Sanction teardown · D. Utah, USA · 2026-05-04
Regan Wilkes, et al. v. Canyons School District, et al.
What happened
In D. Utah, 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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Fabricated (Case Law)Amended Complaint cited a non-existent 'A.S. v. Norwalk Public Schools (2017)' (purported 6th Cir.) in support of tolling/limitations arguments; court identified it as another fabricated citation.
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Misrepresented (Case Law)Amended Complaint misstates the facts and holding of the real case J.M. v. Francis Howell School District; court found the asserted holding inaccurate and relied on this misrepresentation in support of the Sixth Cause.
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Fabricated (Case Law)Amended Complaint cited a non-existent 'F.C. v. Capistrano Unified School District' (purported 9th Cir.) to support tolling of IDEA's two-year statute; court identified it as a fabricated, AI-generated citation that undermined the pleading.
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Fabricated (Case Law)Amended Complaint cited a non-existent 'A.D. v. Puyallup School District No. 3, 2015' (purported 4th Cir.) to support statute-of-limitations arguments; court found it fabricated.
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
Order to Show Cause (monetary penalty: 7000 USD.)
Additional detail
Order to Show Cause is here.
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/2092/Wilkes_v._Canyon_School_USA_4_May_2026.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).