Aikido Security

AI Model Hallucinations Fuel Slopsquatting Attacks on PyPI and npm Registries

Researchers identified 139 package names consistently hallucinated by five different AI models across Python and JavaScript ecosystems. Seven of these names are already registered on PyPI and npm, including one previously used to distribute malware. The attack vector, termed slopsquatting, allows attackers to register AI-suggested package names and execute code with developer privileges during installation. One package, metro-evaluator, contained malicious code removed by npm in December 2025, while another empty package css-color-stop began receiving downloads after the list was published. Real projects such as odf and lusid now occupy names that AI models recommend, causing developers to install unrelated software. Studies show hallucination rates between 4.62% and 21.7% depending on the model, with commercial models performing better than open-source ones. The findings highlight risks when AI coding agents execute dependency installation commands without human verification.

AI-Powered Pentests Deliver Full Attack Chains Unlike Basic Vulnerability Scans

A new generation of AI-driven offensive testing tools is emerging that goes far beyond traditional vulnerability scanners. These AI agents perform reconnaissance, enumeration, business logic analysis, exploitation, and validation in a continuous adaptive loop. The result is not a long list of unconfirmed findings but validated vulnerabilities accompanied by technical descriptions, business impact, risk ratings, and working proof-of-concept evidence. True AI pentesting requires specialized agent architectures, memory, planning modules, and proprietary offensive tooling rather than generic prompts connected to existing scanners. In Brazil, HackerSec has built such a system with its Yaga agent, while XBOW and Aikido Security are recognized internationally. The technology is positioned to complement and eventually transform manual penetration testing practices.