Common Terminology in Adversarial Machine Learning

NetSPI offers five questions security leaders should ask about AI/ML deployments, covering business use-case, model objectives, and training data, to counter adversarial machine learning risks.

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.

Welcome to our hands-on guide where we dive into the world of Large Language Models (LLMs) and their synergy with Vector Databases. LLMs have been a game-changer in the tech world, driving innovation in application development. However, their full potential is often untapped when used in isolation. This is where Vector Databases step in, enhancing…