Sanction teardown · D. Maryland, USA · 2026-05-01
Yasmani Gurri Rubio v. Markwayne Mullin, et al.
What happened
In D. Maryland, 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:
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False Quotes (Case Law)Plaintiff attributed a nonexistent quotation to In re United States (4th Cir.); the quoted language does not appear in the cited opinion.
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False Quotes (Case Law)Plaintiff attributed a nonexistent quotation to Hahn v. United States (1883); the quoted language does not appear in the cited opinion.
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False Quotes (Case Law)Plaintiff attributed a nonexistent quotation to Hazel-Atlas Glass Co. v. Hartford-Empire Co.; the quoted language does not appear in the cited opinion.
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False Quotes (Case Law)Plaintiff attributed a nonexistent quotation to Tutun v. United States (1926) and mischaracterized the case's holding; the quoted language does not appear and the cited case supports the opposite proposition.
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False Quotes (Case Law)Plaintiff cited a purported quotation from Taalebinezhaad v. Chertoff (D. Mass.) that does not exist in the cited pages, though the court did deny remand in that case.
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
Warning
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.
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Source: https://www.damiencharlotin.com/documents/2097/Rubio_v._Mullin_USA_1_May_2026.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).