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Purported Fictitious AI-Generated Citations in Supreme Court of Victoria Murder Case Filing Lead to Delay and King's Counsel Apology

1 news report Snapshot 7 Sep 2026

In brief

An AI system built by Generative Ai Developers and deployed by Rishi Nathwani allegedly harmed Accused Minor In Victoria State Murder Case, Rishi Nathwani and 4 others.

Risk domain
Misinformation False or misleading information
Occurred
Coverage
1 reportAug 2025

What happened

Australian defense lawyer Rishi Nathwani KC reportedly filed submissions in a Supreme Court of Victoria murder case that included allegedly fictitious quotes and nonexistent case citations purportedly generated by AI. The errors were reportedly discovered by the judge's associates, causing a 24-hour delay in the proceedings. Nathwani apologized in court, taking full responsibility, while Justice James Elliott criticized the incident and reiterated that AI outputs must be independently verified.

Laws that address this harm

Policy angle: Classified under Misinformation (False or misleading information) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (1)

Titles link to the original publisher; report text is not reproduced here.

  1. Australian lawyer apologizes for AI-generated errors in murder case
    apnews.com · Rod McGuirk

Who was involved

Alleged deployer
Rishi Nathwani
Alleged developer
Generative Ai Developers
Alleged harmed party
Accused Minor In Victoria State Murder Case, Rishi Nathwani, Daniel Porceddu, James Elliott, Supreme Court Of Victoria, Judicial Integrity

Classification (MIT AI Risk Repository taxonomy)

Risk domain
Misinformation
Causal entity
Human
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 3.1.

  • Pursuing Consistent Context

    "LLMs have been demonstrated to pursue consistent context [129]–[132], which may lead to erroneous generation when the prefixes contain false information. Typical examples include sycophancy [129], [130], false demonstra...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Defective Decoding Process

    In general, LLMs employ the Transformer architecture [32] and generate content in an autoregressive manner, where the prediction of the next token is conditioned on the previously generated token sequence. Such a scheme...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Noisy Training Data

    "Another important source of hallucinations is the noise in training data, which introduces errors in the knowledge stored in model parameters [111]–[113]. Generally, the training data inherently harbors misinformation....

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Hallucinations

    "LLMs generate nonsensical, untruthful, and factual incorrect content"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Faithfulness Errors

    "The LLM-generated content could contain inaccurate information" which is is not true to the source material or input used

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Factuality Errors

    "The LLM-generated content could contain inaccurate information" which is factually incorrect

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Untruthful Content

    "The LLM-generated content could contain inaccurate information"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Knowledge Gaps

    "Since the training corpora of LLMs can not contain all possible world knowledge [114]–[119], and it is challenging for LLMs to grasp the long-tail knowledge within their training data [120], [121], LLMs inherently posse...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

Incidents in the same risk subdomain

All incidents in this subdomain

Source record: incident #1184 on the AI Incident Database · all 1 report