The best AI security tools for LLM and GenAI protection have become an urgent purchase: 13% of organizations reported breaches of AI models or applications in IBM's 2025 Cost of a Data Breach study and 97% of those lacked basic AI access controls. These platforms stop AI-specific attacks like prompt injection, model inversion and data leakage, the kind of security risks that large language models (LLMs) and generative AI (GenAI) introduce, threats that traditional security tools were never designed to handle.

This guide compares 11 leading AI security tools, plus 2 honorable mentions, from Mindgard's continuous automated red teaming to open source frameworks like the Adversarial Robustness Toolbox, so you can secure your LLMs and GenAI applications.

"The data shows that a gap between AI adoption and oversight already exists, and threat actors are starting to exploit it. As AI becomes more deeply embedded across business operations, AI security must be treated as foundational." - Suja Viswesan, Vice President, Security and Runtime Products, IBM. IBM Newsroom, July 2025.

The 13 tools below are how security teams are closing that gap in practice.

Best AI Security Tool Comparison

The 13 tools in this guide, grouped by primary security function. Category and capability notes reflect each vendor's current documentation as of June 2026.

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●MINDGARD AI Security Tool Explorer · June 2026

Compare 13 AI Security Tools for LLM and GenAI Protection

Filter by security category or licensing. Search by name or capability. Click a column header to sort.

All categories Offensive security and red teaming Data protection and shadow AI Guardrails, governance and privacy All licensing Open source Commercial

Tool	Category	Best for	Key strength	Open source

Categories and capability notes reflect the Mindgard review of vendor documentation, June 2026. Tool names link to each vendor’s site.

What are AI Security Tools?

AI security tools are programs that either protect AI systems like large language models (LLMs) and generative AI (GenAI) apps from evolving risks or use AI to improve cybersecurity. They range from software that guards proprietary models and data from malicious inputs to products that apply real-time machine learning to automate detection of threats.

The category includes automated red teaming platforms designed to attack your own models and find vulnerabilities, runtime guardrails that filter prompts and responses in real-time as your apps are running and DLP tools that prevent sensitive data from being uploaded to public AI services. The 13 tools covered by this guide fall into one of these three categories.

"Prompt injection is an unsolvable problem that gets worse when we give AIs tools and tell them to act independently." -  Bruce Schneier, Fellow and Lecturer, Harvard Kennedy School, with Barath Raghavan. IEEE Spectrum, January 2026.

That’s why tools for continuous testing exist. If you can’t eliminate injection, you need to hunt it on a schedule.

The 11 Best AI Security Tools

1. Mindgard

Mindgard is an AI security platform that protects AI systems, including Large Language Models (LLMs) and GenAI applications. It automates red teaming and continuous security testing to help enterprises understand and fix security vulnerabilities unique to AI systems that traditional security tools can't detect. The Mindgard platform was created from more than 10 years of academic research from Lancaster University. Mindgard stops attackers from exploiting AI systems by helping organizations stay proactive about security throughout the AI lifecycle.

Mindgard's flagship product is its Offensive Security solution which leverages simulated adversarial attacks to uncover vulnerabilities such as prompt injection, model inversion, data poisoning and evasion attacks. This proactive approach identifies runtime-specific threats beyond the capabilities of static analysis. Mindgard seamlessly plugs into existing CI/CD workflows to continuously monitor LLMs and prioritize mitigation, turning months of testing into minutes.

Mindgard is model agnostic and supports attacks against LLMs, NLP models, as well as multi-modal applications. With a library mapped to industry frameworks such as MITRE ATLAS and OWASP, security teams can effectively defend against emerging attacks. We also integrate with your existing SIEM to ensure AI security is actionable and auditable.

