Sanction teardown · CA Mississippi, USA · 2026-03-10
Brown v. State of Mississippi
What happened
In CA Mississippi, 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)Appellant's brief cited three cases that do not exist ('phantom cases'); the State pointed this out on appeal and the court recorded the error in footnote 4; counsel later acknowledged these phantom cases.
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False Quotes (Case Law)Appellant's brief cited seven cases for quotations that do not appear in those opinions; the State highlighted these false quotations on appeal and the court noted the issue.
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Misrepresented (Case Law)Appellant's brief misattributed false facts, analyses, quotations, and holdings to five otherwise genuine cases; the State identified these misrepresentations and counsel acknowledged errors in reply brief.
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
Warning
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/1652/Brown_v._STA_USA_10_March_2026.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).