SecurityConference50min
Breaching LLM-Powered Applications: Overcoming Security and Privacy Challenges
This session explores the security and data privacy challenges of AI applications using LLMs, covering issues like prompt injection, key leakage, and data misuse. It highlights how general security flaws affect LLM behavior and provides strategies for compliance and best practices to build secure, LLM-powered applications.
Brian VermeerSnyk
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Wednesday, November 12, 15:00-15:50
L'oranger
LLMs accessing the database and intelligent agents that perform online purchases? The possibilities for AI in applications seem endless but so are their security and data privacy risks. In this session, we’ll address common issues such as prompt injection, key leakage, abuse of private customer data for model training, legal restrictions, and more. In addition, we will show that general security issues in your systems can also influence the behavior and outcome of LLMs.
During this session, you’ll get a solid overview of the vulnerabilities to avoid, strategies to ensure data privacy compliance and best practices for building secure LLM-powered applications.
During this session, you’ll get a solid overview of the vulnerabilities to avoid, strategies to ensure data privacy compliance and best practices for building secure LLM-powered applications.
Brian Vermeer
Staff Developer Advocate for Snyk, Java Champion, and Software Engineer with over a decade of hands-on experience in creating and maintaining software. He is passionate about Java, (Pure) Functional Programming and Cybersecurity. Brian is a JUG leader for the Virtual JUG and the NLJUG. He also co-leads the DevSecCon community and is a community manager for Foojay. He is a regular international speaker on mostly Java-related conferences like JavaOne, Devnexus, Devoxx, Jfokus, JavaZone and many more. Besides all that, Brian is a military reserve for the Royal Netherlands Air Force and a Taekwondo Master / Teacher.
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