Best AI Security Tools 2026: LLM Guard, Prompt Injection Defense & MLSecOps

What is AI Security?

AI security is the practice of testing and protecting AI and ML systems, especially 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 needs purpose-built tools to test it before launch and guard it at runtime. The 36 tools on this page split the work across four jobs: attack simulation, runtime defense, agentic AI and MCP governance, and observability for AI safety and compliance.

The OWASP Top 10 for LLM Applications (2025 edition) is the primary risk framework for LLM-powered applications. Prompt injection holds the LLM01:2025 position at the top of the list, and the threat is backed by research: the PoisonedRAG study (USENIX Security 2025) demonstrated that just 5 crafted documents can manipulate AI responses with a 90% attack success rate 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.

Key terms used on this page

• Prompt injection: an attack that embeds hidden instructions in user input (or in retrieved content) so the model ignores its system prompt and follows the attacker instead.
• Jailbreak: a special case of prompt injection that tricks the model into producing content its safety policy would normally refuse.
• RAG poisoning: inserting malicious documents into a retrieval-augmented generation knowledge base so they surface in the model’s context and manipulate the answer.
• Runtime guardrails: an inline layer that inspects every prompt and response in production, blocking or rewriting content that violates policy.
• Agentic AI: LLM-driven systems that plan and call tools on their own. The attack surface moves from text outputs to real actions like API calls, file writes, and payments.
• MCP (Model Context Protocol): Anthropic’s open protocol that lets LLM clients talk to external tools and data sources. Secure MCP deployments require scoped tools and authorization on every call.

The NIST AI Risk Management Framework (AI RMF 1.0) is the other major reference teams lean on for AI governance. It maps risks to four functions (Govern, Map, Measure, Manage) and is the framework most US enterprises cite when writing internal AI policies alongside the OWASP list.

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 and MCP security (Onyx, Noma, Cerbos, Cisco DefenseClaw, Agentic Radar, Skyrelis, Alter, Xage, 7AI) that govern autonomous agents and secure MCP servers, and AI governance and observability (Holistic AI, Arize AI, Galileo AI, Arthur AI, Vectara, Protecto, WitnessAI, Lasso Security, NeuralTrust, CrowdStrike AIDR, Cylake) that handle compliance, monitoring, and risk management.

AI Safety vs AI Security: Two Different Jobs

AI safety and AI security are often used interchangeably in marketing copy, but they answer different questions, belong to different teams, and ship on different tools. Treating them as the same thing is the fastest way to end up with neither.

AI safety asks whether the model’s output is honest, unbiased, and aligned with human values. It covers hallucinations, toxicity, bias, and the risk of generating harmful content. The owners are research, trust and safety, and ML alignment teams, and the tools look like eval harnesses and observability dashboards (Holistic AI, Arize AI, Galileo AI).

AI security asks whether an attacker can manipulate, poison, or exfiltrate data from your LLM application. It covers prompt injection, jailbreaks, RAG poisoning, and model theft. The owners are application security, red team, and platform engineering, and the tools look like scanners and guardrails (Garak, PyRIT, LLM Guard, NeMo Guardrails).

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:

How Defenses Map to Threats

Reading the OWASP list is one thing. Knowing which tool stops which risk is another.

Every LLM app faces three recurring attack classes in production, and each one maps to a distinct defense layer. Teams that skip a layer usually discover the gap the hard way, in a support ticket or a postmortem.

The first class is prompt injection: direct and indirect attacks that smuggle instructions into the model’s context. The primary defense is pre-deployment testing with a red-team scanner. Garak ships over 100 attack probes covering jailbreaks, encoding tricks, and goal hijacking, while PyRIT (Microsoft) and DeepTeam automate adversarial prompt generation so regression tests catch new payloads before they hit production.

The second class is data leakage: the model inadvertently echoing PII, credentials, or the system prompt itself. The primary defense is a runtime guardrail layer. LLM Guard and NeMo Guardrails inspect every prompt and response, anonymize PII, and reject outputs that match sensitive patterns before they reach the user.

The third class is RAG and agent compromise: poisoned documents in the vector store, or agent tools being tricked into destructive actions. The primary defense is observability plus authorization. Arize AI and Galileo AI monitor retrieved documents and output quality, while Cerbos and MCP-aware access control limit which documents and tools an agent can even reach.

Quick Comparison of AI Security Tools

The three biggest moves in 2025 reshaped which tools you can realistically standardize on. Check Point acquired Lakera in September 2025, folding its prompt injection defense into the broader Check Point security portfolio.

