In this article, we will look at how GitHub built a security architecture that assumes the agent is already compromised.

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GitHub built a layered security architecture for AI agents running inside GitHub Actions, designed around the assumption that the agent is already compromised. The architecture has three independent layers: a substrate layer using Docker containers and kernel-level isolation, a configuration layer that compiles workflows with explicit permissions and keeps secrets physically unreachable from the agent, and a planning layer that stages outputs for deterministic vetting before they affect real state. Key mechanisms include a secretless agent container topology using proxies and gateways, a safe outputs pipeline that enforces allowlists, quantity limits, and content sanitization, and comprehensive logging at every trust boundary. The post also discusses trade-offs: strict-by-default sandboxing limits flexibility, prompt injection remains fundamentally unsolved, and the architecture is complex enough that it may not suit simpler use cases.

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