36 Best AI Security Tools (2026)
Vendor-neutral comparison of 36 AI security tools for LLMs. Covers prompt injection, jailbreaks, and data leakage. Includes open-source.
- I reviewed 36 AI security tools — 16 open-source, 2 freemium, and 18 commercial — now spanning four subcategories: testing/red-teaming (Garak, PyRIT, Promptfoo), runtime protection (LLM Guard, NeMo Guardrails), agentic AI & MCP security (Onyx, Noma, MCP-Scan, Cerbos), and AI governance & observability (Holistic AI, Arize AI, Galileo AI).
- Prompt injection is the #1 vulnerability in the OWASP Top 10 for LLM Applications (2025). Research (PoisonedRAG, USENIX Security 2025) shows just 5 crafted documents can manipulate AI responses 90% of the time via RAG poisoning.
- Garak (NVIDIA) and Promptfoo are the go-to free testing tools — Garak covers the widest attack range, Promptfoo has first-class CI/CD support.
- Major acquisitions reshaped this space: Lakera Guard acquired by Check Point (September 2025), Protect AI Guardian by Palo Alto Networks (July 2025), and Rebuff was archived in May 2025.
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 four groups: testing tools (Garak, PyRIT, Promptfoo, Augustus, DeepTeam) that find vulnerabilities before you deploy, runtime guards (LLM Guard, NeMo Guardrails, Guardrails AI, OpenAI Guardrails, Lakera) that block attacks on live traffic, agentic AI & MCP security (Onyx, Noma, MCP-Scan, Cerbos, Cisco DefenseClaw, Agentic Radar, Skyrelis, Alter, Xage, 7AI) that govern autonomous agents and secure MCP servers, and AI governance & observability (Holistic AI, Arize AI, Galileo AI, WhyLabs, Vectara, Protecto, WitnessAI, Lasso Security, NeuralTrust, CrowdStrike AIDR, Cylake) that handle compliance, monitoring, and risk management.
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 |
| Augustus | LLM vulnerability scanner with attack playbooks | Testing | Open Source |
| Prompt Inspector | Prompt injection detection library | 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 |
| Guardrails AI | LLM output validation framework | Runtime | Open Source |
| OpenAI Guardrails | Agent input/output validation | 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 |
| Agentic AI & MCP Security (NEW) | |||
| Onyx Security | AI control plane for enterprise agents | Agentic | Commercial |
| Noma Security | Unified AI agent security platform | Agentic | Commercial |
| MCP-Scan | MCP server security scanner | MCP Security | Open Source |
| Cerbos | Policy-based authorization for AI agents | MCP Security | Open Source |
| Cisco DefenseClaw | Agentic AI governance framework | MCP Security | Open Source |
| Agentic Radar | CLI scanner for agentic workflows | MCP Security | Open Source |
| 7AI | AI SOC agents with Dynamic Reasoning | Agentic | Commercial |
| CrowdStrike Falcon AIDR | AI Detection & Response | Agentic | Commercial |
| AI Governance & Observability (NEW) | |||
| WitnessAI | AI security & governance platform | Governance | Commercial |
| Holistic AI | AI governance & EU AI Act compliance | Governance | Commercial |
| Arize AI | AI observability with Phoenix (OSS) | Observability | Commercial + OSS |
| Galileo AI | AI evaluation intelligence | Observability | Commercial |
| Lasso Security | GenAI security with shadow AI discovery | Governance | Commercial |
| NeuralTrust | AI gateway & guardian agents | Runtime | Commercial |
| Protecto | AI data privacy & masking | Governance | 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, Augustus | Lakera Guard, LLM Guard, NeMo Guardrails, Guardrails AI, OpenAI 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 five factors: whether you need pre-deployment testing or runtime protection, which LLM providers you use, your budget constraints, how tightly the tool integrates with your CI/CD pipeline, and whether you are building agentic AI systems. This space is still young, but I’ve found these five 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.
Do You Need to Secure AI Agents or MCP Servers?
If you are deploying autonomous AI agents, Onyx and Noma provide enterprise agent governance with policy enforcement and visibility. For MCP server security, MCP-Scan and Cerbos scan for vulnerabilities and enforce authorization policies. Agentic Radar analyzes agentic workflows for security gaps across the entire agent pipeline.
7AI
NEWAI SOC Agents with Dynamic Reasoning
Adversarial Robustness Toolbox (ART)
IBM's ML security library for adversarial attacks and defenses
Agentic Radar
NEWSecurity Scanner for LLM Agentic Workflows
Akto
AI Agent & MCP Security Platform
Alter
NEWZero-Trust Access Control for AI Agents (YC S25)
Arize AI
NEWOpenTelemetry-based AI observability with open-source Phoenix
Arthur AI
NEWAI Observability and Bias Detection
Augustus
NEWProduction-grade LLM vulnerability scanner with 210+ adversarial probes
Cerbos
NEWPolicy-Based Authorization for AI Agents and MCP Servers
Cisco DefenseClaw
NEWEnterprise Security Governance for Agentic AI
CrowdStrike Falcon AIDR
NEWAI Detection & Response for the Falcon Platform
Cylake
NEWAI-Native Cybersecurity with Data Sovereignty
DeepTeam
LLM Red Teaming Framework
FuzzyAI
NEWCyberArk's open-source LLM jailbreak fuzzer
Galileo AI
NEWAI Evaluation Intelligence with Luna Models
Garak
NEWNVIDIA's LLM Vulnerability Scanner
Giskard
LLM testing and red teaming framework
Guardrails AI
NEWOpen-Source LLM Validation with Guardrails Hub
HiddenLayer AISec
ML Model Security Platform — 48+ CVEs, 25+ Patents
Holistic AI
NEWEnd-to-end AI governance for compliance and risk management
Knostic
NEWNeed-to-know access control for enterprise LLMs
Lasso Security
NEWEnd-to-End GenAI Security with Shadow AI Discovery
LLM Guard
Open-Source LLM Guardrails
Mindgard
NEWDAST-AI Continuous Red Teaming
NeuralTrust
NEWAI Gateway, Red Teaming & Agent Security
Noma Security
NEWUnified AI Agent Security with 1,300% ARR Growth
NVIDIA NeMo Guardrails
NVIDIA's Programmable LLM Guardrails
Onyx Security
NEWSecure AI Control Plane for Enterprise Agents
OpenAI Guardrails
NEWDrop-In Safety Wrapper for OpenAI Agents
Prompt Inspector
NEWMulti-layer prompt injection detection for LLM applications
Protecto
NEWContext Security & Data Privacy for AI Agents
PyRIT
NEWMicrosoft's AI Red Team Framework
Skyrelis
NEWAlways-On Security for LLM Multi-Agent Workflows
Vectara
NEWGoverned Enterprise Agent Platform
WitnessAI
NEWIntent-Based AI Security & Governance
Xage Security
NEWIdentity-Based Zero Trust for AI at Protocol Layer
Show 8 deprecated/acquired tools
Frequently Asked Questions
What is AI Security?
What is prompt injection?
What is the OWASP Top 10 for LLM Applications?
Do I need AI security tools if I use OpenAI or Anthropic APIs?
Which AI security tool should I start with?
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+7 Years in AppSec
10+ years in application security. Reviews and compares 208 AppSec tools across 11 categories to help teams pick the right solution. More about me →