Sanction teardown · N.D. California, USA · 2026-01-14
Hang Zhang v. Daniel Driscoll
What happened
In N.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)Reply brief contains multiple citations the Court could not locate and characterized as fictitious/hallucinated; Court declined to reproduce the fictitious names to avoid repeating them.
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Misrepresented (Case Law)Plaintiff cited a purported Fifth Circuit case for the proposition that courts 'credit unopposed constitutional arguments,' but the pincite corresponds to United States v. Santiago, 905 F.3d 1013, 1018 (7th Cir. 2018), a Seventh Circuit case that does not support the proposition cited.
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Misrepresented (Case Law)Plaintiff cited a purported Ninth Circuit case for the proposition that constitutional claims 'almost always demonstrate' irreparable harm, but the pincite corresponds to Thompson v. D.C., 967 F.3d 804, 813 (D.C. Cir. 2020), which does not support that premise.
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
Monetary Sanction (monetary penalty: 500 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/1311/Zhang_v._Driscoll_USA_14_January_2026.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).