From Naptime to Big Sleep: Using Large Language Models To Catch Vulnerabilities In Real-World Code

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

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.

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.

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…

LLMs find vulnerabilities fast but flood teams with unverified reports. Neo confirmed 22 CVEs across 13 open source projects at ~10-20% false positives, with working exploits and runtime evidence.

First part of Project Zero's Pixel 9 zero-click chain series, examining the Dolby audio decoder attack surface reachable from remote media input and developing a potential RCE exploit.