Sanction teardown · CA Indiana, USA · 2026-02-06
Mutugu v. Kiaraho
What happened
In CA Indiana, 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)The court determined Father cited five legal authorities that do not exist in total (multiple likely AI-generated cases cited in the brief).
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Misrepresented (Case Law)Father cited In re Estate of Brown, 587 N.E.2d 686, 689 (Ind. Ct. App. 1992) for the proposition that admitted exhibits "cannot later be deemed inadmissible," but the cited opinion at that pincite addressed jurisdiction/venue, not that proposition.
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Fabricated (Case Law)Father cited a non-existent Indiana case "Thompson v. State" with reporter citation 811 N.E.2d 501, which the court found does not exist and whose reporter cite corresponds to an unrelated Massachusetts case.
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Misrepresented (Exhibits & Submissions)Father cited the transcript at page 371 to support that the trial court 'dismissed' exhibits as 'unreliable' or 'unauthenticated,' but the cited transcript page does not exist.
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
Not specified in source record.
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/1507/Mutugu_v_Kiaraho.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).