The earliest incarnation of this project was built as a way of running Haskell snippets in knitr (a report generation software for R). Jonathan Carroll, a DataHaskell contributor, was working on an article showcasing Haskell’s viability for data science workloads. We built a small shell script that took Haskell code snippets, transformed them to work with GHCi (particularly putting multi-line functions in blocks), evaluated them in the command line, and then captured the output.
The common pattern across all of these seems to be filesystem and network ACLs enforced by the OS, not a separate kernel or hardware boundary. A determined attacker who already has code execution on your machine could potentially bypass Seatbelt or Landlock restrictions through privilege escalation. But that is not the threat model. The threat is an AI agent that is mostly helpful but occasionally careless or confused, and you want guardrails that catch the common failure modes - reading credentials it should not see, making network calls it should not make, writing to paths outside the project.
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