Description

In Episode 37 of AI Security Ops, the team breaks down the most important AI security frameworks and vulnerability databases used to track risks in machine learning and large language models. The discussion covers emerging AI vulnerability databases, the OWASP Top 10 for LLMs, CVE challenges, and frameworks like MITRE ATLAS, highlighting why standardizing AI threats is still difficult. This episode is a practical guide for security professionals looking to stay ahead of AI vulnerabilities, attack techniques, and defensive resources in a fast-moving landscape.

Chapters

• (00:00) - Episode 37 – AI Frameworks & Databases
• (01:39) - A.I. vulnerability tracking is still young
• (02:44) - Should A.I. get its own vulnerability database?
• (07:33) - The benefit of multiple vulnerability databases
• (15:58) - The what is the definition of a vulnerability?
• (17:54) - Final Thoughts

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