Securing LLMs Online Course

Securing LLMs Online Course

The Securing LLMs Online Course is a hands-on course designed to teach you how to secure your LLM applications against the most critical vulnerabilities. Hosted by cybersecurity specialist Clint Bodungen, this course focuses on practical, real-world methods to protect enterprise-grade LLMs from the OWASP Top 10 risks. Learn to attack vectors specific to LLMs and learn how to safeguard your applications through detailed, actionable steps. The course covers vital security topics such as securing the supply chain, preventing data poisoning, and blocking malicious inputs and outputs. You'll also learn to implement advanced techniques like prompt engineering to create secure boundaries for your models. Interactive examples, demos, and sample code ensure that you walk away with the skills to protect your LLM systems effectively.

Who should take the course?

• Developers who are building and deploying LLM-based applications and need to secure them from cybersecurity risks.
• Data Scientists working with generative models, looking to protect their training data and model integrity.
• Security Professionals interested in strengthening their understanding of LLM-specific security threats and mitigation strategies.

What you will learn?

• Supply Chain Protection: Strategies to protect against vulnerabilities in third-party libraries, models, and plugins.
• Data Security: Methods to safeguard data from poisoning attacks, unauthorized access, and theft.
• Input/Output Filtering: Learn how to filter harmful user inputs and sanitize the outputs generated by models.
• Preventing Jailbreaking: Techniques to stop unauthorized modifications and misuse of LLMs.
• Automating Security: Discover tools and frameworks to automate security processes and safeguard your LLM systems.

Knowledge Gained

• Insights into the OWASP Top 10 risks specific to LLMs.
• A comprehensive understanding of the unique attack vectors and threats that face generative models.
• Practical knowledge of advanced security techniques to protect LLM applications.

Skills Acquired

Security Analysis for LLMs

• Understanding key risks and vulnerabilities in the LLM ecosystem.
• Applying targeted protection against third-party code and libraries.

Data Protection Strategies

• Techniques to protect sensitive training data from poisoning and theft.
• Methods to prevent unauthorized access to LLMs.

Input and Output Validation

• Filtering malicious user inputs to prevent exploitation.
• Ensuring that model outputs are safe and sanitized.

Jailbreaking Prevention

• Tools and tactics to block jailbreaking and ensure models stay secure.

Security Automation

• Using automated frameworks and tools to implement and maintain robust security mechanisms.

Key Benefits

• Learn hands-on techniques to protect your LLM applications from the OWASP Top 10 risks.
• Master strategies to secure LLMs from unique attack vectors specific to generative models.
• Work with real-world examples and sample code to deepen your understanding.
• Gain practical experience in implementing prompt engineering for secure operational boundaries.
• Acquire the skills needed to proactively monitor, secure, and maintain LLM systems in production.

Course Outline

The Securing LLMs Online Course covers the following topics - 

Module 1. LLM Security Fundamentals: Addressing OWASP's Top 10 Risks

• Overview of security challenges in LLM applications.
• Breakdown of OWASP's Top 10 risks and their relevance to LLMs.
• Securing the supply chain: Best practices for third-party models and libraries.

Module 2. Protecting Your LLM Data

• Methods for safeguarding data against unauthorized access and poisoning attacks.
• Ensuring data integrity and privacy in training datasets.

Module 3. Input/Output Security Measures

• Techniques to filter malicious user inputs and sanitize model outputs.
• Tools for ensuring data security throughout the LLM pipeline.

Module 4. Blocking Jailbreaking and Misuse

• Methods to prevent unauthorized alterations or exploitation of LLMs.
• Implementing strong safeguards to prevent misuse.

Module 5. Automating Security in LLMs

• Leveraging frameworks and tools to automate LLM security processes.
• Proactive monitoring and continuous improvement of LLM security.

Module 6. Hands-On Case Studies and Demos

• Real-world scenarios demonstrating common security vulnerabilities and solutions.
• Step-by-step walkthroughs of securing LLMs from attack vectors.

Module 7. Prompt Engineering for Security

• Using prompt engineering as a mechanism to secure your LLM applications.
• Configuring guardrails to prevent exploitation.

Module 8. Final Thoughts and Best Practices

• Essential tips for maintaining a secure LLM environment in production.
• Best practices for ongoing monitoring and updates.