Ask These 5 AI Cybersecurity Questions for a More Secure Approach to Adversarial Machine Learning

A glossary of adversarial machine learning terms that distinguishes AI from ML, explains supervised and unsupervised learning paradigms, and equips security teams for conversations about LLM risks.

A CISO audience poll found 82% of organizations use or plan to use AI, yet 91% are unsure of their training data origins. The article outlines AI security testing priorities for leaders adopting LLMs.

A pentest shows Claude refuses to build an LSASS dumper, but local DeepSeek and uncensored Qwen models produce a working XOR-encrypted minidumper, making AI-assisted EDR evasion nearly effortless.

SpecterOps maps where AI helps offensive security (recon, tooling, vuln discovery) and where it still fails, stressing that results hinge on the harness: context, tools, state, independent validation, and human judgment.

How Project Zero evolved Project Naptime into Big Sleep, using large language models to find vulnerabilities in real-world software, with early results and observations.

Project Naptime evaluates the offensive security capabilities of large language models, presenting a methodology, tooling setup, and early successes finding real vulnerabilities.