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Purportedly AI-Generated Images and Videos Reportedly Spread Misinformation About Nicolás Maduro's Capture on X

3 news reports Snapshot 7 Sep 2026

In brief

An AI system built by Google, Xai and 2 others and deployed by Unknown Social Media Account Operators and Information Manipulation Actors allegedly harmed Nicolas Maduro, Social Media Users and 3 others.

Risk domain
Misinformation False or misleading information
Occurred
Coverage
3 reportsJan 2026

What happened

Following the January 2026 U.S. raid and arrest of Venezuelan leader Nicolás Maduro, purportedly AI-generated images and videos circulated widely on platforms including X, reportedly depicting Maduro in fabricated scenarios. NewsGuard identified at least seven manipulated or synthetic visuals reaching over 14 million views, including AI-generated photos detected via Google Gemini's SynthID watermark.

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 (3)

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

  1. That Video of Happy Crying Venezuelans After Maduro’s Kidnapping? It’s AI Slop
    futurism.com · Joe Wilkins
  2. AI-generated content spreads after Maduro’s removal — blurring fact and fiction
    cnbc.com · Dylan Butts
  3. AI deepfakes flood social media after dramatic US raid on Maduro
    scmp.com · Agence France-Presse

Who was involved

Alleged harmed party
Nicolas Maduro, Social Media Users, General Public, General Public Of Venezuela, Epistemic Integrity

Classification (MIT AI Risk Repository taxonomy)

Risk domain
Misinformation
Causal entity
AI
Intent
Intentional
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 #1333 on the AI Incident Database · all 3 reports