Pentesting GenAI LLM models: Securing Large Language Models Course includes 4.5 hrs video content and enrolled by 1K+ students and received a 4.5 average review out of 5. Now, you will get 100%oFF on the original price of the course and discount price differs from country to country, and the course provider offers 30-days money-back guarantee! If you are not satisfied in any way, youâll get your money back.
What you’ll learn
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Understand the unique vulnerabilities of large language models (LLMs) in real-world applications.
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Explore key penetration testing concepts and how they apply to generative AI systems.
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Master the red teaming process for LLMs using hands-on techniques and real attack simulations.
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Analyze why traditional benchmarks fall short in GenAI security and learn better evaluation methods.
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Dive into core vulnerabilities such as prompt injection, hallucinations, biased responses, and more.
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Use the MITRE ATT&CK framework to map out adversarial tactics targeting LLMs.
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Identify and mitigate model-specific threats like excessive agency, model theft, and insecure output handling.
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Conduct and report on exploitation findings for LLM-based applications.
Can I download Pentesting GenAI LLM models: Securing Large Language Models course?
You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The Pentesting GenAI LLM models: Securing Large Language Models course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.Are there any other coupons available for this course?
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Disclosure: This post may contain affiliate links and we may get small commission if you make a purchase. Read more about Affiliate disclosure here.
