Latest cybersecurity reporting from selected sources.

TuxBot v3, an AI-built IoT botnet for 17 architectures, shipped with LLM bugs and safety disclaimers the developer never removed. Palo Alto Networks’ Unit 42 identified a previously undocumented modular IoT botnet framework called TuxBot v3 Evolution, and it comes with an unusual detail: the developer used a large language model to write significant portions […]

Cybersecurity researchers have disclosed details of a previously unreported Internet-of-Things (IoT) botnet framework dubbed TuxBot v3 Evolution that shows signs of being developed with assistance from a large language model (LLM), albeit with not so successful results

The Campaign Compromises Open-Source Vulnerability to Hack IoT Devices at ScaleSecurity firm CloudSEK has uncovered a botnet campaign that is exploiting the React2Shell vulnerability in the Meta-developed, open-source React framework across a variety of devices since December. The security firm attributed the campaign to RondoDox.

The future of cybersecurity means defending everywhere. Securing IoT, cloud, and remote work requires a unified edge-to-cloud strategy. First in a three-part series.

Revised Framework to Address Emerging IoT Risks and TechnologiesThe U.S. National Institute of Standards and Technology plans to revise its Internet of Things cybersecurity framework to address evolving risks posed by emerging technologies and use cases, such as AI and immersive tech. The proposed updates will broaden the focus to entire product ecosystems.

A critical security flaw has been disclosed in the Microchip Advanced Software Framework (ASF) that, if successfully exploited, could lead to remote code execution

Industry standards would provide predictable and understandable IoT security frameworks.

A newly designed privacy-sensitive architecture aims to enable developers to create smart home apps in a manner that addresses data sharing concerns and puts users in control over their personal information.  Dubbed Peekaboo by researchers from Carnegie Mellon University, the system "leverages an in-home hub to pre-process and minimize outgoing data in a structured and enforceable manner before