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AI incident #1492 ·

Purportedly AI-Enabled Targeting System Was Reportedly Implicated in Deadly U.S. Strike on Iranian Primary School

Open on the AI Incident Database 2 news reports Synced from the AIID API · record last edited 4 Sep 2026

What happened

During Operation Epic Fury, U.S. forces reportedly struck Shajareh Tayyebeh Primary School in Minab, Iran, killing at least 150 civilians, many of them children. Reporting said the school was on a U.S. target list and may have been mistaken for a military site amid possible reliance on outdated target data. Palantir's Maven Smart System, reportedly integrated with Anthropic's Claude, was used in the campaign's targeting workflow

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 (2)

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

  1. Iranian school was on U.S. target list, may have been mistaken as military site
    washingtonpost.com · Tara Copp, Souad Mekhennet, Meg Kelly · AIID #7302
  2. AI got the blame for the Iran school bombing. The truth is far more worrying
    theguardian.com · Kevin T Baker · AIID #7408

Who was involved

Alleged developer
Palantir Anthropic
On AIID: Palantir, Anthropic
Alleged harmed party
Teachers Students Shajareh Tayyebeh Primary School community Shajareh Tayyebeh Primary School Minors Minab school-strike victims and families Iranian teachers Iranian schoolchildren Iranian civilians General public of Iran General public Educators
On AIID: Teachers, Students, Shajareh Tayyebeh Primary School community, Shajareh Tayyebeh Primary School, Minors, Minab school-strike victims and families, Iranian teachers, Iranian schoolchildren, Iranian civilians, General public of Iran, General public, Educators

AI systems implicated

Maven Smart SystemClaudeAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
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Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 7.3.

  • Reliability issues

    "Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...

    International AI Safety Report 2025 (Bengio2025)

  • Type 2: Bigger than expected

    Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Type 3: Worse than expected

    AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Ethics and Morality Issues

    LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • Safe learning

    "AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."

    AGI Safety Literature Review (Everitt2018 )

  • Malign belief distributions

    "Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...

    AGI Safety Literature Review (Everitt2018 )

  • Meta-cognition

    "Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...

    AGI Safety Literature Review (Everitt2018 )

  • Misaligned consequentialist reasoning

    "As we think about even more intelligent and advanced AI assistants, perhaps outperforming humans on many cognitive tasks, the question of how humans can successfully control such an assistant looms large. To achieve the...

    The Ethics of Advanced AI Assistants (Gabriel2024)

Linked by AIID editors or by its text-similarity model.

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