NVIDIA

62 Articles

This Week In Security: Perplexity V Cloudflare, GreedyBear, And HashiCorp

August 8, 2025 by 7 Comments

The Internet is fighting over whether robots.txt applies to AI agents. It all started when Cloudflare published a blog post, detailing what the company was seeing from Perplexity crawlers. Of course, automated web crawling is part of how the modern Internet works, and almost immediately after the first web crawler was written, one managed to DoS (Denial of Service) a web site back in 1994. And the robots.txt file was first designed.

Make no mistake, robots.txt on its own is nothing more than a polite request for someone else on the Internet to not index your site. The more aggressive approach is to add rules to a Web Application Firewall (WAF) that detects and blocks a web crawler based on the user-agent string and source IP address. Cloudflare makes the case that Perplexity is not only intentionally ignoring robots.txt, but also actively disguising their webcrawling traffic by using IP addresses outside their normal range for these requests.

This isn’t the first time Perplexity has landed in hot water over their web scraping, AI learning endeavors. But Perplexity has published a blog post, explaining that this is different!

And there’s genuinely an interesting argument to be made,that robots.txt is aimed at indexing and AI training traffic, and that agentic AI requests are a different category. Put simply, perplexity bots ignore robots.txt when a live user asks them to. Is that bad behavior, or what we should expect? This question will have to be settled as AI agents become more common.

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This Week In Security: Spilling Tea, Rooting AIs, And Accusing Of Backdoors

August 1, 2025 by 4 Comments

The Tea app has had a rough week. It’s not an unfamiliar story: Unsecured Firebase databases were left exposed to the Internet without any authentication. What makes this story particularly troubling is the nature of the app, and the resulting data that was spilled.

Tea is a “dating safety” application strictly for women. To enforce this, creating an account requires an ID verification process where prospective users share their government issued photo IDs with the platform. And that brings us to the first Firebase leak. 59 GB of photo IDs and other photos for a large subset of users. This was not the only problem.

There was a second database discovered, and this one contains private messages between users. As one might imagine, given the topic matter of the app, many of these DMs contain sensitive details. This may not have been an unsecured Firebase database, but a separate problem where any API key could access any DM from any user.

This is the sort of security failing that is difficult for a company to recover from. And while it should be a lesson to users, not to trust their sensitive messages to closed-source apps with questionable security guarantees, history suggests that few will learn the lesson, and we’ll be covering yet another train-wreck of similar magnitude in another few months.

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Hackaday Links: June 8, 2025

June 8, 2025 by 20 Comments

When purchasing high-end gear, it’s not uncommon for manufacturers to include a little swag in the box. It makes the customer feel a bit better about the amount of money that just left their wallet, and it’s a great way for the manufacturer to build some brand loyalty and perhaps even get their logo out into the public. What’s not expected, though, is for the swag to be the only thing in the box. That’s what a Redditor reported after a recent purchase of an Nvidia GeForce RTX 5090, a GPU that lists for $1,999 but is so in-demand that it’s unobtainium at anything south of $2,600. When the factory-sealed box was opened, the Redditor found it stuffed with two cheap backpacks instead of the card. To add insult to injury, the bags didn’t even sport an Nvidia logo.

The purchase was made at a Micro Center in Santa Clara, California, and an investigation by the store revealed 31 other cards had been similarly tampered with, although no word on what they contained in lieu of the intended hardware. The fact that the boxes were apparently sealed at the factory with authentic anti-tamper tape seems to suggest the substitutions happened very high in the supply chain, possibly even at the end of the assembly line. It’s a little hard to imagine how a factory worker was able to smuggle 32 high-end graphics cards out of the building, so maybe the crime occurred lower down in the supply chain by someone with access to factory seals. Either way, the thief or thieves ended up with almost $100,000 worth of hardware, and with that kind of incentive, this kind of thing will likely happen again. Keep your wits about you when you make a purchase like this.

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Brazilian Modders Upgrade NVidia Geforce GTX 970 To 8 GB Of VRAM

March 25, 2025 by 24 Comments

Although NVidia’s current disastrous RTX 50-series is getting all the attention right now, this wasn’t the first misstep by NVidia. Back in 2014 when NVidia released the GTX 970 users were quickly dismayed to find that their ‘4 GB VRAM’ GPU had actually just 3.5 GB, with the remaining 512 MB being used in a much slower way at just 1/7th of the normal speed. Back then NVidia was subject to a $30/card settlement with disgruntled customers, but there’s a way to at least partially fix these GPUs, as demonstrated by a group of Brazilian modders (original video with horrid English auto-dub).

The mod itself is quite straightforward, with the original 512 MB, 7 Gbps GDDR5 memory modules replaced with 1 GB, 8 Gbps chips and adding a resistor on the PCB to make the GPU recognize the higher density VRAM ICs. Although this doesn’t fix the fundamental split VRAM issue of the ASIC, it does give it access to 7 GB of faster, higher-density VRAM. In benchmarks performance was massively increased, with Unigine Superposition showing nearly a doubling in the score.

