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.
Profitez d'une croissance illimitée avec un an de Coursera Plus pour 199 $ (régulièrement 399 $). Économisez maintenant.
Expérience recommandée
Ce que vous apprendrez
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.
Compétences que vous acquerrez
- Catégorie : Threat Modeling
- Catégorie : LLM Application
- Catégorie : Security Testing
- Catégorie : AI Personalization
- Catégorie : Responsible AI
- Catégorie : Continuous Monitoring
- Catégorie : Vulnerability Scanning
- Catégorie : AI Security
- Catégorie : System Implementation
- Catégorie : Cyber Security Assessment
- Catégorie : Security Strategy
- Catégorie : Scenario Testing
- Catégorie : Prompt Engineering
- Catégorie : Vulnerability Assessments
- Catégorie : Security Controls
- Catégorie : Penetration Testing
- Catégorie : Large Language Modeling
Détails à connaître
Ajouter à votre profil LinkedIn
décembre 2025
Découvrez comment les employés des entreprises prestigieuses maîtrisent des compétences recherchées
Il y a 3 modules dans ce cours
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.
Inclus
4 vidéos2 lectures1 évaluation par les pairs
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.
Inclus
3 vidéos1 lecture1 évaluation par les pairs
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.
Inclus
4 vidéos1 lecture1 devoir2 évaluations par les pairs
Offert par
En savoir plus sur Computer Security and Networks
Statut : Essai gratuit
Pearson
Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?
Foire Aux Questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Plus de questions
Aide financière disponible,
¹ Certains travaux de ce cours sont notés par l'IA. Pour ces travaux, vos Données internes seront utilisées conformément à Notification de confidentialité de Coursera.