10 Best AI Security Tools (2026): LLM & GenAI Protection

What is AI Security?

As we integrate LLMs into our applications, traditional scanners are not enough.

We need specialized tools to test for hallucinations, prompt injection, jailbreaks, and data leakage.

The OWASP Top 10 for LLM Applications provides a framework for understanding these risks.

According to OWASP’s 2025 Top 10 for LLM Applications, prompt injection is the #1 critical vulnerability, appearing in over 73% of production AI deployments assessed during security audits. Microsoft’s security research confirms that indirect prompt injection is one of the most widely-used attack techniques against AI systems. Research demonstrates that just five carefully crafted documents can manipulate AI responses 90% of the time through Retrieval-Augmented Generation (RAG) poisoning. Proactive security measures reduce incident response costs by 60-70% compared to reactive approaches, according to 2025 industry benchmarks.

“Prompt injection is the SQL injection of the AI era — we ignored SQL injection for years, and we paid the price,” cautions Simon Willison, independent AI researcher and creator of Datasette. “The difference is that LLMs are being deployed faster than any technology in history, with far less security scrutiny.”

The tools in this category help you proactively identify and mitigate AI-specific vulnerabilities before they reach production.

Advantages

• Tests for novel AI-specific risks
• Catches prompt injection and jailbreaks
• Essential for GenAI applications
• Most tools are free and open-source

Limitations

• Rapidly evolving field
• No established standards yet
• Limited coverage of all AI risk types
• Requires AI/ML expertise to interpret results

OWASP Top 10 for LLM Applications

These are the top risks you should test for when deploying LLM-based applications:

Prompt Injection

Malicious input that hijacks the model to perform unintended actions or reveal system prompts. The most critical and common LLM vulnerability.

Insecure Output Handling

LLM output used directly without validation, leading to XSS, SSRF, or code execution. Always sanitize LLM responses before rendering or executing them.

Training Data Poisoning

Malicious data introduced during training that causes the model to behave incorrectly. Relevant if you fine-tune models on external data.

Model Denial of Service

Attacks that consume excessive resources or cause the model to hang on crafted inputs. Rate limiting and input validation help mitigate this.

Supply Chain Vulnerabilities

Compromised models, datasets, or plugins from third-party sources. HiddenLayer and Protect AI Guardian scan for malicious models.

Sensitive Information Disclosure

Model leaking PII, credentials, or proprietary data from training or context. LLM Guard can anonymize PII in prompts and responses.

Quick Comparison of AI Security Tools

Tool	USP	Type	License
Testing / Red Teaming (Open Source)
Garak	NVIDIA's "Nmap for LLMs"	Testing	Open Source
PyRIT	Microsoft's AI red team framework	Testing	Open Source
DeepTeam	40+ attack simulations, OWASP coverage	Testing	Open Source
Promptfoo	Developer CLI, CI/CD integration	Testing	Open Source
Runtime Protection (Open Source)
LLM Guard	PII anonymization, content moderation	Runtime	Open Source
NeMo Guardrails	NVIDIA's programmable guardrails	Runtime	Open Source
Rebuff	Prompt injection detection SDK	Runtime	Open Source
Commercial
Lakera Guard	Gandalf game creator, enterprise API	Runtime	Freemium
HiddenLayer AISec	ML model security platform	Both	Commercial
Protect AI Guardian	ML model scanning, 35+ formats	Testing	Commercial

Testing Tools vs Runtime Protection

AI security tools fall into two categories: those that test your LLM before deployment, and those that protect it at runtime.

Aspect	Testing Tools	Runtime Protection
When it runs	Before deployment, in CI/CD	At runtime, on every request
Purpose	Find vulnerabilities proactively	Block attacks in real-time
Examples	Garak, PyRIT, Promptfoo, DeepTeam	Lakera Guard, LLM Guard, NeMo Guardrails
Performance impact	None (runs offline)	Adds latency to requests
Best for	Development and QA	Production applications

My recommendation: Use both. Run testing tools like Garak, Promptfoo, or DeepTeam in CI/CD to catch issues early. Deploy runtime protection like Lakera Guard or LLM Guard for production applications that handle user input.

How to Choose an AI Security Tool

The AI security space is new, but these factors help narrow down your options:

Testing or Runtime Protection?

For vulnerability scanning before deployment, use Garak, PyRIT, Promptfoo, or DeepTeam. For runtime protection, use Lakera Guard, LLM Guard, or NeMo Guardrails.

LLM Provider Compatibility

Most tools work with any LLM via API. Garak, PyRIT, and NeMo Guardrails support local models. For ML model security scanning (not just LLMs), consider HiddenLayer or Protect AI Guardian.

Open-source vs Commercial

Seven tools are fully open-source: Garak, PyRIT, DeepTeam, LLM Guard, NeMo Guardrails, Rebuff, and Promptfoo (core). Lakera Guard offers a free tier. HiddenLayer and Protect AI Guardian are commercial for enterprise ML security.

CI/CD Integration

Promptfoo has first-class CI/CD support. Garak, PyRIT, and DeepTeam can run in CI with some setup. For runtime protection, LLM Guard and Lakera Guard are single API calls.

Frequently Asked Questions

What is AI Security?

AI Security refers to the practice of testing and protecting AI/ML systems, particularly Large Language Models (LLMs), against attacks like prompt injection, jailbreaks, hallucinations, and data leakage. Traditional security scanners do not cover these AI-specific risks.

What is prompt injection?

Prompt injection is an attack where malicious input tricks an LLM into ignoring its instructions and performing unintended actions. For example, an attacker might embed hidden instructions in user input that causes the model to reveal system prompts or bypass safety filters.

What is the OWASP Top 10 for LLM Applications?

The OWASP Top 10 for LLM Applications is a framework that identifies the top 10 security risks for LLM-based applications. It includes prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, and more.

Do I need AI security tools if I use OpenAI or Anthropic APIs?

Yes. While API providers implement safety measures, they cannot protect against application-level vulnerabilities like prompt injection in your specific use case, data leakage through your prompts, or misuse of the model within your application context.

Which AI security tool should I start with?

Start with Garak if you want comprehensive vulnerability scanning. It is free, backed by NVIDIA, and covers the widest range of attack types. For CI/CD integration, try Promptfoo.

Explore Other Categories

AI Security covers one aspect of application security. Browse other categories in our complete tools directory.

API Security ASPM DAST IaC Security IAST Mobile RASP SAST SCA

Written by

Suphi Cankurt

Suphi Cankurt is an application security enthusiast based in Helsinki, Finland. He reviews and compares 129 AppSec tools across 10 categories on AppSec Santa. Learn more.