ZeroHour
Product

React

1 mentions in 7 days · 2 in 30 days · 2 total · first seen · last

Timeline

Hackers Use Fake LinkedIn Job Offers to Infect Developers With New Cross-Platform RATs

Iran-linked Mirage Kitten uses fake LinkedIn job challenges to deliver new cross-platform RATs NodeRabbit and PollCat to developers in fintech, aviation, and aerospace.

PolySwarm documented a campaign by Iran-linked Mirage Kitten (UNC1549) that uses fake recruiter personas and technical hiring challenges on LinkedIn to deliver the newly documented RATs NodeRabbit and PollCat, which run on Windows, Linux, and macOS. A challenge archive bundles a fake npm package (colorized_terminal 2.1.0) in node_modules that loads NodeRabbit, while a React-based challenge delivers PollCat with an attacker-controlled OTP screen. NodeRabbit persists through a malicious Visual Studio Code extension and Git post-merge/post-checkout hooks; PollCat persists via scheduled tasks, cron, and LaunchAgents. Victims were observed in fintech, aviation, and aerospace, with confirmed targets in Egypt, Ethiopia, and Afghanistan.

Cyber Security News · 6d agoMalware in the wild 2 sources

PrivEscalate: Measuring and Augmenting the Threat of LLM-Automated Linux Privilege Escalation

Researchers release PrivEscalate, a 531-scenario benchmark showing LLM agents' Linux privilege-escalation success varies by vulnerability class, plus PrivEscAgent, a domain-specialized agent that boosts success.

The paper introduces PrivEscalate, an open-source benchmark of 531 Dockerized Linux privilege-escalation scenarios spanning 14 sub-categories, plus 329 parameterized variants measuring sensitivity to environmental distractors. Evaluating six LLMs across three agent architectures shows capability is heterogeneous across vulnerability classes, sensitive to perturbation, and architecture-dependent. The authors also present PrivEscAgent, a wrapper adding deterministic enumeration, category matching, and step planning that outperforms prior privesc-agent baselines without modifying the underlying LLM. The benchmark is released to support LLM agent evaluation, defensive tool validation, and red-team training.

arXiv cs.CR · 7d agoResearch