Sanction teardown · 10th Cir. CA, USA · 2026-02-09
Kusmin L. Amarsingh v. Frontier Airlines, Inc.
What happened
In 10th Cir. CA, USA, a filing relied on ChatGPT to help draft legal argument. The court identified the following problems with the citations in that filing:
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Fabricated (Case Law)Brief contained seven case citations that the court could not locate; court ordered appellant to produce accurate copies or explain and concluded they were fabricated by ChatGPT.
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False Quotes (Case Law)Appellant attributed propositions or quotations to a real case that did not contain the quoted language or stand for the cited proposition; court determined the attribution was inaccurate and resulted from AI output.
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False Quotes (Case Law)A second instance where appellant attributed a quotation or proposition to an actual opinion that did not contain that material; court treated this as an AI-produced misquote and part of the reckless failure to verify authorities.
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Fabricated (Case Law)Appellant's brief included seven case citations that the court could not locate; court determined they were fabricated outputs of ChatGPT.
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False Quotes (Case Law)Appellant attributed propositions and quotations to two real cases that did not contain those propositions or quotations; court found the attributions inaccurate.
Which AI tool
ChatGPT. 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
Monetary Sanction; Bar Referral (monetary penalty: 1000 USD.)
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/1484/Amarsingh_v._Frontier_Airlines.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).