‍Key features

• Automated red teaming uncovers attack scenarios such as prompt injection, model inversion, data poisoning, and more
• Embed security testing into your workflow with CI/CD integration
• Detect issues at runtime instead of relying on static analysis alone
• Gain insight with an attack library that maps to MITRE ATLAS and OWASP
• Generate detailed reports your organization needs for internal and external compliance
• Keep your models secure with continuous monitoring

2. Amazon Bedrock Guardrails

Amazon Bedrock Guardrails is a set of adjustable safety features that enable organizations to develop secure and responsible generative AI applications at scale. Available through Amazon Bedrock, Guardrails offers a uniform approach to enforce safety and compliance policies across foundation models, as well as fine-tuned models and models hosted outside Bedrock.

Guardrails uses contextual grounding checks to measure whether model responses are factually grounded and decrease hallucinations. Automated Reasoning checks reason over information using logical and mathematical principles to ensure output aligns with ground truth documents/policies. Filters flag/filter dangerous output across text and image modalities which can be customized based on your use case.

‍Key features

• Detects and optionally redacts personally identifiable information like names, addresses, and phone numbers
• Determines if a model response is sourced from the given context/source document
• Formally verifies generated responses against documents/facts your team has already approved
• Drag-and-drop interface to configure guardrails with no machine learning or coding experience needed
• Insights into when and why guardrails were triggered

3. Checkmarx

Checkmarx is a software company that provides an application security platform enabling organizations to secure their applications throughout the development lifecycle. These include AI-powered applications, LLM integrations as well as GenAI pipelines. Its core platform, Checkmarx One, combines SAST, SCA, API security and Infrastructure as Code scanning into a unified solution built for the cloud-native era.

Checkmarx brings AI to application security for real-time vulnerability detection, contextual explanations and automated remediation recommendations directly within developer tools. Checkmarx also secures AI-generated code and the modern software supply chain, helping to manage risks from GenAI adoption.

Key features

• Comprehensive AppSec platform including SAST, DAST, SCA, API security, IaC, and container scanning
• AI-driven vulnerability identification with instant feedback in IDEs
• Auto-remediation with AI-driven code fixes and contextual recommendations
• Application Security Posture Management (ASPM) for consolidated risk visibility and prioritization
• Security insights for AI-generated code and the modern software supply chain

4. Holistic AI 

Holistic AI is an enterprise AI governance platform that provides organizations with AI risk management, regulatory compliance and audit readiness tools. Holistic AI features automated discovery, risk scoring, lifecycle assessments and more.

Holistic AI's central dashboards and real-time monitoring provide operational insights you can act on for both decisions and continuous AI governance. Holistic AI's AI Safeguard helps enterprises operate LLMs safely by protecting against data exposure, bias, and other ethical concerns.

Key features

• Easily track compliance, risk and operational KPIs with dynamic dashboards
• Helps with compliance to international AI regulations (EU AI Act, NIST AI Risk Management Framework, etc.)
• Detect and mitigate unfairness within your AI models
• Redacts/block sensitive data being passed into/out of your LLMs
• Static and dynamic prompt testing for your LLMs

5. Adversarial Robustness Toolbox

The Adversarial Robustness Toolbox (ART) is an open source Python library focused on helping developers and researchers improve the security and robustness of machine learning models. Hosted by Linux Foundation AI & Data Foundation, ART provides tools for developers and researchers to test, defend, certify, and verify machine learning models against evasion, poisoning, extraction, and inference attacks.

Developers and researchers can easily customize and extend ART by adding novel attacks, defenses and evaluation techniques. ART is available on the Adversarial Robustness Toolbox GitHub repository.

Key features

• Attacks (evasion, poisoning, extraction, inference) for image, text, audio and tabular data
• Model robustness metrics to quantify vulnerabilities
• Works with TensorFlow, PyTorch, Keras, scikit-learn, XGBoost and more
• Includes defenses like adversarial training, input preprocessing and model regularization
• Supports classification, object detection, speech recognition and generative modeling

6. NVIDIA Flare

NVIDIA FLARE (Federated Learning Application Runtime Environment) is an open source, domain-agnostic SDK that enables secure privacy protection during federated learning. It lets organizations train and fine-tune LLMs and GenAI applications without centralizing sensitive data, addressing data privacy and regulatory compliance challenges.

