A lot of AI security boils down to the boring, but important, software security topics that appsec teams have been dealing with for decades. Niv Braun explains the distinctions between AI-related and AI-specific security as we avoid the FUD and hype of genAI to figure out where appsec teams can invest their time. He notes that data scientists have been working with ML and sensitive data sets for a long time, and it's good to have more scrutiny on what controls should be present to protect that data.
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An open source security project forks in response to license changes (and an echo of how we've been here before), car hacking via spectacularly insecure web apps, hacking a synth via spectacularly cool MIDI messages, cookie parsing problems, the RANsacked paper of 100+ LTE/5G vulns found from fuzzing, and more!
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Show Notes: https://securityweekly.com/asw-315
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