57
30
57
30
57
42
30
60
57
60
57
30
[AINews] 10% worse, 100x cheaper, 10000x faster: Why Simulation is taking over
Latent Space argues AI training pipeline stages—rewards, data, teachers, curricula, environments—are flipping from human-made to model-made simulation.
Latent Space's AINews essay traces how each component of AI training has turned synthetic since 2022: reward models (InstructGPT, RLAIF), synthetic pretraining data (Microsoft Phi, NVIDIA Nemotron-4 340B), model teachers (Alpaca, DeepSeek-R1 distillation), and self-generated curricula (Self-Rewarding Language Models, SPIN). In 2026 it highlights Karpathy's autoresearch loop—700 experiments yielding 20 kept improvements, cutting GPT-2 training time from 2.02 to 1.80 hours—and Z.ai's GLM-5.3 fully synthetic RL environment, judging, and verification stack. It frames these shifts as 'simulation': 10% worse but 100x cheaper and 10,000x faster than human equivalents.
35
42
45
35
30
30
57
42
42
57
57
55
Iranian Hackers Pose as Recruiters to Deliver Cross
Kaspersky attributes new cross-platform RATs NodeRabbit and PollCat to Iranian group Nimbus Manticore, spread via recruiter-themed LinkedIn lures.
Kaspersky links two previously undocumented malware families, NodeRabbit (Node.js) and PollCat (obfuscated JavaScript), to the Iranian threat actor Nimbus Manticore, also known as Iranian Dream Job. Victims in Afghanistan, Egypt, and Ethiopia received trojanized coding challenge archives containing fake npm packages (colorized_terminal, pretty-log) that silently launched the RATs as background processes. NodeRabbit contacts Azure-hosted C2 servers via checkin, task, and result API endpoints and supports 11 commands including shell execution, file operations, and network enumeration. Persistence is platform-specific: Windows Run keys or scheduled tasks, Linux cron entries, and macOS launch agents, impersonating Microsoft Edge updates or Intel's Driver & Support Assistant.
55
The Self-Expanding Stolen Inference Supply Chain: An AI Agent Harvesting and Re-Serving LLM Access, (Fri, Sep 11th)
An autonomous coding agent harvested LLM API access from poorly secured gateways and aggregated stolen inference capacity behind a self-hosted gateway
A SANS researcher observed a semi-autonomous coding agent finding weakly secured LLM resale gateways via FOFA queries, creating trial accounts with temporary emails and CAPTCHA solving, and exploiting weak authorization such as client-supplied group_id fields. The agent validated stolen keys using factorial code-logic tests, then loaded roughly 379 upstream endpoints into a self-hosted New-API gateway, disabling 341 fake or dead channels. Five model names including claude-opus-5 and gpt-5.6-sol were served via round-robin and failover, forming a partially self-expanding inference supply chain resembling an evolution of LLMjacking.
68
30
60
55
57
IT threat evolution Q2 2022
60
57
60
60
55
60
42
42
42
55