Validate bounded representations before expensive inference work

Apply an ingress byte limit and bounded parsing depth or field counts before building an unbounded object tree. For compressed bodies or uploaded media, enforce decoded-byte, pixel, frame, duration, and elapsed-work budgets appropriate to the supported codecs. A reported width or duration is untrusted until checked against the actual decode path. Count cumulative work across multiple attachments, not only per-item maxima, and reject inconsistent or unsupported representations without echoing sensitive content into error logs.

JSON Schema 2020-12 can express types, numeric ranges, array lengths, and string limits, but it does not replace transport budgets or semantic checks involving multiple fields. Its format annotation vocabulary does not by default require format assertion. Record the selected validator and configuration and test the desired behavior explicitly. A syntactically valid URL, identifier, or JSON operation is still not proof that the destination is allowed or that its requested action is authorized.

Tokenize with the approved tokenizer version and bound the resulting token count, including trusted templates, conversation history, retrieved text and tool results. Do not estimate a hard limit solely from character length. For multimodal models, account for the actual representation of images, audio or video under the selected preprocessing policy; a count of text tokens alone can omit substantial work. The service should choose an explicit reject or documented truncation policy rather than silently changing meaning at an arbitrary boundary.

Validate generation parameters as finite values with allowed ranges and enforce a maximum output budget. A request fitting the model context can still exceed a tenant or physical-work budget. Conversely, a rate limit does not make malformed tensor dimensions valid. Verify shape/dtype/device compatibility before an expensive kernel path and check dimension products with overflow-safe arithmetic. Treat limits advertised by a framework as pinned API contracts to test, not as a complete security policy for an exposed endpoint.

Create fixtures at the maximum and just beyond it for body bytes, nesting, decoded pixels, batch dimensions, prompt tokens and output reservations. Include contradictory declared media sizes and nonfinite numeric parameters. Observe where rejection occurs and what CPU, memory, decoder and device work already happened. A request denied after allocating a giant image has not demonstrated the intended preallocation protection. Errors should remain bounded, and temporary buffers and reservations should follow a verified cleanup path.

The queue lab isolates the consequence of accepted work arriving faster than it is serviced. Reset, set both λ=3,μ=2, predict backlog eight at t=4 from initial four, then take eight Steps. Reset and set both λ=2,μ=3 predicts zero at t=4. Edits retain elapsed time and recompute from the initial four, so these are separate runs. The fluid model has no body parser, tokenizer, media decoder, finite admission cap or percentile latency; use actual boundary tests for those controls.