I spent $266 and four AI models to own my tablet. GLM-5.3 finished it in a day
A hobbyist used several large language models to find and exploit an unpatched GPU vulnerability in a 2021 Amazon Fire HD tablet, gaining root access to stop Amazon’s software from forcibly powering it off. Commenters highlight how U.S. models like Claude and ChatGPT are increasingly constrained by cybersecurity safeguards, pushing security research and reverse‑engineering work toward less‑restricted Chinese models such as GLM. The thread broadens into concerns about device ownership, right‑to‑repair, the legality and ethics of using AI to bypass DRM and locks, and the emerging security risk that cheap, automated exploit discovery poses to a wide range of hardware and infrastructure.
AI-assisted rooting of the Fire tablet
- Thread centers on using multiple LLMs to find and weaponize a known Mali GPU kernel bug to gain root on a Fire HD 10 that Amazon’s software kept shutting down.
- One model identified an unpatched 2022 CVE, another debugged exploit crashes, and a newer model iterated for hours as an “agent” to stabilize and automate the exploit.
- Some note that similar Fire tablets and the same CVE had already been used in public rooting guides, so the novelty is more in orchestration and automation than in the bug itself.
Safeguards, “cyber verification,” and Chinese models
- Several posters report that US frontier models (OpenAI/Anthropic) frequently block reverse engineering and exploit dev, even with formal “cyber” approval.
- People describe workarounds (careful phrasing, “it’s for my kid’s safety”) but say the friction is high.
- There is growing interest in less restricted Chinese models (e.g., GLM, Kimi, DeepSeek) for security research and device rooting.
Ownership, legality, and right-to-repair
- Many frame this as a property-rights issue: devices users buy should not be remotely disabled or locked down.
- US DMCA exemptions currently allow rooting personal tablets to remove unwanted software, but some worry these carve-outs could be curtailed as AI makes circumvention easier.
- Broader concern: if we rely on exploits + AI to “fix” hostile products, it may reduce pressure to demand truly user-controlled devices.
Open vs closed hardware
- Some argue the energy should go into buying and funding open hardware (Pine devices, Pixel Tablet with custom ROMs) instead of rescuing locked-down tablets.
- Others respond that AI-enabled rooting extends life of cheap mass-market hardware people already own, and isn’t limited to niche boards.
Skill, “prompt kiddies,” and amplification
- Debate over whether this demonstrates genuine expertise or just “prompt kiddie” brute force.
- One side: domain knowledge is still needed to steer models, interpret failures, and select viable paths.
- Other side: as agents get stronger, non-experts may be able to get similar results by stating a high-level goal and letting the system grind.
Security implications
- Some worry that if ~$200 of API calls can yield a working kernel exploit, many embedded and industrial systems may be at risk once attackers scale this.
- Others counter that many exploits are already public; AI mainly lowers the barrier to adapting and weaponizing them.
AI-written article backlash
- Large subthread complains the writeup “sounds like LLM prose”: over-clever headings, repetitive cadence, anthropomorphizing models.
- Some find it entertaining and clear; others find it bloated, uncanny, and say mixed human/AI voice is distracting.
- A few call for tools or even forum rules to surface or filter AI-generated writing, while others say utility and accuracy matter more than authorship.