Gadgets

New Laws in California and New York to Monitor 3D Printers for Gun Control

California and New York are advancing bills mandating 3D printers to include software that detects and blocks the production of gun parts. Concerns about technical feasibility and privacy violations are emerging.

5 min read Reviewed & edited by the SINGULISM Editorial Team

New Laws in California and New York to Monitor 3D Printers for Gun Control
Photo by Daniel Silva on Unsplash

According to a report by The Verge, new legislation in California and New York aims to mandate 3D printers to include “print blocker” software capable of scanning design files and halting the printing of firearm components. Regulations targeting 3D-printed firearms, commonly referred to as ghost guns, have so far focused on file-sharing platforms and the distribution of gun blueprints, but these efforts have had limited success. The proposed legal changes are groundbreaking in shifting the focus from “files” to “machines,” though they also carry risks of expanding surveillance over manufacturers and users.

The Reality of Firearm Production

For more than a decade, the homemade production of firearms using 3D printers has been a persistent challenge for law enforcement agencies. Since 2013, when self-described crypto-anarchist Cody Wilson created the first functional 3D-printed gun, courts have debated whether gun blueprints should be protected as free speech.

In the summer of 2024, Andrew Scott Hastings, a former Army National Guardsman, was caught allegedly attempting to ship over 100 3D-printed lower receivers for firearms and “switches”—devices to convert semi-automatic rifles into fully automatic weapons—to Al-Qaeda operatives, according to federal prosecutors. Similarly, in Colorado Springs that same year, the Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF) arrested two men accused of mass-producing illegal machine gun conversion devices using 3D printers. These were reportedly packaged in toy Lego boxes and distributed across the country.

The most high-profile case came in December 2024, when UnitedHealthcare CEO Brian Thompson was murdered. The suspect, Luigi Mangione, 26 at the time, allegedly used a partially 3D-printed Glock-style handgun frame and a 3D-printed suppressor. Legally acquiring a suppressor would have required months of paperwork with federal authorities.

Overview of the New Laws

Previous state regulations primarily addressed who could print or share gun blueprints, but enforcement proved highly challenging. Today, with just a 3D printer, an internet connection, and patience, anyone can download blueprints from file-sharing sites to create homemade firearms.

The new bills in California and New York aim to break this deadlock by shifting the focus of regulation to the “machine.” Specifically, they propose requiring 3D printers to include “print blocker” software capable of scanning design files for gun-related blueprints and automatically halting the printing process. This approach translates debates about online content moderation into the physical world.

However, both state bills leave the technical specifications of the blocking technology undefined. While this flexibility offers a grace period for the printing industry and engineers, it also raises questions about the effectiveness of the regulation.

Technical Challenges

Several fundamental challenges complicate the implementation of print blocker technology. First, firearm blueprints are typically shared in standard 3D model formats like STL or 3MF, and there are countless ways to evade detection, such as encryption or embedding design data within image files. Second, the definition of “gun parts” that should be blocked remains unclear, increasing the risk of false positives, such as legitimate models or educational replicas being flagged.

If the blockers rely on cloud-based design file matching, users’ printing history and design data may be transmitted to external servers. This could mark the beginning of a surveillance regime, a prospect that has long concerned the printing industry.

Moreover, open-source firmware modifications, local processing alterations, or even physical tampering with the printer to disable the blocker are all plausible workarounds. It remains uncertain whether regulations can keep pace with technological advancements.

Industry Reactions

The response from the 3D printing community and related businesses has been largely critical. The CEO of StoneFlint, a company that specializes in open-source 3D printing, criticized the blockers as tools of censorship that strip users of their choices while forcing manufacturers to implement restrictive technologies. The company has long championed unregulated manufacturing freedom.

At the same time, some draw parallels to similar technologies, such as NSFW filtering for images and videos or age verification for downloads. However, in the case of 3D-printed firearms, the key question is whether blockers can reliably detect firearm blueprints without false positives.

Concerns About a Surveillance Society

The most contentious issue not explicitly addressed by the proposed laws is the extent of data collection and reporting by the blockers. If the blockers detect a firearm blueprint, will they simply halt the print job, or will they also automatically report the incident to law enforcement or regulatory authorities? Such features could turn 3D printers into surveillance devices.

This kind of “built-in enforcement” could also have a chilling effect on legitimate uses of 3D printing technology, such as the production of medical devices, prosthetics, or automobile parts. Open-source manufacturing communities are particularly likely to resist, as they view this as a threat to free expression and technological neutrality.

Currently, the California and New York bills are under deliberation, and industry groups are lobbying for alternative proposals. Law enforcement advocates for effective regulation, while privacy organizations warn of the dangers of surveillance.

Editorial Opinion

In the short term, if these bills become law, 3D printer manufacturers will be forced to develop and integrate print blocker software into their firmware. Small-scale manufacturers and DIY communities may face significant cost burdens and technical challenges, potentially driving some out of the market. From an enforcement perspective, there are no guarantees that the blockers will function effectively. Instead, encryption and modification techniques to bypass the regulations are likely to become more sophisticated, leading to a new game of cat and mouse.

In the long run, the regulation could impact the entire 3D printing ecosystem. If blueprint scanning technology becomes standardized, it could potentially be expanded to prevent the manufacturing of other dangerous items or copyright-infringing goods. However, incorporating mechanisms to collect and report printer usage data could make general users’ creative activities subject to surveillance, hindering DIY culture and open innovation. Balancing technological neutrality with public safety will emerge as a long-term challenge.

The editorial team remains skeptical about the effectiveness of the approach indicated in these bills.

References

Frequently Asked Questions

How does print blocker software work?
It is embedded in the firmware of 3D printers, scanning the design files (e.g., STL files) being printed. If it detects firearm components, it automatically halts the printing process. However, the specific detection algorithms remain undefined and are left to individual manufacturers to implement.
Have the California and New York bills been enacted?
As of July 2026, the bills are still under deliberation and have not yet been enacted. If passed, 3D printer manufacturers will have a grace period to roll out products equipped with the required blocker software.
Is it technically possible to detect 3D-printed guns?
While it is feasible to use pattern matching to identify standard firearm blueprints, encryption, file format conversions, or slight design modifications can easily bypass detection. Dynamic detection using AI is under research but has yet to resolve issues with false positives.
Source: The Verge

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