Sanction teardown · C.D. California, USA · 2026-02-12
TQJ, LLC v. Jennifer Esquivel et al.
What happened
In C.D. 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)Citation pointed to an unrelated case (United States v. De La Paz) and the purported Muller decision cited (43 F. Supp. 2d 372, 379 (S.D.N.Y. 1999)) could not be located as cited; an older unrelated Muller decision exists but does not support the proposition.
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Fabricated (Case Law)Court could not find any case called 'Steele v. County of San Mateo' at the cited 2021 WL and the offered quotation was not found in the cited jurisdiction; closest similar language found only in an unrelated N.D. Ohio case.
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Fabricated (Case Law)Court could not locate the cited authority; the 2011 WL citation did not match the cited C.D. Cal. decision and the referenced Lewis case is from a different district and does not discuss the proposition cited.
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Fabricated (Case Law)Court was unable to find any case called 'Kogan v. Martin' or the 2019 WL citation offered for the proposition about suggestions and critiques.
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
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/1500/TQJ_LLC_v_Jennifer_Esquivel_et_al.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).