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

What is AI Security?

AI security is the practice of testing and protecting AI/ML systems — particularly Large Language Models (LLMs) — against threats like prompt injection, jailbreaks, data poisoning, and sensitive information disclosure. Traditional application security scanners were not designed for these risks, so any application that interacts with an LLM requires purpose-built tools to test it before launch and guard it at runtime.

The OWASP Top 10 for LLM Applications (2025 edition) is the primary risk framework for LLM-powered applications.

Prompt injection holds the #1 position, and the threat is backed by research: the PoisonedRAG study (USENIX Security 2025) demonstrated that just five crafted documents can manipulate AI responses 90% of the time through RAG poisoning, even in knowledge bases containing millions of texts.

That single finding underscores why pre-deployment testing and runtime guardrails are both necessary for any production LLM application.

I split the tools on this page into two groups: testing tools (Garak, PyRIT, Promptfoo) that find vulnerabilities before you deploy, and runtime guards (LLM Guard, NeMo Guardrails) that block attacks on live traffic.

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
Standards still maturing (OWASP LLM Top 10 and NIST AI RMF exist but evolving)
Limited coverage of all AI risk types
Requires AI/ML expertise to interpret results

What Are the OWASP Top 10 Risks for LLM Applications?

The OWASP Top 10 for LLM Applications (2025 edition) defines the ten most critical security risks for any application built on large language models. If you’re building on LLMs, these are the risks you should be testing for:

Prompt Injection

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

Sensitive Information Disclosure

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

Supply Chain Vulnerabilities

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

Data and Model Poisoning

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

Improper Output Handling

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

Excessive Agency

LLM-based systems granted excessive functionality, permissions, or autonomy, enabling harmful actions triggered by unexpected outputs.

System Prompt Leakage

Attackers extracting or inferring system prompts, revealing business logic, filtering criteria, or access controls embedded in the prompt.

Vector and Embedding Weaknesses

Vulnerabilities in how vector databases and embeddings are generated, stored, or retrieved, enabling data poisoning or unauthorized access in RAG systems.

Misinformation

LLMs generating false or misleading content that appears authoritative. Critical for applications where users rely on model outputs for decision-making.

Unbounded Consumption

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

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+ vulnerability types, 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 ARCHIVED	Prompt injection detection SDK; archived May 2025	Runtime	Open Source
Commercial
Lakera Guard ACQUIRED	Gandalf game creator; acquired by Check Point (September 2025)	Runtime	Commercial
HiddenLayer AISec	ML model security platform	Both	Commercial
Protect AI Guardian ACQUIRED	ML model scanning; acquired by Palo Alto Networks (July 2025)	Testing	Commercial

What Is the Difference Between AI Testing Tools and Runtime Protection?

AI security tools fall into two categories that mirror traditional AppSec. Testing tools (like SAST/DAST in conventional security) scan for vulnerabilities before deployment.

Runtime protection tools (like WAFs and RASP) block attacks against live production applications.

Most teams need both — testing alone misses novel attack patterns that emerge after deployment, and runtime guards alone leave you blind to systemic weaknesses during development.

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 take: Use both. I’d run Garak or Promptfoo in CI/CD to catch issues before they ship, then put LLM Guard or Lakera Guard in front of any production app that takes user input.

Testing alone will not stop a novel prompt injection at runtime, and runtime guards alone mean you are flying blind during development.

How Do You Choose the Right AI Security Tool?

Selecting an AI security tool comes down to four factors: whether you need pre-deployment testing or runtime protection, which LLM providers you use, your budget constraints, and how tightly the tool integrates with your CI/CD pipeline. This space is still young, but I’ve found these four questions cut through the noise:

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

Six tools are fully open-source: Garak, PyRIT, DeepTeam, LLM Guard, NeMo Guardrails, and Promptfoo (core). Rebuff was archived in May 2025 and is no longer maintained. HiddenLayer is commercial for enterprise ML security. Lakera Guard and Protect AI Guardian were acquired in 2025 (by Check Point and Palo Alto Networks respectively).

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.

Akto

AI Agent & MCP Security Platform

Commercial (Free tier available)

Arthur AI

NEW

AI Observability and Bias Detection

Commercial (with open-source components)

DeepTeam

LLM Red Teaming Framework

Free (Open-Source) 1 langs

Garak

NEW

NVIDIA's LLM Vulnerability Scanner

Free (Open-Source) 1 langs

HiddenLayer AISec

ML Model Security Platform — 48+ CVEs, 25+ Patents

Commercial

LLM Guard

Open-Source LLM Guardrails

Free (Open-Source) 1 langs

Mindgard

NEW

DAST-AI Continuous Red Teaming

Commercial

NVIDIA NeMo Guardrails

NVIDIA's Programmable LLM Guardrails

Free (Open-Source) 1 langs

PyRIT

NEW

Microsoft's AI Red Team Framework

Free (Open-Source) 1 langs

Show 6 deprecated/acquired tools

CalypsoAI acquired Lakera Guard acquired Prompt Security acquired Promptfoo acquired Protect AI Guardian acquired Rebuff deprecated

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. The 2025 edition covers prompt injection, sensitive information disclosure, supply chain vulnerabilities, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption.

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.

AI Security Guides

Vibe Coding Security AI-Generated Code Security LLM Red Teaming Prompt Injection Attacks What is AI Security?

AI Security Comparisons

Garak vs Promptfoo

AI Security Alternatives

Lakera Alternatives

Explore Other Categories

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

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Suphi Cankurt

AppSec Enthusiast

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10+ years in application security. Reviews and compares 168 AppSec tools across 11 categories to help teams pick the right solution. More about me →