Johnathan Wilkes, John Anny (Palo Alto Networks)

By embracing automation, organizations can transcend manual limitations to reduce mean time to response and address exposures consistently across their cybersecurity infrastructure. In the dynamic realm of cybersecurity, swiftly addressing externally discovered exposures is paramount, as each represents a ticking time bomb. A paradigm shift towards automation to enhance speed, efficiency, and uniformity in the remediation process is needed to answer the question, "You found the exposure, now what?". Traditional manual approaches are not only time-consuming but also prone to human error, underscoring the need for a comprehensive, automated solution. Acknowledging the diversity of exposures and the array of security tools, we will propose how to remediate common external exposures, such as open ports and dangling domains. The transformative nature of this shift is crucial, particularly in the context of multiple cloud platforms with distinct data enrichment and remediation capabilities.

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K-LEAK: Towards Automating the Generation of Multi-Step Infoleak Exploits...

Zhengchuan Liang (UC Riverside), Xiaochen Zou (UC Riverside), Chengyu Song (UC Riverside), Zhiyun Qian (UC Riverside)

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Non-Disruptive Disruption: An Empirical Experience of Introducing LLMs in...

Francis Hahn (University of South Florida), Mohd Mamoon (University of Kansas), Alexandru G. Bardas (University of Kansas), Michael Collins (University of Southern California – ISI), Jaclyn Lauren Dudek (University of Kansas), Daniel Lende (University of South Florida), Xinming Ou (University of South Florida), S. Raj Rajagopalan (Resideo Technologies)

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Stacking up the LLM Risks: Applied Machine Learning Security

Dr. Gary McGraw, Berryville Institute of Machine Learning

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