FLARE enables clients to train models on their private datasets without revealing raw data. FLARE supports PyTorch, TensorFlow, XGBoost and others frameworks. It also supports federated learning algorithms like FedAvg, FedProx and FedOpt.

Key features

• Decentralized training without requiring sharing private data
• Allows supervised fine-tuning and parameter-efficient fine-tuning methods on large language models
• Includes differential privacy and homomorphic encryption tools to protect private data
• Flexible APIs and plugin support for bespoke workflows and components
• Aims to provide privacy that meets regulatory industry demands such as healthcare and finance

7. Flower

Flower is an open source federated learning framework for enhancing the privacy, scalability and personalization capabilities of LLMs and GenAI applications. With Flower developers can build applications that support decentralized training and inference of machine learning models.

Flower's federated learning architecture allows devices or organizations to collaboratively train models without sharing raw data between each other. Flower also pushes the boundaries of privacy-preserving artificial intelligence through Flower Intelligence, a local inference and confidential remote compute hybrid platform.

Key features

• Enable federated fine-tuning of large language models (LLMs) on private data
• Allows coupling on-device inference with privacy-preserving, confidential remote computation
• Keeps training data local and prevents leaks of sensitive information
• Facilitates federated learning scenarios from edge devices to the cloud
• Flexible and modular building blocks, including APIs to plug into your existing ML workflows

8. Netskope

Netskope's SkopeAI solution provides visibility into, and control over shadow AI. Netskope watches over usage of AI applications and automatically identifies consumer and business versions of AI products to apply policies at a granular level based on both behavior and data sensitivity.

Netskope uses data loss prevention and data security posture management to prevent sensitive information from ending up in training sets for public or private LLMs. These solutions identify, classify and label data within storage resources, allowing you to create policies that prevent confidential data from being used in AI.

Key features

• Catalogs and tracks over 370 GenAI apps, separating personal from business use (according to Netskope's released SkopeAI resources)
• Identifies shadow AI applications being used throughout the company
• Enforces contextual, real-time policies based on who the user is, what device and app instance they're using, and what they're doing within the app
• Delivers risk scores for tens of thousands of apps to inform access policies
• Blocks sensitive information from being shared with or used to train LLMs

9. Nightfall AI

Nightfall AI is an artificial intelligence-enhanced DLP platform that secures sensitive data wherever it travels through cloud apps, endpoints and GenAI tools. It creates a trust boundary around corporate and customer data that secures information wherever developers are building and deploying AI models.

Prevent sensitive data exposure (such as personally identifiable information, payment card information and protected health information) from being exposed. Protects against data exposure, privacy violations and prompt injection attacks leading to unintentional leakage or manipulation of AI responses.

Key features

• AI classification models with industry leading accuracy, according to Nightfall’s product docs
• A browser plugin that identifies and redacts sensitive information before it’s entered into AI tools such as ChatGPT
• Automated workflows that redact, quarantine, or alert when violations occur
• Visibility into where, how, and why sensitive data is accessed, shared, and used within GenAI workflows through detailed logging
• Pre-built integrations with leading SaaS, messaging, and AI platforms

10. Cyberhaven

Cyberhaven secures organizations’ visibility and control over their data with AI applications. Cyberhaven discovers and responds to shadow AI use cases when employees use artificial intelligence applications without IT’s knowledge.

The Cyberhaven data detection and response solution allows customers to monitor data interactions with AI applications in real-time, even without pre-configured policies. This allows businesses to block sensitive data from being consumed by unauthorized AI applications and also track the source of AI-generated content to reduce exposure to malicious and/or erroneous AI-generated data.

