The US spent billions on border surveillance. Why can't it catch people before they die? Surveillance towers, increasingly powered by AI, promised faster responses to crossings. Our investigation of deaths near the towers found they are failing to deliver. When José Morales Bernal crossed the border into the United States on April 8, 2024, the day before his 32nd birthday, it should have triggered a chain of technological alerts and human responses. As he walked through the desert in southern New Mexico that morning, he was within range of three surveillance towers. Newly installed by US Customs and Border Protection (CBP), they were built by the defense tech company Anduril and equipped with cameras and AI to automatically detect and track people. They transmit live video to nearby control rooms and can send alerts to the government-issued smartphones held by agents in the area, prompting the closest available to respond. This story is part of Dying on Camera, a collaboration between and . These AI-enabled towers are meant to give greater visibility across the 1,951-mile southern border, freeing up border agents from having to spend hours staring into video monitors. They were installed in this particular place to spot border crossers before they reached the nearby town of Sunland Park. If the system worked as intended, Morales should have been apprehended. If he needed medical help, agents were trained to provide it. Despite the nearby surveillance towers, it was employees of the local landfill, rather than Border Patrol, who first spotted Morales that morning. At 1 p.m. the workers saw him again, now lying in the sand. At 4 p.m. the landfill workers saw that he had not moved and called Border Patrol. Agents arrived 45 minutes later. When an agent then called 911 to report the body, he said it was "probably one of the migrants crossing through there," seemingly unaware that Morales had been moving near the agency's surveillance systems earlier that day. Morales had died just 360 feet from the closest surveillance tower.

The US has spent billions on surveillance towers, but hundreds of people have died near them. Here's how to strengthen federal oversight. We found cases of people who walked undetected through areas surveilled by advanced, AI-enabled towers and later died nearby, where their bodies remained unnoticed for weeks or months. These cases reflect systemic flaws in the virtual wall: broken towers, algorithms that failed to detect people, and agents who simply did not respond to alerts. This story is part of Dying on Camera, a collaboration between and . Each of these deaths should have raised alarms inside US Customs and Border Protection (CBP) about the flaws in the agency's surveillance network.

Records obtained by WIRED detail hundreds of allegations of Customs and Border Protection workers misusing internal tools to look up romantic interests and track colleagues' cell phones. Internal records obtained by WIRED reveal how, for years, United States Customs and Border Protection employees and contractors were accused of abusing sensitive government databases for reasons that had nothing to do with their jobs. The records contain hundreds of allegations of misuse of law enforcement databases, including federal agents querying data to look up romantic interests, monitor family members, expose various personal information and, in some cases, provide intelligence to suspected smugglers or drug-trafficking organizations. Acquired through Freedom of Information Act requests to CBP's Office of Professional Responsibility and the Department of Homeland Security's Office of Inspector General, the records reveal the breadth of alleged database abuse by CBP employees spanning more than a decade. As immigration and border authorities expand their surveillance through facial recognition, license plate readers, mobile-device searches, and commercially purchased location information generated by ordinary apps, the sheer range of these records, which date from 2009 through 2022, highlights how US residents can be--and have been--targeted by federal government employees with access to highly sensitive data and powerful tools.

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Backward propagation of errors (backpropagation) is a method to minimize objective functions (e.g., loss functions) of deep neural networks by identifying optimal

Immigration agents have used Mobile Fortify to scan the faces of countless people in the US--including many citizens. On Wednesday, the Department of Homeland Security published new details about Mobile Fortify, the face recognition app that federal immigration agents use to identify people in the field, undocumented immigrants and US citizens alike. The details, including the company behind the app, were published as part of DHS's 2025 AI Use Case Inventory, which federal agencies are required to release periodically. The inventory includes two entries for Mobile Fortify--one for Customs and Border Protection (CBP), another for Immigration and Customs Enforcement (ICE)--and says the app is in the "deployment" stage for both. CBP says that Mobile Fortify became "operational" at the beginning of May last year, while ICE got access to it on May 20, 2025.

US Customs and Border Protection is paying General Dynamics to create prototype "quantum sensors," to be used with an AI database to detect fentanyl and other narcotics. United States Customs and Border Protection is paying General Dynamics to create a prototype of "quantum sensors" alongside a "database with artificial intelligence " designed "to detect illicit objects and substances (such as fentanyl) in vehicles, containers, and other devices," according to a contract justification published in a federal register last week. "This database and sensor project will integrate advanced quantum and classical sensing technologies with Artificial Intelligence and ultimately deploy proven concepts and end products anywhere in the CBP environment," the justification document reads. "Under this requirement, CBP will take additional steps to enhance its ability to detect, and thus, significantly reduce the harms of illicit contraband entering the United States of America, thus bolstering national security." The document redacts the name of the company developing the prototype; however, contract details included in the federal register entry reveal that the justification is for a $2.4 million General Dynamics contract that has been public since December 2025.

'We Ain't Seen Nothing Yet'--Trump's Mass Deportations Will Only Grow From Here Militias and far-right extremists believed they would be central to Trump's mass deportation plans. When Donald Trump won a second term as US president a year ago, members of violent militias and far-right extremist groups who had spent years boosting the lie that the 2020 election was rigged were ready to assist the president with delivering on one of his main campaign promises: mass deportations. "I'm willing to help," Richard Mack, a former sheriff who founded the far-right Constitutional Sheriffs and Peace Officers Association, told WIRED at the time, claiming he was in touch with Tom Homan, the man Trump installed as his "border czar." Tim Foley, head of the Arizona Border Recon, which describes itself as a "non-government organization," also told WIRED he was in contact with administration officials. William Teer, then head of the far-right Texas Three Percenters militia, wrote a letter to Trump offering his help.

Backward propagation of errors (backpropagation) is a method to minimize objective functions (e.g., loss functions) of deep neural networks by identifying optimal sets of weights and biases. Imposing constraints on weight precision is often required to alleviate prohibitive workloads on hardware. Despite the remarkable success of backpropagation, the algorithm itself is not capable of considering such constraints unless additional algorithms are applied simultaneously. To address this issue, we propose the constrained backpropagation (CBP) algorithm based on the pseudo-Lagrange multiplier method to obtain the optimal set of weights that satisfy a given set of constraints. The defining characteristic of the proposed CBP algorithm is the utilization of a Lagrangian function (loss function plus constraint function) as its objective function. We considered various types of constraints -- binary, ternary, one-bit shift, and two-bit shift weight constraints. As a post-training method, CBP applied to AlexNet, ResNet-18, ResNet-50, and GoogLeNet on ImageNet, which were pre-trained using the conventional backpropagation. For most cases, the proposed algorithm outperforms the state-of-the-art methods on ImageNet, e.g., 66.6\%, 74.4\%, and 64.0\% top-1 accuracy for ResNet-18, ResNet-50, and GoogLeNet with binary weights, respectively. This highlights CBP as a learning algorithm to address diverse constraints with the minimal performance loss by employing appropriate constraint functions.

Federal records show CBP is moving from testing small drones to making them standard surveillance tools, expanding a network that can follow activity in real time and extend well beyond the border. US Customs and Border Protection is quietly doubling down on a surveillance strategy built around human-portable drones, according to federal contracting records reviewed by WIRED. The shift is pushing border enforcement toward a distributed system that can track activity in real time and, critics warn, may extend well beyond the border. New market research conducted this month shows that, rather than relying on larger, centralized drone platforms, CBP is concentrating on lightweight uncrewed aircraft that can be launched quickly by small teams, remain operational under environmental stress, and relay surveillance data directly to frontline units. The documents emphasize portability, fast setup, and integration with equipment already used by border patrol.