Sanction teardown · SC New York, USA · 2026-04-03
Kleyman Law Group, P.C. v. James Kaloidis
What happened
In SC New York, 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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Misrepresented (Case Law)Plaintiff represented Pace v Raisman & Assocs. as dismissing counterclaims for post-breakdown claims; court found this to be a mischaracterization of the actual holding.
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Misrepresented (Case Law)Plaintiff claimed Butler v Catinella involved sanctions; court found Butler does not involve sanctions and Plaintiff misstated that holding.
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Fabricated (Case Law)Court identified 'Feder Kaszovitz LLP v Rosen, 202 AD3d 421 [1st Dept 2022]' as a non-existent (fabricated) citation repeatedly used in Plaintiff's submissions.
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Fabricated (Case Law)Court identified 'Bauer v Cravath, Swain & Moore, LLP, 177 AD3d 479 [1st Dept 2019]' as a fictitious case cited by Plaintiff.
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Fabricated (Case Law)Court identified 'Pine v AJA Delivery, Inc., 201 AD3d 640 [2d Dept 2022]' (listed in filings as Pine v AIA Delivery) as a fabricated citation.
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Misrepresented (Case Law)Court identified widespread misstatements of holdings (at least 83 instances) across many cited authorities, requiring judicial resources to correct.
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
Sanctions
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/2026/Kleyman_L__Grp__P_C__v__Kaloidis.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).