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AI incident #734 ·

Leading AI Models Reportedly Found to Mimic Russian Disinformation in 33% of Cases and to Cite Fake Moscow News Sites

Open on the AI Incident Database 4 news reports Snapshot 7 Sep 2026

What happened

An audit by NewsGuard revealed that leading chatbots, including ChatGPT-4, You.com’s Smart Assistant, and others, repeated Russian disinformation narratives in one-third of their responses. These narratives originated from a network of fake news sites created by John Mark Dougan (Incident 701). The audit tested 570 prompts across 10 AI chatbots, showing that AI remains a tool for spreading disinformation despite efforts to prevent misuse.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (4)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. Top 10 Generative AI Models Mimic Russian Disinformation Claims A Third of the Time, Citing Moscow-Created Fake Local News Sites as Authoritative Sources
    newsguardtech.com · McKenzie Sadeghi · AIID #3956
  2. Exclusive: Leading chatbots are spreading Russian propaganda
    axios.com · Ina Fried · AIID #4885
  3. Popular Chatbots Spout Russian Misinformation, Finds Study
    bankinfosecurity.com · Rashmi Ramesh · AIID #4884
  4. Most AI chatbots now 'tainted,' are spreading Russian propaganda, finds study
    firstpost.com · FP Staff · AIID #4886

Who was involved

Alleged harmed party
Western Democracies, Volodymyr Zelenskyy, Ukraine, Secret Service, Researchers, Media Consumers, General Public, Democratic Integrity, Ai Companies Facing Reputational Damage

Classification (MIT AI Risk Repository taxonomy)

Risk domain
Misinformation
Causal entity
AI
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)

  • 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)

  • 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)

  • 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)

  • Untruthful Content

    "The LLM-generated content could contain inaccurate information"

    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)

  • 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)

Incidents in the same risk subdomain

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