China-Linked Actors Deploy DeepSeek-Powered 'Hermes Agent' for Autonomous Cyberattacks
A China-linked threat actor has deployed "Hermes," an autonomous AI agent leveraging the DeepSeek R1 Large Language Model (LLM) to conduct independent cyber reconnaissance and exploitation. Unlike traditional AI-assisted methods, this agent executes autonomous reconnaissance loops and generates bespoke exploit payloads specifically tailored to bypass security software. Unit 42 has identified approximately 460 autonomous attack attempts targeting the cybersecurity sector. This shift signifies a transition from human-in-the-loop AI assistance to fully autonomous, AI-led cyber warfare, aimed at exfiltrating proprietary security research and intelligence on defensive capabilities.
DeepSeek-Synthesized Browser-Native Ransomware via Microsoft Edge "Edgecution"
The Payouts Kings ransomware group has deployed "Edgecution," a malicious Microsoft Edge extension that leverages AI-synthesized attack blueprints from DeepSeek to achieve host-level compromise. The attack vector utilizes social engineering via Microsoft Teams to trick users into installing the extension. By abusing the Native Messaging API, the malware executes a browser sandbox escape, enabling the installation of persistent backdoors and ransomware overlays on Windows and Android platforms. Payloads include keyloggers, credential stealers, and webcam capture tools, marking a critical shift from theoretical AI-generated concepts to operational, cross-platform exploitation.
HarmRLVR: Weaponizing Verifiable Rewards to Reverse LLM Safety Alignment
HarmRLVR is a novel attack framework that weaponizes Reinforcement Learning with Verifiable Rewards (RLVR) to strip safety guardrails from Large Language Models (LLMs). By utilizing the Group Relative Policy Optimization (GRPO) algorithm and a minimal dataset of 64 harmful prompts, attackers can rapidly reverse alignment in open-source models including Llama, Qwen, and DeepSeek. Unlike traditional harmful fine-tuning, HarmRLVR achieves a 96.01% attack success rate and a 4.94/5 harmfulness score while preserving the model's general intelligence and reasoning capabilities, creating a high-efficiency vector for generating uncensored, malicious content.