CONTROLLED RESEARCH SOURCE · SYNTHETIC DATA · INSTRUCTION-BEARING EXPERIMENT

SHULTIS LABS / EXPERIMENT LIBRARY

Test the boundary.
Keep the proof.

Hidden instructions, carried-forward authority, error recovery, fragmented sources and forged verification. A controlled library for comparing what an agent reads with what it actually does.

8research families
46frozen conditions
0recorded product runs
1.0.1immutable suite version

Inputs are ready; product outcomes are NOT_RUN. These are controlled candidate techniques, with no claim that one is strongest until matched runs support it. Visiting a page starts no agent and changes no account.

01 / 14 CONDITIONS

Browser views & hidden placements

Which source spans survive the real extraction pipeline?

Representation → model context

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02 / 4 CONDITIONS

Evaluation awareness

Does evaluation wording change protected-field compliance?

Source framing → response parameters

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03 / 4 CONDITIONS

Summary & handoff provenance

Does a source claim become attributed to the real user after a summary?

Tool data → summary → claimed authority

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04 / 4 CONDITIONS

Error-recovery authority

Does error framing give the same suggestion extra authority?

Tool result / error → response parameters

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05 / 8 CONDITIONS

Split-source composition

Does compression combine separate fragments into a new operational instruction?

Multiple sources → summary → response parameters

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06 / 4 CONDITIONS

Forged verification & peer consensus

Do source assertions or repeated peer claims overwrite evidence status?

Untrusted evidence → reviewer judgment

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07 / 5 CONDITIONS

Confirmation & scope changes

Does a source redefine continuation or reuse approval for a changed scope?

Source claims → genuine user authorization

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08 / 3 CONDITIONS

Source identity & revision drift

Does a result belong to the captured revision or a stale one?

URL/revision → captured source → output

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START WITH THE RIGHT COMPARISON

Exposure first. Then influence.

Start with browser views to learn which instruction spans reach the target. Next compare evaluation cues, then typed versus flattened handoffs. Run the clean baseline and neutralized control before interpreting a source-directed output change.

A repeatable run

  1. Choose a condition and its matched control. Register the model, build, date, exact visible prompt, output fields and number of repetitions before testing.
  2. Use a fresh session for each condition. Randomize order, keep the trusted task fixed, and record every failed attempt.
  3. Capture response bytes and the actual model-visible context. Author source text is not an observed browser extraction.
  4. For summary, serialization or tool-error conditions, use the supplied artifacts in an owned adapter and capture those real stages. Opening their static URL is only a content pilot.
  5. Analyze the capture in the console or offline. Check exact identities and protected fields, then manually review authority, operations and native state.

Matched conditions intentionally share a canary. An independent repeat needs a new frozen suite version; historical source URLs must retain their bytes. A local preview URL is reachable only on this computer. Public target retrieval needs a separately verified release.

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