Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Researchers recently analyzed 18,000 Claude Code configuration files pulled from public GitHub repositories. What they found was straightforward and alarming: developers are already installing mistyped, misconfigured, and near-identical MCP server names — often without realizing it. The human-error condition that makes typosquatting work was already present at scale before any attacker needed to exploit it.

In February 2026, researchers uncovered something that should give every security leader pause. A malware operation called SmartLoader, previously known for targeting consumers who downloaded pirated software, had completely pivoted its infrastructure. SmartLoaders new target was developers, and its new entry point was a protocol most security teams had never heard of. The payload delivered to victims: every saved browser password, every cloud session token, every SSH key on the machine.

Anthropic built a powerful AI model and then kept it on a short leash. The important part is not that a model found bugs, which has been coming for a while. What’s worth acknowledging is that Anthropic looked at what Mythos could do and decided broad release was a bad idea. Attackers do not need a perfect autonomous system. They need leverage.

There’s never a good time to lose a production database, but losing one to your own AI coding agent on a Friday afternoon has to rank near the bottom of the list. That’s the backdrop to the PocketOS incident, and it’s the clearest case yet for why AI agent security and intent-based access control belong at the top of every cloud security roadmap this year.

As more organizations move past experimentation and start planning real AI agent deployments, the same set of concerns keeps surfacing in our conversations with security teams. Whether the worry is a shadow agent that shows up uninvited or a sanctioned agent going rogue, the questions tend to cluster around control: These are the right questions to be asking, and they share a common answer that’s more concrete than most people expect. AI agents are only as dangerous as the privileges they can reach.

As autonomous and semi-autonomous AI systems take on more responsibility within the enterprise, they shift from being “features” of software to becoming true internal actors. They make decisions, take actions, call tools, orchestrate workflows, and influence other AI agents. With this evolution, we must confront an uncomfortable truth: the metrics and response patterns we built for deterministic software no longer work.