Pentesting GenAI LLM models: Securing Large Language Models

Master LLM Security: Penetration Testing, Red Teaming & MITRE ATT&CK for Secure Large Language Models

Description

Red Teaming & Penetration Testing for LLMs is a carefully structured course is designed for security professionals, AI developers, and ethical hackers aiming to secure generative AI applications. From foundational concepts in LLM security to advanced red teaming techniques, this course equips you with both the knowledge and actionable skills to protect LLM systems.

Throughout the course, you’ll engage with practical case studies and attack simulations, including demonstrations on prompt injection, sensitive data disclosure, hallucination handling, model denial of service, and insecure plugin behavior. You’ll also learn to use tools, processes, and frameworks like MITRE ATT&CK to assess AI application risks in a structured manner.

By the end of this course, you will be able to identify and exploit vulnerabilities in LLMs, and design mitigation and reporting strategies that align with industry standards.

Key Benefits for You:

• LLM Security Insights:
  Understand the vulnerabilities of generative AI models and learn proactive testing techniques to identify them.

• Penetration Testing Essentials:
  Master red teaming strategies, the phases of exploitation, and post-exploitation handling tailored for LLM-based applications.

• Hands-On Demos:
  Gain practical experience through real-world attack simulations, including biased output, overreliance, and information leaks.

• Framework Mastery:
  Learn to apply MITRE ATT&CK concepts with hands-on exercises that address LLM-specific threats.

• Secure AI Development:
  Enhance your skills in building resilient generative AI applications by implementing defense mechanisms like secure output handling and plugin protections.

Join us today for an exciting journey into the world of AI security—enroll now and take the first step towards becoming an expert in LLM penetration testing!

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