Name: Pentesting GenAI LLM models: Securing Large Language Models
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What You'll Learn

Understand the unique vulnerabilities of large language models (LLMs) in real-world applications.
Explore key penetration testing concepts and how they apply to generative AI systems.
Master the red teaming process for LLMs using hands-on techniques and real attack simulations.
Analyze why traditional benchmarks fall short in GenAI security and learn better evaluation methods.
Dive into core vulnerabilities such as prompt injection, hallucinations, biased responses, and more.
Use the MITRE ATT&CK framework to map out adversarial tactics targeting LLMs.
Identify and mitigate model-specific threats like excessive agency, model theft, and insecure output handling.
Conduct and report on exploitation findings for LLM-based applications.

Requirements

Basic understanding of IT or cybersecurity Curiosity about AI systems and their real-world impact No prior knowledge of penetration testing or LLMs required

SOC Analysts, Security Engineers, and Security Architects aiming to secure LLM systems
CISO, Security Consultants, and AI Security Consultants seeking to protect AI-driven applications.
Red Team/Blue Team members and Penetration Testers exploring LLM exploitation and defense techniques.
Students and tech enthusiasts looking to gain hands-on experience in LLM penetration testing and red teaming.
Ethical Hackers and Incident Handlers wanting to develop skills in securing generative AI models.
Prompt Engineers and Machine Learning Engineers interested in securing AI models and understanding vulnerabilities in LLM-based applications.

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