Sanction teardown · CA California, USA · 2026-02-26
Samuel K. v. Winsley Focia
What happened
In CA California, 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)Opening brief cited a nonexistent case 'Medical Board v. Superior Court (2022) 88 Cal.App.5th 459, 475' and attributed multiple due-process quotations to it; court found no such published case or quotations and labeled them AI 'hallucinations.'
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Misrepresented (Case Law)Brief quoted 'Admitting a transcript without the original recording is reversible error.' and cited People v. Panah (2005) 35 Cal.4th 395, 475; court found the quotation does not exist and Panah reached the opposite conclusion.
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Fabricated (Other)The brief contained 12 quotations (11 fabricated) and numerous inaccurate or inapposite citations (some reporter citations corresponding to different criminal cases); court characterized the brief as 'peppered with inaccurate citations' and AI-generated fabrications.
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
Appeal dismissed; appellant to bear her own costs on appeal.
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/1587/Samuel_K._v._Focia_USA_26_February_2026.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).