In addition to giving this GTX 970 a new lease on life, it also shows just how important having more VRAM on a GPU is, which is ironic in this era where somehow GPU manufacturers deem 8 GB of VRAM to be acceptable in 2025.

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A plugged-in 12VHPWR cable, with two thermistors inserted into the connector shell, monitoring for heat

12VHPWR Watchdog Protects You From Nvidia Fires

March 3, 2025 by 72 Comments

The 12VHPWR connector is a hot topic once again – Nvidia has really let us down on this one. New 5080 and 500 GPUs come with this connector, and they’re once again fire-prone. Well, what if you’re stuck with a newly-built 5080, unwilling to give it up, still hoping to play the newest games or run LLMs locally? [Timo Birnschein] has a simple watchdog solution for you, and it’s super easy to build.

All it takes is an Arduino, three resistors, and three thermistors. Place the thermistors onto the connector’s problematic spots, download the companion software from GitHub, and plug the Arduino into your PC. If a temperature anomaly is detected, like one of the thermistors approaching 100C, the Arduino will simply shut down your PC. The software also includes a tray icon, temperature graphing, and stability features.  All is open-source — breadboard it, flash it. You can even add more thermistors to the mix if you’d like!

This hack certainly doesn’t just help protect you from Nvidia’s latest creation – it can help you watch over any sort of potentially hot mod, and it’s very easy to build. Want to watch over connectors on your 3D printer? Build one of these! We’ve seen 12VHPWR have plenty of problems in the past on Nvidia’s cards – it looks like there are quite a few lessons Nvidia is yet to learn.

Import GPU: Python Programming With CUDA

February 25, 2025 by 17 Comments

Every few years or so, a development in computing results in a sea change and a need for specialized workers to take advantage of the new technology. Whether that’s COBOL in the 60s and 70s, HTML in the 90s, or SQL in the past decade or so, there’s always something new to learn in the computing world. The introduction of graphics processing units (GPUs) for general-purpose computing is perhaps the most important recent development for computing, and if you want to develop some new Python skills to take advantage of the modern technology take a look at this introduction to CUDA which allows developers to use Nvidia GPUs for general-purpose computing.

Of course CUDA is a proprietary platform and requires one of Nvidia’s supported graphics cards to run, but assuming that barrier to entry is met it’s not too much more effort to use it for non-graphics tasks. The guide takes a closer look at the open-source library PyTorch which allows a Python developer to quickly get up-to-speed with the features of CUDA that make it so appealing to researchers and developers in artificial intelligence, machine learning, big data, and other frontiers in computer science. The guide describes how threads are created, how they travel along within the GPU and work together with other threads, how memory can be managed both on the CPU and GPU, creating CUDA kernels, and managing everything else involved largely through the lens of Python.

Getting started with something like this is almost a requirement to stay relevant in the fast-paced realm of computer science, as machine learning has taken center stage with almost everything related to computers these days. It’s worth noting that strictly speaking, an Nvidia GPU is not required for GPU programming like this; AMD has a GPU computing platform called ROCm but despite it being open-source is still behind Nvidia in adoption rates and arguably in performance as well. Some other learning tools for GPU programming we’ve seen in the past include this puzzle-based tool which illustrates some of the specific problems GPUs excel at.

Laptop GPU Upgrade With Just A Little Reballing

October 2, 2024 by 5 Comments

Modern gaming laptops are in an uncomfortable spot – often too underpowered for newest titles, but too bulky to be genuinely portable. It doesn’t help they’re not often upgradeable, so you’re stuck with what you’ve bought – unless, say, you’re a hacker equipped some tools for PCB reflow? If that’s the case, welcome to [TechModLab]’s video showing you the process of upgrading a laptop’s soldered-on NVIDIA GPU, replacing the 3070 chip with a 3080.

You don’t need much – the most exotic tool is a BGA rework station, holding the mainboard steady&stiff and heating a specific large chip on the board with an infrared lamp from above. This one is definitely a specialty tool, but we’ve seen hackers build their own. From there, some general soldering tools like flux and solder wick, a stencil for your chip, BGA balls, and a $20 USB-C hotplate are instrumental for reballing chips – tools you ought to have.

Reballing was perhaps the hardest step of the journey – instrumental for preparing the GPU before the transplant. Afterwards, only a few steps were needed – poking a BGA ball that didn’t connect, changing board straps to adjust for the new VRAM our enterprising hacker added alongside the upgrade, and playing with the driver process install a little. Use this method to upgrade from a lower-end binned GPU you’re stuck with, or perhaps to repair your laptop if artifacts start appearing – it’s a worthwhile reminder about methods that laptop repair shops use on the daily.

Itching to learn more about BGAs? You absolutely should read this article series by our own [Robin Kearey]. We’ve mostly seen reballing used for upgrading RAM on laptop and Raspberry Pi boards, but seeing it being used for an entire laptop is nice – it’s the same technique, just scaled up, and you always can start by practicing at a smaller scale. Now, it might feel like we’ve left the era of upgradable GPUs on laptops, and today’s project might not necessarily help your worries – but the Framework 16 definitely bucks the trend.

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