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AI incident #1330 ·

Purportedly AI-Generated 'Eric Langford' Missing Boy Scout Hoax Circulating Across Multiple Social Media Platforms

Open on the AI Incident Database 3 news reports Snapshot 7 Sep 2026

What happened

A purportedly AI-generated fictional story falsely claimed that a Boy Scout named Eric Langford disappeared in New York in 1989 and reappeared in 2001 after being kidnapped. The reported hoax originated on the UNKNOWN Files YouTube channel and was repackaged into viral TikTok videos and AI-written blog posts promoted on Facebook, misleading millions of viewers and searchers about a fabricated missing-person case.

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 (3)

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

  1. Story about Eric Langford, missing Boy Scout, isn't what it seems
    snopes.com · Jordan Liles · AIID #6778
  2. Fact Check: Is Story Of Missing Boy Scout Eric Langford Real?
    newsweek.com · Jordan King · AIID #7578
  3. Terrifying truth about what really happened to Boy Scout who vanished
    dailymail.com · Nic White · AIID #7579

Who was involved

Alleged deployer
Unknown Files
Alleged harmed party
Epistemic Integrity, Social Media Users, Youtube Users, Tiktok Users, Facebook Users

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)

  • 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)

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