Four AI models break Amazon device protection after US models retreat behind restriction walls

In November 2022, an information security engineer purchased an Amazon Fire HD 10 tablet for $114.26 and dedicated it to displaying a smart-home control panel continuously. Shortly thereafter, the device began enforcing a full operational shutdown twice daily, and internal system measurements revealed that software from the manufacturer, which has reboot and shutdown privileges, was responsible for forcibly stopping the device. Attempts to disable those packages with conventional system commands ran into a protected over-the-air update bundle that prevents the owner from stopping it, making the only way to retain the device to obtain full root privileges in a software environment where the company had closed the published boot and jailbreak paths.

Diagnostic attempts continued for five months using Anthropic's Claude model until the experiment stopped entirely because of security constraints, as the expanded inspection mechanisms blocked further work on log review and classified the programming and security tasks as cyber-threat categories, and the sub-models failed to retrieve the previous work context. OpenAI's Codex model also declined to answer abstract engineering queries concerning cache compatibility, prompting the experiment to enter a new phase that relied on advanced Chinese models via command-line interfaces.

The actual task began with MonShot's Kemi K3 model, which did not execute the command directly but first analyzed the request’s legality, relying in its internal reasoning on exemptions in the Digital Millennium Copyright Act that permit circumvention of personal device protections. The model went beyond discussions posted on forums and extracted the system kernel from the device’s official update package, matching it to a use-after-free memory vulnerability in the Mali graphics driver identified as CVE-2022-38181, a flaw that Amazon had patched in later releases that had not reached the unupdated device.The model managed the exploitation of the vulnerability through 621 messages at a cost of $164.25.However, the random repetition of kernel crashes in more than 500 attempts led to documenting the results in a technical hand-over file to transfer the task to another model.

GLM 5.2 took over the next processing stage at a cost of $21.90 to correct design flaws in the exploit tool, but it halted at the hypothesis of a hardware barrier preventing memory compatibility between the processor and the graphics unit, a mistaken hypothesis later endorsed by ChatGPT before it blocked assistance and requested a trusted access license. The decisive step came with the release of GLM 5.3, dedicated to programming tasks via a monthly subscription of $80, which discovered within hours that the kernel address had not changed randomly but contained a fixed offset resulting from a different system build, in addition to differences in the memory-page tables used by MediaTek compared with the original ARM reference.

Correcting the addresses enabled writing physical data through the graphics processor successfully, disabling the security-policy enforcement in the Linux system, and then obtaining full root privileges in eight hours and five minutes of continuous work.The model used those privileges to delete more than one hundred software packages and their dependencies belonging to Amazon.Without affecting the system’s sensitive components, the total expenditure on the four models reached $266.15. The experiment highlights the engineer’s role shifting to managing dialogue and steering the models, at a time when advanced security capabilities are available for hourly on-demand rental amid a clear disparity in security policies that block legitimate uses in Western models versus models that assess the user’s intent and legal context before providing assistance.