AI Safety

Pro-Russia Influence Ecosystem: Drivers, Dynamics, and Tactics

This report from Google Threat Intelligence Group maps the pro-Russia influence ecosystem across six core components, revealing how four years of war have hardened its tactics, expanded the use of generative AI, and created a resilient, interconnected machine now pivoting back to global strategic objectives targeting NATO and the EU. Essential reading for anyone tracking state-backed information operations.

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GPT-5.6 Sol: OpenAI’s Next-Gen Model with Enhanced Safety and Capabilities

OpenAI's GPT-5.6 series introduces Sol, Terra, and Luna with improved coding, biology, and cybersecurity capabilities, paired with robust layered safeguards and a limited preview in coordination with the U.S. government. The model family offers new reasoning modes and competitive pricing, with Sol achieving state-of-the-art on Terminal-Bench 2.1 and matching Mythos Preview on ExploitBench at lower cost.

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AI Security After Codex and Claude Code: New Vulnerabilities and Guardrails

This episode from Gray Swan's cofounders explains why AI agents create a new class of security vulnerabilities that traditional cybersecurity cannot solve. Essential for engineers deploying Codex, Claude Code, or similar agents — prompt injection, automated red-teaming, and the lethal trifecta are must-understand concepts.

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Patch the Planet: OpenAI and Trail of Bits’ Initiative to Secure Open Source

Patch the Planet combines frontier AI models like GPT-5.5-Cyber with dedicated security engineers to find, validate, and patch vulnerabilities in open-source software. It addresses the maintainer burden by filtering false positives and developing patches, turning AI's speed into tangible security improvements for projects like cURL, Python, and the Linux kernel.

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MosaicLeaks: How Deep Research Agents Leak Private Information Through Queries

A new benchmark called MosaicLeaks reveals that deep research agents frequently leak private enterprise information through their web query logs, and training for task performance alone makes it worse. The authors propose PA-DR, a reinforcement learning method that trains agents to construct safer queries, cutting leakage by over 3x while preserving task success.

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