Sanction teardown · Supreme Court, India · 2026-02-27
Gummadi Usha Rani & Anr. v. Sure Mallikarjuna Rao & Anr.
What happened
In Supreme Court, India, 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)Trial Court relied on this alleged judgment in the Advocate Commissioner's Report; High Court found it AI‑generated and non‑existent.
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Fabricated (Case Law)Trial Court relied on this alleged judgment (note year '1071' is manifestly erroneous); High Court found it AI‑generated and non‑existent.
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Fabricated (Case Law)Trial Court relied on this alleged judgment in the Advocate Commissioner's Report; High Court determined it to be AI‑generated and not a genuine precedent.
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Fabricated (Case Law)Trial Court relied on this alleged judgment in the Advocate Commissioner's Report; High Court found it AI‑generated and non‑existent.
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
Dismissed
Additional detail
Trial Court relied on several judgments in the Advocate Commissioner's Report that the High Court found to be AI‑generated, non‑existent and fake; Supreme Court issued notices, restrained use of the report and directed further examination of consequences and accountability.
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/1578/Gummadi_Usha_Rani_vs_Sure_Mallikarjuna_Rao.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).