Sanction teardown · D. New Jersey, USA · 2025-12-31
Eric Hildebrandt v. siParadigm LLC et al.
What happened
In D. New Jersey, 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)Supplemental Filing contained forty-three identified instances of fabricated citations or case law; Court declined to consider the filing and treated those propositions as unsupported.
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Fabricated (Case Law)Consolidated Complaint was 'replete with citations to irrelevant, and sometimes fabricated, case law' across many paragraphs; Court refused to credit those citations.
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Misrepresented (Case Law)Defendants noted several incorrect/inaccurate citations in the Consolidated Complaint, including two cases cited over fifty times that did not support Plaintiffs' propositions; counsel's errata did not cure these inaccuracies.
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False Quotes (Exhibits & Submissions)Plaintiffs' Opposition contained AI drafting artifacts and unchecked inserted text (e.g., 'Let me know when you’d like to proceed to the next section: Count Eight . . .'), evidencing unverified AI-generated material and potential false quotations in submissions.
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/1289/Hildebrant_v_8j95Os6._Paradigm_USA_31_December_2025.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).