Jason Kramer
ObjectSecurity, Senior Software Engineering Researcher
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Jason is dedicated to advancing the state of the art in secure and robust AI. With a bachelor’s degree in computer science from San Diego State University, he is focused on ensuring trust, security, privacy, bias, and robustness of AI/ML models. Jason has led the development efforts of a commercial solution for the detection and repair of vulnerabilities in deep learning systems, and the co-author of multiple patents related to the cybersecurity of systems including AI/ML, embedded devices, supply chain, and others. His passion for improving the field has driven him to push the boundaries of what is possible and make a meaningful impact in the fields of AI and cybersecurity.
Behind the Binaries: Cracking Compiled AI for Vulnerabilities
This presentation explores the risks and techniques involved in reverse engineering AI models, focusing on how attackers can extract and exploit AI models for adversarial attacks. We’ll cover vulnerabilities in popular model formats like ONNX and TFLite, as well as the challenges of reversing more complex models compiled with systems like TVM and Glow, emphasizing the need for stronger AI security practices.
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