Claude Opus Reverse-Engineered PC Accessories in Hours — and That Raises a Security Warning

An Amazon security engineer has demonstrated how dramatically AI can speed up hardware reverse engineering.

Senior Security Engineer Chaz Schlarp used Claude Opus 5 to analyze and modify firmware from several PC accessories, including an Insta360 webcam, Shure microphone, ASUS monitor, and Elgato devices. What previously could require substantial specialist effort was completed with roughly 13 hours of AI work and 98 prompts.

The experiment had a useful side—giving owners more control over their hardware—but it also revealed a bigger concern: AI could make sophisticated firmware attacks easier and cheaper to develop.

Key Takeaways

• Claude Opus 5 helped reverse-engineer five PC peripherals.
• The devices included products from Insta360, Shure, ASUS, and Elgato.
• The experiments took about 13 hours of AI processing and 98 prompts.
• AI helped analyze firmware and understand undocumented device behavior.
• Several devices had weak firmware-integrity protections.
• The webcam’s recording indicator could be disabled through firmware modification.
• A microphone could appear muted while still capturing audio.
• The research suggests AI could significantly lower the expertise and time required for hardware reverse engineering.

What Did the Amazon Security Engineer Do With Claude?

Schlarp wanted to see how effectively an AI agent could help him understand and modify hardware he owned.

His process involved obtaining firmware and official update utilities, placing them inside a reverse-engineering environment, and giving Claude Opus 5 specific goals.

Claude then handled much of the analysis required to understand how each device worked.

The experiment covered five peripherals:

• Insta360 Link webcam
• Shure MV7 microphone
• ASUS ROG Swift PG42UQ monitor
• Elgato Cam Link 4K
• Elgato Key Light Mini

The results demonstrated both practical customization opportunities and serious security weaknesses.

What Could Claude Do With the ASUS Monitor?

One motivation was surprisingly ordinary.

Schlarp was frustrated by a recurring pixel-cleaning notification on his ASUS ROG Swift PG42UQ OLED monitor.

With Claude’s help, he investigated the monitor’s firmware and found ways to gain more control over its functions. He also developed a Linux shell script capable of accessing features normally exposed through ASUS’s Windows software, including the hardware crosshair and FPS counter.

This demonstrates the positive side of AI-assisted reverse engineering.

Hardware features that were previously inaccessible because of limited manufacturer software support could potentially become usable on other operating systems.

What Happened With the Webcam?

The Insta360 webcam revealed the more concerning side of the experiment.

Schlarp demonstrated that firmware modification could disable the webcam’s recording indicator light.

That means a compromised webcam could potentially record while giving the user no obvious visual indication that the camera is active.

The technique itself isn’t a new security concept.

What is different is how much AI can reduce the time and manual work required to investigate individual devices.

What Did Claude Find in the Microphone?

The Shure microphone produced another worrying result.

The research uncovered a plaintext command shell and showed that the device’s mute indicator could be manipulated.

In practice, the microphone could potentially display its mute light while the microphone itself remained active.

For users, hardware indicators such as microphone mute lights and webcam LEDs are important trust signals.

If firmware affects the reliability of these indicators, users might mistakenly think the device is inactive when it’s actually still running. 

Did the Devices Have Firmware Security?

Not much, according to the experiment.

Schlarp found that most of the tested devices lacked strong protections preventing modified firmware from being installed.

The Elgato Key Light Mini did include a firmware verification mechanism, but the researcher was still able to bypass it.

That matters because peripherals are effectively small computers connected directly to larger computers.

They have processors, firmware, data connections, and update mechanisms—all of which can potentially become attack surfaces.

Why Is AI Changing Reverse Engineering?

Reverse engineering hardware traditionally requires specialized expertise and considerable time.

Researchers need to inspect firmware, understand undocumented protocols, identify protections, test modifications, and repeatedly analyze the results.

AI agents can automate significant portions of that work.

In this experiment, Claude performed much of the repetitive analysis while the researcher provided objectives and oversight.

Across the five devices, the work involved about 13 hours of AI processing and fewer than 100 prompts.

That reduction in effort is what makes the experiment important.

Is AI-Assisted Hardware Hacking Always Bad?

No.

Reverse engineering can have legitimate uses.

Developers might use it to restore support for abandoned hardware, create Linux drivers, understand devices they own, improve interoperability, or unlock functionality manufacturers haven’t exposed.

AI could make that work much easier.

