Sanction teardown · FedCFamC2G (Division 2), Australia · 2026-07-22

Asif v Minister for Immigration and Citizenship [2026] FedCFamC2G 1402

What happened

In FedCFamC2G (Division 2), Australia, a filing relied on ChatGPT to help draft legal argument. The court identified the following problems with the citations in that filing:

• Fabricated (Case Law)
  ChatGPT produced fabricated details relating to a 'No 3' judgment and admitted fabricating those details when interrogated by the Judge.
• Misrepresented (Case Law)
  Submission cited a non-existent paragraph [39] in Garikimukku; the Court noted paragraph [39] does not exist.
• Misrepresented (Case Law)
  Applicant's submissions relied on Inderjit in a manner the Court found the case did not support (misstated principle).
• Misrepresented (Case Law)
  Applicant referenced 'Patel 2026' in a likely AI-driven miscitation; the Court noted the citation was unintended and likely meant Patel 2019.
• Fabricated (Exhibits & Submissions)
  AI-generated written submissions contained hallucinated authorities and repetitive, substantively weak material imposing extra burden on respondent and Court.

Which AI tool

ChatGPT. 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

Application dismissed; Applicant ordered to pay First Respondent's costs of 9,097.93; Court criticised AI-generated submissions and noted possibility of above-scale costs due to AI hallucinations. (monetary penalty: 1 .)

Additional detail

The self-represented Applicant admitted using ChatGPT to draft written submissions which contained hallucinated and incorrect authorities, paragraph references, and propositions. The Court identified fabricated details (including a ChatGPT-created account concerning a 'No 3' judgment) and multiple mis-citations/misrepresentations (eg. incorrect paragraph citation in Garikimukku, improper use of Inderjit and Patel citations). The Court criticised the AI-generated material, treated it as imposing an additional burden on the respondent and the Court, and ordered costs (with consideration of above-scale costs because of the AI usage).

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/2711/Asif_v_Minister_for_Immigration_and_Citizenship_2026_FedCFamC2G_1402_22_July_2026.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).