Updated on
February 28, 2025
Analyst Report: OWASP LLM and Generative AI Security Solutions Landscape
The LLM and Generative AI Security Solutions Landscape is an industry report developed by OWASP that maps out key vendors and solutions in the AI security space. This landscape provides a comprehensive view of tools and technologies that help organizations safeguard their AI-powered applications.
Fergal Glynn
TABLE OF CONTENTS
Key Takeaways
Key Takeaways

1. Emergence of AI Security Solutions

  • The security landscape for LLMs and Generative AI is rapidly evolving, requiring new security paradigms beyond traditional cybersecurity tools.
  • Organizations are struggling to secure AI applications, as traditional security measures often fail to address risks unique to AI models, such as prompt injection, adversarial attacks, and model bias.

2. Four Primary LLM Application Architectures Identified

  • Static Prompt Augmentation Applications: Simple applications relying on predefined prompts, vulnerable to prompt injection attacks and data leakage.
  • Agentic Applications: Autonomous AI agents that perform tasks with decision-making capabilities. These need strict security measures to prevent unauthorized actions.
  • LLM Plug-ins & Extensions: Third-party tools that integrate LLMs into existing software, increasing risks related to API security, unauthorized data access, and compatibility vulnerabilities.
  • Complex LLM Applications: Advanced AI-powered applications requiring multi-layered security controls to prevent misconfigurations, data breaches, and compliance issues.

3. OWASP's Top 10 for LLMs Defines Key Risks

  • The OWASP Top 10 for LLM Applications provides a structured approach to addressing critical vulnerabilities such as:
    • Prompt Injection (LLM01:2025)
    • Insecure Plugin Architectures (LLM04:2025)
    • Model Theft (LLM06:2025)
    • Data Poisoning (LLM09:2025)
  • These categories help guide developers, security teams, and AI stakeholders in mitigating key threats.

4. AI Security Solutions Landscape is Maturing

  • Several emerging security tools are addressing AI-specific vulnerabilities:
    • LLM Firewalls: Blocking unauthorized access and malicious inputs.
    • LLM Benchmarking & Testing: Evaluating LLM performance, security, and adversarial robustness.
    • Penetration Testing for AI: Identifying AI system vulnerabilities before attackers can exploit them.
    • AI Security Posture Management (AI-SPM): Encompassing continuous security monitoring and compliance tracking.

5. Security is the Biggest Barrier to AI Adoption

  • Security concerns are the #1 factor slowing down enterprise AI adoption.
  • Organizations are seeking comprehensive AI security frameworks to ensure trust, compliance, and resilience in AI deployments.
  • OWASP’s LLM & GenAI Security Solutions Landscape serves as a reference guide to help security professionals navigate the growing field of AI security tools.

What is OWASP?

The Open Web Application Security Project (OWASP) is a globally recognized non-profit organization dedicated to improving software security. OWASP provides free and open resources, including best practices, frameworks, and tools, to help organizations mitigate security risks in web applications, cloud computing, and emerging technologies like AI.

For over two decades, OWASP has been a trusted authority in security, widely referenced by businesses, developers, and security professionals. Their Top 10 Security Risks lists are industry benchmarks that help guide secure software development.

What is the OWASP Top 10 for LLM Applications?

With the rise of Large Language Models (LLMs) and Generative AI, OWASP introduced the Top 10 for LLM Applications to identify and address the most pressing security risks associated with AI-powered systems. This list helps organizations understand vulnerabilities unique to AI models and provides guidance on mitigating them.

The OWASP Top 10 for LLM Applications includes threats such as:

  • Prompt Injection Attacks – Manipulating LLM behavior through crafted inputs.
  • Data Leakage – LLMs unintentionally exposing sensitive information.
  • Adversarial Inputs – Maliciously designed queries that cause unintended AI responses.
  • System Prompt Leakage – Unauthorized access to system prompts and model instructions.
  • Unbounded Consumption – Overuse or abuse of AI models leading to performance degradation.

By following OWASP’s guidelines, organizations can deploy AI more securely and responsibly while minimizing risks.

What is the LLM and Generative AI Security Solutions Landscape?

The LLM and Generative AI Security Solutions Landscape is an industry report developed by OWASP that maps out key vendors and solutions in the AI security space. This landscape provides a comprehensive view of tools and technologies that help organizations safeguard their AI-powered applications.

With AI adoption accelerating across industries, OWASP created this landscape to help security leaders, developers, and decision-makers identify trusted solutions that address the evolving threats in AI.

What Are the Key Findings of the OWASP LLM and Generative AI Security Solutions Landscape?

The 2025 OWASP report highlights several critical trends in AI security, including:

  • AI Security is Now a Business Imperative – Organizations must proactively address AI risks to maintain trust, compliance, and operational resilience.
  • Rapid Growth of AI Security Solutions – More vendors are entering the space, developing specialized tools for adversarial testing, AI runtime security, and vulnerability scanning.
  • The Need for AI-Specific Testing – Traditional security approaches (e.g., SAST, DAST, IAST) are not enough—AI systems require new testing methodologies that address LLM-specific risks.
  • Focus on Regulatory Compliance – Businesses deploying AI must align with emerging compliance frameworks, ensuring AI governance, explainability, and ethical considerations.

What Direction is the AI Security Solutions Landscape Headed?

The AI security landscape is evolving rapidly, with several clear trends emerging:

  • Shift from Reactive to Proactive Security – Organizations are moving from reactive security (patching vulnerabilities after attacks) to proactively testing and securing AI models before deployment.
  • AI Security Automation – Automated security tools, including AI-powered red teaming and continuous monitoring, are becoming essential for large-scale AI deployments.
  • Increasing Collaboration – Security researchers, enterprises, and regulatory bodies are working together to define AI security standards and frameworks.
  • Integrated AI Security in DevOps – AI security is being embedded into the software development lifecycle (AI DevSecOps), ensuring security from model training to deployment.

How Mindgard Fits into OWASP's LLM and Generative AI Security Solutions Landscape

Mindgard is proud to be featured in OWASP’s LLM and Generative AI Security Solutions Landscape as a leading provider of AI security solutions. Our inclusion reflects our commitment to helping organizations safely deploy AI while mitigating security risks.

Mindgard’s solutions align with OWASP’s priorities in several key areas:

  • Adversarial Testing – Protecting AI systems from malicious inputs and adversarial manipulation.
  • Vulnerability Scanning – Identifying weaknesses in AI models, training data, and inference processes.
  • Final Security Audit – Ensuring AI systems are secure, compliant, and ready for production.
  • LLM Benchmarking – Evaluating AI model performance and security against industry standards.
  • Penetration Testing for AI – Simulating real-world attacks to uncover vulnerabilities before attackers do.
  • SAST/DAST/IAST for AI – Bringing runtime vulnerability detection to AI applications, ensuring security throughout the AI lifecycle.

With security risks emerging as the biggest blocker to AI adoption, Mindgard is committed to delivering solutions that help organizations confidently embrace AI’s potential—without compromising security.

Learn More

The report highlights the urgency for robust security solutions in AI development and deployment while offering a framework for organizations to align their security strategies with emerging best practices.

Explore our blog about the OWASP LLM and Generative AI Security Solutions Landscape and discover how Mindgard is leading the charge in AI security.

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