The Nx compromise abused installed AI coding CLIs.
Week of Aug 25–31, 2025
What changed in what we know
Anthropic reports residual success after mitigations in its browser agent.
New findings
Attacks & incidents
Anthropic's August 2025 threat intelligence report describes a criminal who used Claude Code to automate reconnaissance, credential harvesting and network intrusion against at least 17 organizations, including healthcare, emergency services, government and religious institutions, then threatened to publish the stolen data. The report also describes North Korean operatives using Claude to obtain and keep remote technical jobs, and a low-skill actor selling ransomware developed with Claude.
Attackers exploited a GitHub Actions workflow injection to steal Nx's npm token and publish malicious versions whose install script scanned systems for secrets, attempted to use locally installed AI CLIs such as Claude and Gemini to assist, and uploaded results to public GitHub repositories. Nx reports the packages were live about four hours and has since moved to trusted publishing and mandatory 2FA approval.
ESET Research reported PromptLock, ransomware samples uploaded to VirusTotal that use a locally run open-weight model to generate scripts for file discovery, exfiltration and encryption at runtime, and called it the first known AI-powered ransomware. In a September 3, 2025 update, ESET said the authors of an academic study had contacted it and that their research prototype closely resembles the samples, supporting ESET's view that PromptLock was a proof of concept rather than malware used in attacks.
Defense & research
Announcing a limited pilot of Claude in Chrome, Anthropic reports red-teaming with 123 test cases across 29 attack scenarios. Attack success in autonomous mode was 23.6% without new mitigations and 11.2% with them; on a separate set of browser-specific attacks, mitigations reduced success from 35.7% to 0%.
A systematic review in ACM Computing Surveys covers autonomous blue- and red-team agents and cyber operations environments, and proposes criteria for judging whether autonomous network defence is ready for real deployment. It identifies gaps in explainability, continual learning under evolving threats, and realistic training environments.