Hands-on course on LLM security: learn prompt injection, jailbreaks, adversarial attacks, and defensive controls
β±οΈ Length: 1.3 total hours
β 4.40/5 rating
π₯ 665 students
π November 2025 update
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- Course Overview
- Explore the rapidly evolving field of AI security, specifically targeting the unique vulnerabilities and complex attack surfaces of Large Language Models (LLMs).
- Understand the critical role of proactive AI red teaming in identifying, simulating, and mitigating sophisticated LLM vulnerabilities before they can be exploited in real-world scenarios.
- Adopt the mindset of an adversarial attacker, learning to creatively pinpoint weaknesses, potential data exfiltration paths, and unintended behaviors within intricate AI architectures.
- Gain foundational knowledge in LLM threat modeling, recognizing common attack surfaces, understanding the potential impact of successful breaches, and analyzing the unique security paradigms of generative AI.
- Empower yourself to contribute significantly to the development of robust, secure, and trustworthy AI systems by mastering ethical hacking techniques against them.
- Navigate crucial ethical considerations inherent in AI hacking and security research, focusing on responsible disclosure practices and the overarching goal of improving overall AI safety and reliability.
- Become a vital safeguard for the future of artificial intelligence, equipped to defend against a dynamic and increasingly sophisticated landscape of cyber threats.
- Requirements / Prerequisites
- Basic Technical Familiarity: Comfort with general computing environments, command-line interfaces, and standard software installation processes will be highly beneficial for navigating the practical lab setups.
- Fundamental IT Concepts: Familiarity with basic networking principles, file systems, data encoding/decoding, and general cybersecurity concepts can significantly aid in grasping advanced attack vectors.
- Keen Interest in AI & Cybersecurity: A strong, self-driven interest in both artificial intelligence technologies and information security is paramount, fostering an engaging and rewarding learning experience.
- No Prior Hacking Experience Required: This course is meticulously designed to introduce ethical hacking concepts from the ground up, specifically tailored for the unique domain of LLMs, making it accessible to motivated beginners.
- Standard Computing Environment: Access to a personal computer with sufficient processing power and memory (e.g., 16GB RAM recommended, 32GB preferred for running multiple local LLMs concurrently) for virtualization and heavy computations.
- Stable Internet Connection: Essential for downloading comprehensive course materials, setting up cloud-based lab environments, and accessing external LLM resources or critical updates.
- Skills Covered / Tools Used
- Advanced Adversarial Thinking: Develop a strategic and systematic mindset for identifying, simulating, and exploiting vulnerabilities in complex AI systems, moving beyond superficial testing.
- LLM Penetration Testing Methodologies: Learn structured approaches for systematically assessing the security posture of language models, differentiating from traditional application penetration testing.
- AI Vulnerability Assessment: Master techniques for uncovering hidden weaknesses, unintended biases, critical misconfigurations, and inherent design flaws within LLM deployments.
- Ethical Hacking Principles in AI: Apply a robust ethical framework to all security testing activities, ensuring integrity, compliance, and contributing positively to global AI safety standards.
- Sophisticated Data Exfiltration & Evasion: Explore advanced methods for extracting sensitive information, model parameters, and proprietary data, while bypassing sophisticated AI-driven defense mechanisms.
- Containerization Technologies (Docker): Gain practical, hands-on experience with Docker for setting up reproducible, isolated, and scalable hacking environments tailored specifically for LLM security research.
- Cloud AI Services (Azure OpenAI): Interact with and red team enterprise-grade LLM platforms to understand real-world security implications and develop effective testing strategies against production systems.
- Open-Source LLM Deployment & Hardening: Acquire practical skills in deploying, configuring, and conducting adversarial testing against various open-source language models for specialized, unrestricted scenarios.
- Exploitative Prompt Engineering: Learn highly advanced prompt crafting techniques specifically engineered to elicit unintended behaviors, initiate data leaks, and achieve control circumvention from LLMs.
- Security Control Bypass & Adaptive Attacks: Understand the intricate strategies for circumventing active guardrails, content filters, and other defensive measures implemented within LLMs, adapting your attack vectors dynamically.
- Benefits / Outcomes
- Become an In-Demand AI Security Specialist: Equip yourself with the niche, highly sought-after skills crucial for securing the next generation of artificial intelligence technologies.
- Significantly Enhance Career Prospects: Position yourself as a valuable and indispensable asset to organizations developing, deploying, or extensively utilizing AI, demonstrating unparalleled expertise in safeguarding these critical systems.
- Actively Contribute to Safer AI Development: Play a direct and impactful role in making artificial intelligence more secure, trustworthy, and resistant to malicious attacks, thereby fostering its responsible global adoption.
- Develop a Proactive Security Stance: Shift your expertise from reactive problem-solving to proactive vulnerability identification, enabling the construction of inherently more resilient and secure AI systems from the ground up.
- Master Practical, Highly Relevant Skills: Gain hands-on experience with cutting-edge tools and sophisticated techniques directly applicable to real-world AI security challenges faced by enterprises today.
- Deepen Understanding of LLM Attack Surfaces: Develop a comprehensive and nuanced understanding of the unique vulnerabilities inherent in large language models and the most effective strategies to defend against them.
- Build a Robust AI Red Teaming Toolkit: Establish and maintain your own professional-grade laboratory environment for continuous learning, independent research, and advanced experimentation in AI security.
- Network and Collaborate with AI Security Peers: The shared, specialized experience gained in this course can open significant doors to professional connections and collaborative opportunities within the nascent but rapidly growing AI security community.
- PROS
- Highly Practical and Lab-Focused Approach: Emphasizes direct application and extensive hands-on experience, which is paramount for mastering complex AI security concepts effectively.
- Cutting-Edge and Highly Relevant Content: Addresses the very latest vulnerabilities, red teaming strategies, and defensive tactics specifically tailored for advanced Large Language Models.
- Strong Ethical Hacking Perspective: Instills responsible security practices, focusing on improving overall system integrity, robustness, and trust rather than destructive actions.
- Direct Real-World Applicability: Directly tackles pressing security issues faced by organizations deploying AI, preparing learners for immediate and impactful contributions.
- Empowers Proactive Security Mindset: Teaches participants how to anticipate, simulate, and prevent threats, moving beyond traditional reactive defense mechanisms to truly harden AI systems.
- Cultivates Niche and In-Demand Skillset: Develops a specialized and highly sought-after skillset in the burgeoning and critical domain of AI security.
- CONS
- Potentially Limited Depth for Comprehensive Skill Mastery: Given the extensive range of advanced topics covered, the advertised total course length of 1.3 hours might necessitate significant further independent study and deeper practical engagement to fully internalize all sophisticated techniques and concepts presented.
Learning Tracks: English,Business,Operations
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