Key features

• Prevents source code, training data and proprietary models from being inadvertently exposed or stolen
• Labels generated content from AI tools so it can be traced
• Recognizes when personal accounts are being used in a corporate environment and segregates them
• Educates employees on how to use generative AI responsibly and in compliance with your corporate policies
• Allows you to set nuanced, role-based guardrails on your generative AI tools

11. Symmetry

Symmetry Systems works to secure GenAI deployments by mapping out potential risks before models interact with data stored in environments like OneDrive, SharePoint and Microsoft Teams. Symmetry scans for and monitors sensitive information before it's accessed by generative AI models and before it can be embedded in model knowledge, stopping data leaks before they happen.

Symmetry Systems can also ensure your organization maintains its compliance and ethical standards with continued GenAI monitoring of user access to information.

Key features

• Maintains a catalog of generative AI models in deployment, such as LLaMA, OpenAI and ChatGPT
• Easily integrates into existing security stacks including SIEM, SOAR, and ticketing solutions
• Observes generative AI usage with regulated data to help maintain regulatory compliance
• Allows you to customize classification rulesets and train your own models on your data
• Identifies sensitive data in images and PDFs throughout unstructured data repositories

Honorable Mentions

Prompt Security

Prompt Security provides enterprise-grade, comprehensive security for AI use within the enterprise. It analyzes every prompt entered into every LLM as well as every response generated, and it can prevent prompt injection attacks and jailbreaks that attempt to coerce an LLM into taking actions that might be unsafe or malicious.

Data privacy and protection are also managed, preventing accidental data exposure should an LLM connect to internal data repositories, and proactive penetration testing that attacks your in-house AI apps.

Key features

• Monitor prompts/responses in real time across all your LLM apps
• Potentially dangerous input/output such as prompt injection, jailbreaks and toxic outputs are blocked
• Guardrails prevent your data from leaving your org or accessing unauthorized APIs
• Ethical AI red teaming to identify weaknesses before production
• Works with most major LLMs

Lakera

Lakera is an AI-native application security platform designed to secure generative AI apps and other applications powered by LLMs. Lakera Guard analyzes inputs and outputs at runtime and blocks unwanted behavior like prompt injection, leaking of sensitive data and content violations dynamically and in real-time.

Lakera Red attacks your model before deployment to help catch vulnerabilities as early as possible. It tests for prompt attacks, leakage, improper use of tools that call out to the internet, and errors in complicated setups such as when agents call other agents. Lakera can be deployed in the cloud or on-premise.

Key features

• Lakera Guard provides real-time protection that monitors every prompt and response
• Easily deploy LLMs to support multimodal, multilingual use cases
• Scan models ahead of time with Lakera Red for red teaming and vulnerability discovery
• Unified, centralized policy control across every AI app

Which AI Security Tools Are Open Source?

The most widely used open source AI security tools are the Adversarial Robustness Toolbox (ART), NVIDIA FLARE, Flower and Garak. ART, hosted by the Linux Foundation, simulates evasion, poisoning, extraction and inference attacks against models built in TensorFlow, PyTorch, scikit-learn and other frameworks.

NVIDIA FLARE and Flower are federated learning frameworks that keep training data decentralized. Garak is NVIDIA's open source LLM vulnerability scanner that tests models for prompt injection vulnerability, jailbreaks and data leakage. Open source AI security tools can be downloaded at no license cost, but still require engineering time to implement, configure and maintain.

How to Choose the Best AI Security Tools for LLM and GenAI Protection

Securing LLMs and GenAI applications begins with understanding where you are exposed: model endpoints, training pipelines, inference APIs, and the data they ingest. Companies developing in-house LLM apps will want tools for automated red teaming and runtime protection. Enterprises focused on managing employee AI usage will want tools for shadow AI discovery and data loss prevention. It's also useful to understand the overall vendor landscape of AI security companies and what security risks they're attempting to address. Ideal AI security solutions validate against established threat frameworks such as the OWASP Top 10 for LLM Applications and MITRE ATLAS, integrate with your SIEM and CI/CD pipelines, and provide both pre-deployment testing and runtime monitoring.

