AI Security’s False Alarm Crisis: When Doritos Become “Weapons”

In what could be a scene from a dark comedy about our technological times, a Baltimore County high school student found himself handcuffed and searched by police because an AI security system saw something threatening in his snack choice. The culprit? A bag of Doritos that the algorithm apparently mistook for a firearm. This isn’t just another amusing AI fail story—it’s a sobering case study in what happens when flawed automation meets real-world consequences in our schools.

The Incident That Shouldn’t Have Happened

According to reports from CNN affiliate WBAL, Taki Allen, a student at Kenwood High School, was going about his normal school day when security protocols suddenly escalated into what he described as a traumatic experience. “I was just holding a Doritos bag—it was two hands and one finger out, and they said it looked like a gun,” Allen told reporters. The result was exactly what you’d fear: “They made me get on my knees, put my hands behind my back, and cuffed me.”

What makes this particularly troubling is the cascade of failures that followed the initial misidentification. Principal Katie Smith’s statement to parents revealed that the school’s security department had already reviewed and canceled the gun detection alert. Yet somehow, Smith—apparently unaware the alert had been canceled—escalated the situation to the school resource officer, who then called local police. This breakdown in communication suggests that even when the technology correctly identifies its own errors, human systems can still fail catastrophically.

When “Functioning as Intended” Isn’t Good Enough

Omnilert, the company behind the AI gun detection system, offered what might be the most concerning statement in this entire saga. While expressing regret for the incident and concern for the student, the company maintained that “the process functioned as intended.” This defense reveals a fundamental disconnect between how technology companies view their systems and how those systems actually perform in complex educational environments.

I’ve covered enough AI implementation failures to recognize a pattern here. Companies often design systems for ideal conditions, then deploy them in messy real-world scenarios where the stakes are incredibly high. When a secondary school environment—with its crowded hallways, varied lighting conditions, and constantly changing objects—becomes the testing ground for firearm detection, the margin for error should be vanishingly small. Yet here we have a system that can’t reliably distinguish between snack foods and weapons.

The Broader Implications for School Security

This incident arrives at a time when schools nationwide are increasingly turning to automated security solutions, often in response to legitimate safety concerns. The market for AI-based security in educational institutions has been growing rapidly, with companies promising faster threat detection than human security teams could ever achieve. But the Kenwood High case raises uncomfortable questions about whether we’re trading one set of risks for another.

What’s particularly concerning is the psychological impact on students. Being handcuffed and treated as a potential threat in your own school creates lasting trauma and damages the trust between students and administration. Meanwhile, the “boy who cried wolf” effect is very real—if systems generate enough false positives, will school staff become desensitized to actual threats when they occur?

The Competitive Landscape and Technical Challenges

Omnilert isn’t alone in this space—companies like ZeroEyes, Athena Security, and Evolv Technology all compete in the AI weapons detection market. Each promises varying levels of accuracy, but industry experts I’ve spoken with suggest that even the best systems struggle with object recognition in dynamic environments. The fundamental challenge is that AI models are trained on limited datasets, and the real world presents infinite variations that algorithms simply haven’t encountered.

What’s surprising is how basic this particular failure appears. A Doritos bag—with its distinctive triangular logo and crinkly texture—shouldn’t be confused with the hard edges and metallic surfaces of firearms by any competent computer vision system. This suggests either inadequate training data, poor image quality, or algorithmic limitations that raise questions about the system’s overall reliability.

Where Do We Go From Here?

The path forward requires a more nuanced approach to school security technology. First, companies need to be transparent about their systems’ limitations and accuracy rates in real-world conditions. Second, schools must implement robust human oversight protocols that don’t automatically treat AI alerts as definitive threats. And third, we need clearer accountability frameworks for when these systems fail—because they will fail, and the consequences shouldn’t fall entirely on students.

What happened to Taki Allen represents more than just a technological glitch—it’s a warning about how we’re implementing AI in sensitive environments. As schools continue to invest in security technology, they need to ask harder questions about reliability, oversight, and the real-world impact of false positives. Because when the choice is between student safety and student trauma, there should be no room for error—especially not over a bag of chips.