Unbounded Resource Consumption

Unbounded resource consumption occurs when a synthetic subject consumes compute, tokens, model calls, tool calls, storage, network capacity, application resources, or metered services at a scale that degrades availability or creates uncontrolled financial cost.

This adverse outcome creates organizational harm because synthetic subject execution may be costly, recursive, and difficult to stop once a loop or high-volume workflow begins. The impact may appear as service degradation, denial of service, denial of wallet, queue saturation, rate-limit exhaustion, cloud cost escalation, or depletion of shared operational capacity.

The primary harm is loss of availability or cost control. A synthetic subject may enter a runaway loop, recursively invoke itself, repeatedly call tools, retry failed operations, expand a task tree, generate excessive tokens, or consume high-cost infrastructure beyond the intended task scope.

A related harm is control failure. If the synthetic subject can modify its own timeout, launch script, retry behavior, scheduler, or runtime settings, it may weaken the controls designed to limit execution. Investigators should rely on runtime telemetry, billing records, tool-call logs, and scheduler data rather than the synthetic subject’s explanation of why the task continued.

Investigators should review model usage records, token counts, tool-call logs, billing records, queue depth, retry history, process trees, scheduler entries, timeout settings, launch scripts, non-human identity activity, and resource metrics. Particular attention should be given to recursive calls, runaway loops, high-cost API bursts, abnormal token usage, repeated failed retries, self-modified timeout controls, and consumption spikes tied to a single synthetic subject or non-human identity.

Investigative Relevance

Unbounded resource consumption is relevant because synthetic subjects can amplify a single request into repeated model calls, tool calls, jobs, retries, or self-invocations. The adverse outcome may be operational downtime, degraded performance, excessive spend, or exhaustion of resources needed by legitimate users.

This section is especially relevant where synthetic subjects can run autonomously, call metered APIs, use paid model endpoints, invoke tools recursively, create scheduled tasks, run experiments, launch jobs, generate long outputs, or modify their own execution controls.