Securing LLM Applications: Tools and Evaluation Criteria

According to Gartner research, security and product leaders must secure both the development and consumption of Generative AI (GenAI) while managing its impact on cybersecurity. Securing AI-integrated systems, particularly those utilizing GenAI, is a critical concern for organizations today. The unique challenges posed by GenAI, such as prompt injection attacks, data leakage, and model manipulation, necessitate robust security measures.

Product managers play a pivotal role in ensuring that enterprise GenAI applications are secure, compliant, and resilient against vulnerabilities and sensitive data leakage while maintaining accuracy.

The AI security landscape is evolving rapidly, with numerous tools emerging to provide threat coverage for large language model (LLM) applications. Choosing the right tool requires careful consideration, and an evaluation rubric helps streamline the decision-making process by focusing on key security aspects.

Key Criteria for Evaluating AI Security Tools

Evaluating ML Security Operations (MLSecOps) tools requires criteria similar to traditional security solutions, with additional considerations for AI-specific risks. When assessing AI security tools, consider the following:

1. Threat Coverage – Protection against adversarial attacks, data leakage, model tampering, and supply chain risks.

2. Integration & Compatibility – Ease of integration with existing MLOps workflows, cloud infrastructures, and AI deployment pipelines.

3. Compliance & Governance – Support for industry compliance standards (e.g., GDPR, SOC 2, HIPAA) and AI model governance.

4. Ease of Use & Deployment – Simplicity of setup, user interface design, and operational overhead.

5. Scalability – The tool’s ability to support growing AI models and enterprise-wide AI security needs.

6. Open Source vs. Proprietary – Availability, licensing requirements, support, and maintenance options.

7. Enterprise Support & Community Adoption – Vendor support availability, developer engagement, and industry adoption.

GenAI Security Tools

With a clear understanding of key evaluation criteria, the next step is exploring the tools available in the market. Below are some notable GenAI security tools that address various aspects of AI security and compliance:

1. Garak

• Overview: Garak is an open-source AI security tool designed to test and evaluate language models for vulnerabilities, adversarial robustness, and ethical risks. It identifies issues such as hallucinations, data leakage, prompt injection, misinformation, and jailbreaks.

  • Free and open-source with strong community support

  • Useful for researchers and security practitioners

  • Multi-model support

• Usage/Application:

  • Security auditing of LLMs

  • Evaluating AI models against adversarial inputs

  • Strengthening AI safety and compliance

2. Lasso Security

• Overview: Lasso Security focuses on protecting AI-generated content from data leakage and ensuring compliance with regulatory frameworks.

  • Specializes in LLM security and content protection

  • Compliance-focused, ideal for regulated industries

  • Lightweight deployment for cloud-based AI applications

• Usage/Application:

  • Preventing prompt injections and data leakage in GenAI applications

  • AI-driven compliance monitoring

  • Securing AI chatbots and LLM-based systems

3. Protect AI

• Overview: Protect AI provides a robust security platform for machine learning (ML) models, addressing threats such as adversarial attacks and supply chain vulnerabilities. It emphasizes security observability in AI environments.

  • Comprehensive security platform covering multiple AI attack vectors

  • Strong visibility into model vulnerabilities

  • Designed for seamless MLOps integration

• Usage/Application:

  • Continuous monitoring of AI/ML security threats

  • Model vulnerability assessments

  • AI supply chain security

4. LLM Guard

• Overview: An open-source solution from ProtectAI, LLM Guard is designed to secure large language models (LLMs) against threats such as prompt injection, data exfiltration, and model manipulation.

  • Specializes in protecting LLMs from unique security threats

  • Effective for chatbot applications and GenAI-based platforms

  • Helps enforce ethical AI use

• Usage/Application:

  • Securing LLM-driven applications against adversarial prompts

  • Preventing sensitive data leaks in AI chatbots

  • Implementing content filtering for responsible AI deployment

5. PromptFoo.dev

• Overview: PromptFoo.dev is a security and evaluation platform for prompt engineering, helping developers test, validate, and secure AI-generated responses.

  • Enables secure prompt engineering for LLM application

  • Helps developers refine and harden AI interactions

  • Open-source and developer-friendly

• Usage/Application:

  • Testing prompt effectiveness and security

  • Identifying weaknesses in AI-generated content

  • Enhancing LLM application robustness against prompt-based attacks

6. AIRA Security

• Overview: AIRA Security provides continuous monitoring and real-time security guardrails for AI systems. Its platform integrates with MLOps pipelines to ensure AI models remain secure throughout their lifecycle.

  • Real time scanning

  • Multi modal model support

• Usage/Application:

  • Monitor Model Interaction

  • Customizable guardrails

  • Data leakage prevention

Final Thoughts

Securing AI-integrated systems is an ongoing challenge that requires dedicated tools to address different threat vectors. Whether protecting LLMs, securing AI-generated content, or ensuring compliance, solutions like AIRA Security, Protect AI, and Lasso Security provide tailored approaches. Open-source tools like Garak,, and PromptFoo.dev, and LLM Gaurd also offer accessible security capabilities for those looking to enhance AI safety on a budget.