Palo Alto Networks signed a definitive agreement to acquire Protect AI in July 2025, bringing ML model scanning and MLSecOps workflows inside the Prisma Cloud stack.

Rebuff, the original open-source prompt injection detection SDK, was archived on GitHub in May 2025, a reminder that early OSS wins in this space aged fast as the attack surface grew.

36 AI Security Tools at a Glance

Every tool in the comparison above has its own identity — a specific attack library, a specific guardrail policy, or a specific agent governance model. This gallery gives you a visual for each one so you can recognize the tool when a teammate pastes a screenshot into Slack, or decide which one to install next.

Garak

NVIDIA's "Nmap for LLMs" — the widest open-source attack probe library for red-team scanning.

PyRIT

Microsoft's open-source Python Risk Identification Toolkit for automated LLM red teaming.

DeepTeam

Open-source framework covering 40+ LLM vulnerability types with OWASP LLM Top 10 mapping.

Augustus

LLM vulnerability scanner shipping attack playbooks for structured red-team runs.

FuzzyAI

CyberArk's open-source LLM jailbreak fuzzer. Runs bulk probes across providers.

Giskard

Freemium LLM testing and red-teaming framework with an open-source core and cloud hub.

Mindgard

Commercial DAST-AI platform running continuous red-team probes against deployed LLM apps.

ART (Adversarial Robustness Toolbox)

IBM's open-source library for adversarial attacks and defenses across classical ML and deep learning models.

Prompt Inspector

Open-source prompt injection detection library that scores user inputs against known attack patterns.

LLM Guard

Open-source runtime guardrail library with PII anonymization, content moderation, and prompt injection detection.

NeMo Guardrails

NVIDIA's open-source programmable guardrails — Colang-based rails for input, dialog, and output safety.

Guardrails AI

Open-source LLM output validation framework with a reusable validator hub and structured output enforcement.

OpenAI Guardrails

OpenAI's first-party agent input/output validation layer with built-in policy checks.

Lasso Security

Commercial GenAI security platform with shadow AI discovery and end-to-end LLM traffic monitoring.

NeuralTrust

Commercial AI gateway with guardian agents — inline policy enforcement, tracing, and red-team testing.

Protecto

Commercial AI data privacy layer that masks PII before it reaches the LLM and reverses it in responses.

Onyx Security

Enterprise AI control plane unifying agent discovery, policy enforcement, and observability.

Noma Security

Unified agent security platform covering discovery, posture, and runtime enforcement for AI agents.

Akto

AI agent and MCP security platform built on Akto's API testing engine.

Agentic Radar

Open-source CLI scanner that maps agentic workflows and flags policy and security gaps.

Cerbos

Open-source policy engine for fine-grained authorization — widely used as the authz layer for AI agents.

Cisco DefenseClaw

Cisco's open-source agentic AI governance framework, focused on runtime policy enforcement.

Alter AI

Y Combinator S25 startup shipping zero-trust access control for AI agents across MCP and REST tools.

Xage Security

Identity-based zero trust for AI at the protocol layer — agent-to-tool authentication at every hop.

Skyrelis

Always-on security for LLM multi-agent workflows, focusing on cross-agent tool misuse detection.

Knostic

Need-to-know access control for enterprise LLMs — enforces document-level permissions inside Copilot-style assistants.

7AI

AI SOC agents with Dynamic Reasoning — autonomous triage for alert backlogs in enterprise security teams.

Holistic AI

Commercial AI governance platform focused on EU AI Act compliance, bias audits, and risk registers.

Arize AI

AI observability platform with Phoenix (OSS) — the tracing backend most teams use to debug LLM apps.

Galileo AI

AI evaluation intelligence platform — covers hallucination, toxicity, and safety metrics across production traces.

Arthur AI

AI observability and bias detection suite — used by regulated industries for fairness and drift monitoring.

Vectara

Governed enterprise agent platform with built-in RAG grounding and hallucination detection.

WitnessAI

AI security and governance platform with activity monitoring, policy enforcement, and DLP for LLM traffic.

CrowdStrike Falcon AIDR

AI Detection and Response module inside Falcon — telemetry for AI agents and LLM app workloads.

Cylake

AI-native cybersecurity with data sovereignty controls for regulated environments.

HiddenLayer

ML model security platform — scans models for backdoors, adversarial weaknesses, and supply-chain tampering.

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 and 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.

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:

Frequently Asked Questions

Related AI Security Resources

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Suphi Cankurt

Founder, AppSec Santa

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Years in application security. Reviews and compares 215 AppSec tools across 12 categories to help teams pick the right solution. More about me →