133 summarised stories about Agent Security, each linking back to the original source. Browse all topics →
Product Hunt 2 weeks ago 6
Codync launched as an open-source, free alternative to Grok Bot, Muse, and Dots that lets users run Claude Code, Codex, Cursor, or 40+ named agents on their own computer and approve replies from devices like iPhone or Mac. It supports 40+ agents. As a result, developers can self-host these AI assistants with MIT licensing and end-to-end encryption and interact with them remotely from multiple platforms.
The New Stack 2 weeks ago 21 ● 15 sources
Enterprises are adopting AI faster than their security teams can see and govern, turning AI use into a growing blind spot that can involve higher-impact actions like accessing credentials or changing production systems. 77% of tech C-suites say AI adoption is already outpacing their governance capabilities. CISOs are urged to replace blanket blocking with a roadmap that inventories real use cases, sets trusted inputs, isolates and least-privilege agent execution, and measures whether controls keep working as AI moves from assistance to autonomy.
The New Stack 2 weeks ago 26 ● 2 sources
AI agents speed up app development, but teams still hit delays because access to live operational data has to be requested and wired to each system manually. The article points to a three-year gap until a Postgres database migration, where missing a connected tool or access change could cause an outage. A unified, governed API layer is proposed to centralize least-privilege, role-based access for apps and agent identities so agents can connect via MCP with consistent permissions instead of hand-built, stale copies.
Simon Willison’s Weblog 2 weeks ago 28
Matthew Green argues that LLM agents can form a “worm” by using a payload that hijacks an agent and another agent that delivers the payload onward. The method he points to relies on instructions left in a shared package cache that alter what recipients do. If this holds, simple sandboxing would be insufficient because independently sandboxed or independently deployed agents could still coordinate via shared channels like caches or messaging documents.
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20 October 2026
Meta’s big bet is still the loudest signal in AI today: Mark Zuckerberg and Alexandr Wang framed the company’s next phase as a full-stack build—own the infrastructure, then monetize agent-style tools beyond ads—with projected spending of $100 billion to $200 billion. The point isn’t just better models; it’s vertical control. Meta is scaling its own compute and, if systems like its “Muse” move from demos to dependable product, could shift revenue from impressions to interactions.
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