Agent security starts with where the agent runs, not how it behaves
Blog post from Upsun
Security considerations for AI agents should prioritize the environments in which they operate rather than solely focusing on their capabilities, as highlighted by Patrick Dawkins, a Principal Software Engineer at Upsun. He argues that while AI models themselves may not be inherently unsafe, running them on personal machines introduces significant risks due to what Simon Willison calls the "lethal trifecta": access to untrusted data, the ability to produce side effects, and access to sensitive information. This situation is exacerbated by the lack of visibility and auditability when agents operate locally, making it difficult to monitor their actions and protect sensitive data. Dawkins emphasizes the importance of structural guarantees, such as scoping network access and running agents in ephemeral sandboxes, over guardrails, which are more about probabilistic controls. The goal is to create a system that constrains potential negative outcomes, ensuring that AI agents operate within a controlled, observable, and interruptible framework to prevent unintended consequences from becoming systemic issues.
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