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AI incident #1250 ·

Alleged False Positive by Omnilert AI Gun Detection System Prompts Police Search at Baltimore County High School

Open on the AI Incident Database 3 news reports Synced from the AIID API · record last edited 4 Sep 2026

What happened

A purportedly AI-powered gun detection system at Kenwood High School in Baltimore County, Maryland, reportedly misidentified a student's empty Doritos bag as a firearm. Armed police reportedly detained and handcuffed the student before determining there was no weapon. The AI system, operated by Omnilert, issued the false alert, leading to public outcry and an official review of school security protocols. Omnilert reportedly expressed regret for the incident.

Editor's notes (AI Incident Database)

See also Incident 1267: Omnilert AI Reportedly Triggered False Gun Alert at Parkville High, Prompting Student Relocation.

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. A.I. gun detection false alarm at school has Baltimore County leaders calling for review
    cbsnews.com · Janay Reece · AIID #6328
  2. US student handcuffed after AI system apparently mistook bag of chips for gun
    theguardian.com · Guardian staff · AIID #6329
  3. Student handcuffed after Doritos bag mistaken for a gun by school's AI security system
    abc7.com · Karina Tsui, Zoe Sottile · AIID #6321

Who was involved

Alleged developer
Omnilert
On AIID: Omnilert
Alleged harmed party
Taki Allen Students Minors Kenwood High School students High school students Educational communities
On AIID: Taki Allen, Students, Minors, Kenwood High School students, High school students, Educational communities

AI systems implicated

Weapons detection systemOmnilertAI-enabled video surveillance system for gun detectionAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

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 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.

Incidents in the same risk subdomain

All incidents in this subdomain

Other incidents involving Baltimore County Public Schools