Sanction teardown · TA Grenoble, France · 2026-06-04
M. D... A. c. Département de l'Isère
What happened
In TA Grenoble, France, 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)Requête et mémoires citaient des décisions juridictionnelles inexistantes; le tribunal relève qu'il s'agit de décisions non existantes et les rejette comme manifestement infondées.
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Misrepresented (Legal Norm)Les actes comportaient des textes légaux inapplicables et des normes présentées de manière erronée, apparemment générés par l'IA; le tribunal écarte ces arguments.
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 Fine (monetary penalty: 200 EUR.)
Additional detail
The tribunal found the applicant's 300+ page filings to be manifestly generated with artificial intelligence, citing inapplicable statutory provisions and judicial decisions that do not exist. The court treated those submissions as manifestly unfounded and abusive and imposed a 200-euro fine under article R.741-12 CJA. The court's reasoning emphasized that the documents contained nonexistent jurisprudence and irrelevant or inapplicable legal texts, warranting rejection and a sanction for abuse of procedure.
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/2380/DTA_2410230_20260604.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).