes for hallucination, data leakage, prompt injection, toxicity and jailbreaks | Runs probes you select; results show failure rates per probe |
| PyRIT | Test | MIT | Open source framework to help find risks in generative AI systems | Repo page lists no feature detail; see the project site |
| Promptfoo | Test | Open source (license not stated on the page we read) | Red teaming through adversarial testers called plugins; can run in CI/CD | Page gave no config detail |
| DeepTeam | Test | Apache 2.0 | Simulates attacks such as jailbreaking and prompt injection against agents, RAG pipelines and chatbots; 50+ vulnerabilities | Quickstart expects an OpenAI key or a DeepEval-supported model |
| NeMo Guardrails | Guard | Apache 2.0 | Programmable input, dialog, retrieval, execution and output rails for LLM systems | Aimed at conversational systems; uses the Colang language |
| LLM Guard | Guard | MIT | Input and output scanners for prompt injection, secrets, PII anonymization, toxicity and more | Archived and no longer maintained, per its page |
| Purple Llama | Guard and eval | Mixed, by component | Llama Guard moderation models, Prompt Guard, CyberSec Eval benchmarks | Licenses differ per component, so read each one |
| ModelScan | Scan | Apache 2.0 | Scans model files for unsafe code without loading them; supports H5, Pickle and SavedModel | Covers the formats on its page only |
Which tool fits which situation?
Each tool fits a different starting point:
- You ship an LLM feature next month: run garak or Promptfoo against a staging endpoint, and wire a small suite into CI.
- You build agents or RAG: DeepTeam lists agentic and RAG targets, and NeMo Guardrails has retrieval and execution rails.
- You download open models: scan the files with ModelScan before loading, because pickle formats can run code on load.
- You need content moderation: Llama Guard in Purple Llama classifies input and output, under its own license terms.
For deeper reading on one layer, see our guide to comparing LLM red teaming tools.
How do you choose? A checklist
Apply this list to any candidate:
- Which OWASP LLM risks does it test or block, and which does it not?
- Is the project maintained? Check the repo for an archive notice and recent commits.
- Can it run in your environment, or does it need to send prompts to a third party?
- Which license applies, including model licenses for any bundled models?
- Does it output machine-readable results you can gate a build on?
- Who triages the findings, and how fast?
What about governance and inventory?
Tools do not replace a framework. NIST released the AI Risk Management Framework on 26 January 2023 as voluntary guidance, and published a Generative AI Profile (NIST-AI-600-1) on 26 July 2024. NIST states that the AI RMF 1.0 is being revised as of its current page.
For inventory, CycloneDX describes an ML-BOM that covers models, datasets and their dependencies. See AI bill of materials explained.
Try it yourself
Safeguard has a free dependency scan at gold.safeguard.sh/scan. It runs in your browser, needs no login, keeps your file on your machine and accepts up to 100 dependencies. Use it on the requirements.txt of an AI project to see which Python packages carry known CVEs, then walk through the interactive model risk demo.
Click through it at your own pace, or open it full screen.
Frequently asked questions
What is the best AI security tool for a startup?
Start with free, open source options: garak or Promptfoo for testing, and NeMo Guardrails for runtime rules. Add model file scanning if you download third party models.
Are open source AI security tools good enough?
They cover testing and basic guardrails well, but you carry the integration and upkeep. Check each repository for maintenance status, since LLM Guard is archived.
Do I need a guardrail if I already red team?
Yes, they do different jobs. Red teaming finds weaknesses before release, while guardrails reduce harm from inputs you did not predict.
What is the difference between AI security and AI governance?
Security tools test and protect systems. Governance sets who may build and use AI, which risks are accepted and who is accountable.
Which standard should I map tools to?
The OWASP Top 10 for LLM Applications 2025 is a practical risk list, and the NIST AI RMF is a voluntary management framework.
Sources
- garak on GitHub
- PyRIT on GitHub
- Promptfoo red teaming docs
- DeepTeam on GitHub
- NeMo Guardrails on GitHub
- LLM Guard on GitHub
- Purple Llama on GitHub
- ModelScan on GitHub
- OWASP Top 10 for LLM Applications
- NIST AI Risk Management Framework
- CycloneDX ML-BOM
Last reviewed: 19 October 2026.
