Sanction teardown · S.D. Ohio, USA · 2026-07-29

Terence A. Gragston v. Amazon LLC

What happened

In S.D. Ohio, USA, a filing relied on an unnamed/unconfirmed AI tool to help draft legal argument. The court identified the following problems with the citations in that filing:

• Fabricated (Case Law)
  Pro se plaintiff cited a purported Sixth Circuit opinion to support his argument; the Court found no such case or Westlaw citation and determined the citation was AI‑hallucinated.

Which AI tool

an unnamed/unconfirmed AI tool. Note: Charlotin's public database records tool attribution only where a court order, brief, or reporting on the matter states it explicitly; "unidentified" or "implied" means the record indicates AI use but does not name a specific product — we do not guess.

Outcome

Court issued a formal warning that future reliance on AI‑hallucinated caselaw will result in sanctions, including monetary penalties and possible dismissal with prejudice; no sanction was imposed at this time.

Additional detail

The pro se plaintiff relied on purported Sixth Circuit authority that the Court determined was AI‑hallucinated (a fabricated opinion/citation). The Court's independent review found no case matching the cited docket number or Westlaw citation and formally warned that further reliance on AI‑generated or unverified authorities will result in sanctions (including monetary penalties and dismissal with prejudice). The Court preserved the unverified citations in the record but noted links were unavailable.

How Citation Safe would have caught this

Citation Safe runs three deterministic layers before a brief is filed: (1) does the citation exist against CourtListener's database of published opinions, (2) if quoted, does that exact language appear in the source, (3) does the cited case actually support the proposition it is cited for. Fabricated case citations fail Layer 1. Fabricated or misattributed quotations fail Layer 2 even when the underlying case is real. Misrepresented holdings — a real case cited for a proposition it does not support — are the target of Layer 3. None of these checks involve asking another language model whether the citation looks right; they are lookups and text-matches against the actual source, which is why a hallucinated citation has to survive a direct lookup against the authoritative source — not another model's opinion — to earn a VERIFIED stamp; our measured false-verify rate is published live at /quality.

Check a brief before you file it → · See our live false-verify rate

Source: https://www.damiencharlotin.com/documents/2786/TERENCE_A_GRAGSTON_Plaintiff_v_AMAZON_LLC_Defendant_USA_July_2026.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).