Compiled by Citation Safe (Digital Empire Holdings LLC) from the court’s published sanction order (decided August 20, 2026). Quotes and case details come from the order itself — check the primary source before relying on them.

Sanction teardown · Gujarat High Court, India · 2026-08-20

Faiz Enterprise v. State Tax Officer, Unit-67, Surat & Anr.

What happened

In Gujarat High 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:

Fabricated (Case Law)

Impugned order cited 'State of Gujarat v. Aarbee Structures Pvt. Ltd., 2024 (4) TMI 951' which opposing counsel says does not exist; court found reliance on AI-generated case law and officer admitted AI use.
Misrepresented (Case Law)

Impugned order referred to 'M/s. Sri Vinayaga Agencies v. Assistant Commissioner, 2023 (5) TMI 106' with a different citation/ratio than the actual Madras High Court decision; opposing counsel said citation and ratio do not apply.
Misrepresented (Case Law)

Impugned order relied on 'Jyoti Tar Products Pvt. Ltd. v. Deputy Commissioner, 2024 (7) TMI 512' which opposing counsel stated does not match cited authorities and whose ratio was inapplicable; court treated these as AI-generated/irrelevant citations.

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.

Check a brief before you file it → · See our live false-verify rate

Source: https://www.damiencharlotin.com/documents/2931/Faiz_Enterprise_v._State_Tax_Officer.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).