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Sigal v. Croner

Court
Nevada DC
Jurisdiction
USA
Decided
2025-09-22
AI tool
Implied
Outcome
Court declined to impose sanctions now but cautioned Plaintiff's counsel and warned that future submission of inaccurate or AI-generated authorities will trigger a NRCP 11(c)(3) show-cause hearing and potential sanctions.
Monetary penalty
None reported

What was hallucinated

Fabricated: Case Law | Listed by Defendants and flagged by the court as a likely AI-generated/fictitious authority cited in Plaintiff's Opposition. || Fabricated: Case Law | Listed by Defendants and flagged by the court as an authority that appeared inaccurate or AI-generated in Plaintiff's Opposition. || Misrepresented: Case Law | Appeared among authorities challenged as inaccurate/AI-generated; court noted concerns though LaMantia is an existing Nevada case. || Fabricated: Case Law | Listed by Defendants and flagged by the court as a likely AI-generated/fictitious authority cited in Plaintiff's Opposition.

Details

The court flagged multiple authorities in Plaintiff's Opposition as likely AI-generated or inaccurate, preserving the unverified citations in the record but finding basis for concern after its own review. Defendants' counsel identified several suspect authorities (e.g., Pope v. Fellhauer, Wells v. Maroulis, Taylor v. Colon), and the court cautioned Plaintiff's counsel that future submissions of inaccurate or AI-generated citations will prompt a NRCP 11(c)(3) show-cause order and possible sanctions. No show-cause hearing or monetary sanction was imposed at this time.

Sanction teardown · Nevada DC, USA · 2025-09-22

Sigal v. Croner

What happened

In Nevada DC, 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:

  • Fabricated (Case Law)
    Listed by Defendants and flagged by the court as a likely AI-generated/fictitious authority cited in Plaintiff's Opposition.
  • Fabricated (Case Law)
    Listed by Defendants and flagged by the court as an authority that appeared inaccurate or AI-generated in Plaintiff's Opposition.
  • Misrepresented (Case Law)
    Appeared among authorities challenged as inaccurate/AI-generated; court noted concerns though LaMantia is an existing Nevada case.
  • Fabricated (Case Law)
    Listed by Defendants and flagged by the court as a likely AI-generated/fictitious authority cited in Plaintiff's Opposition.

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

Court declined to impose sanctions now but cautioned Plaintiff's counsel and warned that future submission of inaccurate or AI-generated authorities will trigger a NRCP 11(c)(3) show-cause hearing and potential sanctions.

Additional detail

The court flagged multiple authorities in Plaintiff's Opposition as likely AI-generated or inaccurate, preserving the unverified citations in the record but finding basis for concern after its own review. Defendants' counsel identified several suspect authorities (e.g., Pope v. Fellhauer, Wells v. Maroulis, Taylor v. Colon), and the court cautioned Plaintiff's counsel that future submissions of inaccurate or AI-generated citations will prompt a NRCP 11(c)(3) show-cause order and possible sanctions. No show-cause hearing or monetary sanction was imposed at this time.

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/2353/26_-_Sigal_v_Croner.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).

Source: https://www.damiencharlotin.com/documents/2353/26_-_Sigal_v_Croner.pdf

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