AIPolicyTracker
Menu

AI incident ·

Image Purporting to Show President Paul Kagame of Rwanda in M23 Uniform Reportedly AI-Generated

1 news report Snapshot 7 Sep 2026

In brief

An AI system built by Deepfake Technology Developers and Image Generation Technology Developers and deployed by Information Manipulation Actors and Information Manipulation Actors Targeting Paul Kagame allegedly harmed Regional Peacebuilding Efforts In The African Great Lakes Region, Paul Kagame and 6 others.

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

What happened

An image circulating on social media appeared to show Rwandan President Paul Kagame wearing a military uniform with an M23 label, referencing the rebel group active in eastern DRC. Fact-checking organization PesaCheck found no credible sources supporting the image and used AI-detection tools to determine it was likely AI-generated. The image was reportedly used to imply Kagame's affiliation with M23 amid ongoing regional tensions and allegations of Rwandan support for the group.

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. FAKE: This photo of President Kagame in an M23-labelled army uniform is AI-generated
    pesacheck.org · Pius Enywaru, Mary Mutisya, Stephen Ndegwa

Who was involved

Alleged harmed party
Regional Peacebuilding Efforts In The African Great Lakes Region, Paul Kagame, National Security And Intelligence Stakeholders, Government Of Rwanda, General Public Of The Democratic Republic Of The Congo, General Public Of Rwanda, General Public, Epistemic Integrity

Classification (MIT AI Risk Repository taxonomy)

Risk domain
Misinformation
Causal entity
AI
Intent
Other
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 #1098 on the AI Incident Database · all 1 report