The problem is that the same capability can also be used maliciously.

The technology doesn’t inherently distinguish between someone trying to improve their own webcam and someone attempting to compromise another person’s device.

Why Does This Create a Cybersecurity Risk?

Historically, developing custom firmware attacks against individual hardware models could require enough specialist work that they were mainly practical for highly targeted attacks.

AI changes those economics.

If an agent can inspect unfamiliar firmware and rapidly identify ways to modify it, attackers may be able to target a much larger variety of devices.

Schlarp said the results concern him as a security professional because devices attached to computers may increasingly need to be treated as potential locations for malicious firmware implants.

The risk isn’t necessarily that Claude itself will attack people’s computers.

The concern is that AI-assisted reverse engineering reduces the effort required to discover and develop hardware attacks.

Could Malware Eventually Do This Automatically?

This is the more serious long-term possibility raised by the research.

Imagine malware that infects a computer and then examines the hardware connected to it.

An AI-powered system could potentially identify the webcam, microphone, monitor, USB devices, or nearby IoT equipment, analyze their firmware, search for weaknesses, and attempt to expand the infection.

Schlarp suggested that a future self-replicating system could perform reconnaissance and use intelligent command-and-control infrastructure to investigate connected accessories and other nearby equipment.

That remains a forward-looking security scenario rather than something demonstrated as an active widespread threat.

But AI makes the concept more plausible.

What Should Hardware Manufacturers Learn From This?

The clearest lesson is that firmware security needs more attention.

Manufacturers can no longer assume that obscure firmware or undocumented protocols provide meaningful protection.

As AI makes reverse engineering faster, hardware vendors have stronger reasons to implement protections such as secure firmware verification, signed updates, stronger authentication, and carefully designed update mechanisms.

Security models built around the assumption that firmware is simply “too difficult to reverse engineer” are becoming increasingly risky.

Why This Experiment Matters

The most interesting part of the experiment isn’t that one engineer modified a webcam or monitor.

Security researchers have been reverse-engineering hardware for decades.

The important change is speed.

AI can help compress specialized work that might previously have required extensive manual investigation into hours of guided analysis.

That creates exciting possibilities for hardware customization and interoperability.

But it also means attackers may eventually gain the same productivity boost.

Conclusion

Claude Opus 5 helped an Amazon security engineer reverse-engineer five PC accessories in roughly 13 hours of AI work, revealing how powerful agentic AI has become for hardware analysis.

The experiment showed legitimate benefits, including better hardware control and support for features outside manufacturers’ intended software environments.

But it also exposed weak firmware protections and demonstrated how trusted indicators such as webcam recording lights and microphone mute LEDs could potentially be manipulated.

The larger cybersecurity lesson is straightforward: AI isn’t creating firmware vulnerabilities from nothing—it is making existing weaknesses much faster to discover and exploit.

As AI-assisted reverse engineering improves, manufacturers may need to start treating peripheral firmware security with the same seriousness as the software running on the PC itself.

FAQs

1. What did the Amazon security engineer do with Claude Opus?

Amazon Senior Security Engineer Chaz Schlarp used Claude Opus 5 to help reverse-engineer and modify firmware from five PC peripherals, including a webcam, microphone, monitor, and Elgato devices.

2. How long did the AI-assisted reverse engineering take?

Across five devices, Claude performed roughly 13 hours of work with 98 prompts from the researcher.

3. Which devices were tested?

The research covered an Insta360 Link webcam, Shure MV7 microphone, ASUS ROG Swift PG42UQ monitor, Elgato Cam Link 4K, and Elgato Key Light Mini.

4. Could Claude disable a webcam recording light?

The experiment demonstrated that modified firmware could disable the Insta360 webcam’s activity indicator, meaning the camera could potentially operate without the usual visual signal.

5. Why is AI-assisted reverse engineering a security concern?

AI can automate parts of firmware analysis that previously required substantial specialist time. That could lower the cost and effort required to discover hardware vulnerabilities or develop malicious firmware modifications.

6. Does this mean Claude can automatically hack PC accessories?

No. The experiment involved a security researcher providing devices, firmware, tools, objectives, and oversight. It demonstrates how AI can accelerate expert reverse-engineering work, not autonomous widespread attacks.

7. What should hardware manufacturers do?

Manufacturers may need stronger firmware-integrity protections, authenticated updates, secure boot mechanisms, and better safeguards against unauthorized firmware modifications as AI makes reverse engineering easier.