According to Anthropic (@AnthropicAI), the company has launched a new set of complex evaluation protocols to assess AI models' sabotage and sabotage-monitoring capabilities. As AI models evolve with greater agentic abilities, Anthropic emphasizes the necessity for smarter monitoring tools to ensure AI safety and reliability. These evaluations are specifically designed to detect and mitigate potential sabotage risks, providing businesses and developers with practical frameworks to test and secure advanced models. This move addresses growing industry concerns about the trustworthiness and risk management of next-generation AI systems (Source: AnthropicAI Twitter, June 16, 2025).

According to Andrej Karpathy, the security risk is highest when running local LLM agents such as Cursor or Claude Code, as these models have direct access to local files and infrastructure, posing significant security and privacy challenges for AI-driven businesses (source: @karpathy, June 16, 2025). In contrast, interacting with LLMs via web platforms like ChatGPT generally presents lower risk unless advanced features such as Connectors are enabled, which can extend access or permissions. For AI industry leaders, this highlights the importance of implementing strict access controls, robust infrastructure monitoring, and secure connector management when deploying local AI agents for code generation, automation, or workflow integration. Addressing these risks is essential for organizations adopting generative AI tools in enterprise environments.