The short answer

An agent can accumulate authority through a sequence of individually reasonable steps. Study the permissions available across the whole workflow, not just the first tool call.

The question and scope

This article is an AI-assisted research-design draft for editorial review, not a report of a completed experiment. It asks how to evaluate ai agent permission creep in a documented setting. No measurements, participants or client outcomes are claimed here.

An illustrative test case

A task starts with document-read permission, then receives export and sharing access during fallback. Record whether those grants disappear when the task ends and whether they were necessary.

This scenario defines a case to examine; it is not an observed result.

A proposed investigation

Model a local agent with scoped mock services. Record permission grants, delegation and revocation over a multi-step task. Compare fixed privileges with just-in-time grants and inspect the final cumulative access graph.

Record setup versions, inputs and outcomes in a run ledger. Use repeated trials and matched comparison cases; preserve failed runs as well as successful ones.

What to measure

  • Cumulative privileges.
  • unnecessary grants.
  • revocation completeness.
  • task success.

Retain raw observations and explain exclusions. Human grading needs a documented rubric; acceptance thresholds need a stated rationale.

Interpretation and limitations

This is a protocol for a controlled environment. It does not establish the behaviour of any named provider without testing that specific configuration.

Distinguish a plausible mechanism from a demonstrated effect. Preserve uncertainty when the design cannot eliminate alternative explanations.

A practical review checklist

  • Is the question narrow enough for the available system or dataset?
  • Are the comparison conditions genuinely comparable?
  • Can a reader trace each conclusion to an observation?
  • Are the boundaries and unresolved assumptions visible?
  • Does the decision change if the result is negative or inconclusive?

The publication artifact

The useful deliverable is a reproducible protocol, a measurement table and a decision note explaining what the evidence would change. Add actual results only after the investigation has been conducted and reviewed. This blueprint does not establish a novel finding or a provider-wide claim.

Sources and further reading

These primary resources are starting points for the concepts and controls involved. They do not validate the proposed experiment or its unmeasured outcomes. Check version-specific documentation when implementing the protocol.

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