As large language models revolutionize business operations, sophisticated attackers exploit AI systems through prompt injection, jailbreaking, and content manipulation—vulnerabilities that traditional security tools cannot detect. This intensive course empowers AI developers, cybersecurity professionals, and IT managers to systematically identify and mitigate LLM-specific threats before deployment. Master red-teaming methodologies using industry-standard tools like PyRIT, NVIDIA Garak, and Promptfoo to uncover hidden vulnerabilities through adversarial testing. Learn to design and implement multi-layered content-safety filters that block sophisticated bypass attempts while maintaining system functionality. Through hands-on labs, you'll establish resilience baselines, implement continuous monitoring systems, and create adaptive defenses that strengthen over time.
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Recommended experience
What you'll learn
Design red-teaming scenarios to identify vulnerabilities and attack vectors in large language models using structured adversarial testing.
Implement content-safety filters to detect and mitigate harmful outputs while maintaining model performance and user experience.
Evaluate and enhance LLM resilience by analyzing adversarial inputs and developing defense strategies to strengthen overall AI system security.
Skills you'll gain
- Prompt Engineering
- AI Security
- Scenario Testing
- Penetration Testing
- LLM Application
- Threat Modeling
- Vulnerability Scanning
- Cyber Security Assessment
- Security Controls
- Continuous Monitoring
- Security Strategy
- Vulnerability Assessments
- Responsible AI
- Large Language Modeling
- Security Testing
- AI Personalization
- System Implementation
Details to know
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December 2025
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There are 3 modules in this course
This module introduces participants to the systematic creation and execution of red-teaming scenarios targeting large language models. Students learn to identify common vulnerability categories including prompt injection, jailbreaking, and data extraction attacks. The module demonstrates how to design realistic adversarial scenarios that mirror real-world attack patterns, using structured methodologies to probe LLM weaknesses. Hands-on demonstrations show how red-teamers simulate malicious user behavior to uncover security gaps before deployment.
What's included
4 videos2 readings1 peer review
This module covers the design, implementation, and evaluation of content-safety filters for LLM applications. Participants explore multi-layered defense strategies including input sanitization, output filtering, and behavioral monitoring systems. The module demonstrates how to configure safety mechanisms that balance security with functionality, and shows practical testing methods to validate filter effectiveness against sophisticated bypass attempts. Real-world examples illustrate the challenges of maintaining robust content filtering while preserving user experience.
What's included
3 videos1 reading1 peer review
This module focuses on comprehensive resilience testing and systematic improvement of AI system robustness. Students learn to conduct thorough security assessments that measure LLM resistance to adversarial inputs, evaluate defense mechanism effectiveness, and identify areas for improvement. The module demonstrates how to establish baseline security metrics, implement iterative hardening processes, and validate improvements through continuous testing. Participants gain skills in developing robust AI systems that maintain integrity under real-world adversarial conditions.
What's included
4 videos1 reading1 assignment2 peer reviews
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