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DOJ Attorney Reportedly Used AI to File Brief With Purportedly Fabricated Quotes and Misstated Case Holdings

2 news reports Snapshot 7 Sep 2026

In brief

An AI system built by Large Language Model Developers and deployed by United States Attorney'S Office For The Eastern District Of North Carolina and Rudy Renfer allegedly harmed Judicial Integrity, Epistemic Integrity and 1 other.

Risk domain
Misinformation False or misleading information
Occurred
Coverage
2 reportsMar 2026

What happened

In Fivehouse v. U.S. Department of Defense, Assistant U.S. Attorney Rudy Renfer filed a response brief that the court reportedly said contained fabricated quotations and misstatements of case holdings. Renfer reportedly admitted he had used AI to help draft the inaccurate filing after losing an earlier draft. The court reportedly also identified similar fabricated quotations in other filings signed by Renfer.

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 (2)

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

  1. DOJ Attorney Used Fabricated Quotes in Court Filing (3)
    news.bloomberglaw.com · Ben Penn
  2. DOJ leaders must explain why attorney used fabricated quotes in court filing
    abajournal.com · ABA Journal

Who was involved

Alleged harmed party
Judicial Integrity, Epistemic Integrity, Derence V. Fivehouse

Classification (MIT AI Risk Repository taxonomy)

Risk domain
Misinformation
Causal entity
Other
Intent
Unintentional
Timing
Other
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 #1434 on the AI Incident Database · all 2 reports