LLMs require specialized red teaming capabilities. Red teaming solutions should allow you to mimic prompt injection attacks, data leakage problems and jailbreak situations in a repeatable manner. See our AI pentesting tools shortlist for a comparison of the top tools.

"This year, we've seen agentic systems move from experiments to real deployments, and that shift brings a different class of threats into clear view." - Steve Wilson, Project Lead, OWASP GenAI Security Project. OWASP, December 2025.

For teams planning AI agents into their roadmap, prioritize tools that already have coverage of agent workflows as well as single-model prompts.

To that end, visibility should be another critical factor. Look for solutions that provide telemetry data that maps both user behavior and model responses, and highlight anomalous behavior so you can catch attacks in real-time vs. after the fact. Lastly, make sure the solution allows for both attack and defense: red teams attack and blue teams patch, and the best solutions allow you to do both rapidly.

How Much Do AI Security Tools Cost?

AI security solutions are offered in three categories:

1. Open source solutions (such as Adversarial Robustness Toolbox, NVIDIA FLARE and Flower) are free to license.
2. Commercial AI security solutions (such as Mindgard, Lakera and Prompt Security) typically charge based on the volume of usage, number of models being tested, or on an annual subscription basis. Annual costs for enterprise licenses typically range from low to high five figures.
3. Cloud-native guardrails such as Amazon Bedrock Guardrails bill based on the number of text units processed.

List pricing is rarely published so ask for quotes specific to your number of models and volume of requests.

Final Thoughts

The AI threat landscape isn’t standing still. Legacy defenses weren’t designed to address model-level exploits, generative misuse, or runtime security issues in LLMs. Protecting GenAI systems and LLMs requires evolving past superficial defenses to security tools designed for the unique inner workings of these models.

Mindgard’s Offensive Security solution is engineered from the ground-up for AI adversarial testing, as opposed to static scanners or repurposed monitoring platforms. It simulates realistic attacks to stress test your LLMs, uncovering runtime vulnerabilities only exposed during program execution. Featuring CI/CD integration and a model agnostic engine, Mindgard puts security into your AI pipeline for everything from development to production.

Book a demo today to learn how Mindgard can help you secure your GenAI and LLM applications.

Frequently Asked Questions 

How do LLMs and GenAI create risk?

Prompt injection, data leakages, model inversion, poisoning of training data, and adversarial inputs that try to manipulate output are security risks specific to LLMs and GenAI that attempt to steal sensitive data.

How can automated red teaming make AI more secure?

Automated red teaming questions the integrity of AI models in the same way a human attacker would. By simulating common real-world attacks such as prompt injection, evasion and data extraction, teams can identify weaknesses before a malicious actor does. Automated red teaming allows for ongoing, scalable security testing instead of relying only on manual security audits.

How does federated learning (e.g., NVIDIA FLARE, Flower) fit into AI security?

With federated learning, raw training data stays decentralized which inherently improves security and privacy. Rather than pooling sensitive data in one place it shares model updates. This sharing approach decreases exposure to security risks and helps with meeting data protection requirements.

Can I add AI security to my current DevOps pipeline?

AI security products can be integrated into CI/CD pipelines as part of the automated testing, red teaming and monitoring process. By adding this into your existing pipeline, you can scan in real-time for vulnerabilities and continually evaluate machine learning models as you build and update your software. Products such as Mindgard and ART allow for CI/CD integration, automating security testing throughout development, deployment and production.

Are open source AI security tools free?

Yes. Adversarial Robustness Toolbox, NVIDIA FLARE, Flower and Garak are free and open source AI security tools. There are some that cover adversarial attack simulation, federated learning and testing LLM vulnerabilities. However, they will take up engineering time to implement and operate.

When should I choose a commercial AI security tool vs. an open source one?

Use commercial platforms like Mindgard for ongoing automated testing, vendor support, compliance reports and SIEM integration. Use open source tools if you have ML security engineers on staff and need maximum flexibility for research or building custom pipelines. Some teams use both: open source libraries for experimenting and a commercial platform for broad production coverage.