Automation Security | Breaking Cybersecurity News | The Hacker News

In March 2026, an internal AI agent at Meta triggered a “Sev 1” incident after sensitive company and user data was exposed to employees who weren’t authorized to access it.  The incident began when a Meta employee posted a technical question on an internal forum. An engineer used an approved AI agent to analyze it, but the agent posted its response publicly without approval. The employee followed its advice, inadvertently making a large volume of sensitive data available to unauthorized engineers for over two hours.  This was not shadow AI. The tool was approved, but the AI behaved in ways nobody had anticipated.  It’s a perfect example of security’s next big AI governance problem: shady AI.  Shadow AI is the unapproved use of AI tools  Shady AI is when employees use approved AI tools in unapproved, unexpected, or poorly governed ways  Shadow AI happens outside the organization's visibility. Shady AI happens inside it. And that makes it mu...

AI agents are no longer just writing code. They are executing it. Tools like Copilot, Claude Code, and Codex can now build, test, and deploy software end-to-end in minutes. That speed is reshaping engineering—but it’s also creating a security gap most teams don’t see until something breaks. Behind every agentic workflow sits a layer few organizations are actively securing: Machine Control Protocols (MCPs) . These systems quietly decide what an AI agent can run, which tools it can call, which APIs it can access, and what infrastructure it can touch. Once that control plane is compromised or misconfigured, the agent doesn’t just make mistakes—it acts with authority. Ask the teams impacted by CVE-2025-6514 . One flaw turned a trusted OAuth proxy used by more than 500,000 developers into a remote code execution path. No exotic exploit chain. No noisy breach. Just automation doing exactly what it was allowed to do—at scale. That incident made one thing clear: if an AI agent can execute...