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Veil#Drop is a multi‑stage PowerShell‑based framework that starts from a JavaScript file disguised as a document, then uses Windows Script Host to launch PowerShell with execution policy bypassed and pull further stages from attacker‑controlled Blogspot pages. Payloads are XOR‑encoded, decoded only in memory, and reconstructed into .NET assemblies that are loaded via reflection without touching disk. The campaign relies on compromised websites, document‑style multi‑extension filenames, LOLBIN‑based execution fallbacks, decoy content, cleanup of the initial launcher, and finally deploys PureLog Stealer to grab browser credentials, cookies, crypto wallets, and other user data.

Anthropic's new research paper introduces the concept of "J-space" in language models, revealed as internal neural patterns playing a unique role in Claude's processing. Emerging during training without explicit design, the J-space allows Claude to internally reason, modulate thoughts, and solve multi-step problems, distinguishing them from automatic processes. These patterns enable the monitoring of internal thoughts for AI misbehavior and provide insights into AI consciousness, suggesting a tangible distinction between deliberate and automatic decisions.

A small, cost-effective LLM was implemented to prune retrieved context chunks for question-answering systems in order to improve efficiency, successfully discarding 68% of unnecessary chunks while maintaining 96% recall. This method addresses the traditional challenge of balancing cost and recall in complex knowledge bases, where irrelevant context can increase expenses without aiding the response accuracy.