Matt's Homepage

After Nicholas Carlini’s famous talk at Black Hat conference this year, his predictions have become shockingly real. Critical bugs are being found at an alarming rate. This means patches are also coming at a never before seen rate – and system developers and software engineers must now deal with this historic flood of patches. How many? Most companies would release in the low double digits of bug fixes in a given period. Now we’re seeing hundreds – in mere days.

• Over 600 CVEs were identified in the Security Updates Guide across all of Microsoft’s products for July 2026 alone.
• Oracle dropped 1449 security patches in one week.
• 432 Linux kernel CVE’s were published in just one 24 hour period

None of this appears to be slowing – and we have AI to thank for it. AI is able to find bugs at a rate never before seen. It’s a reminder to the software development world, and especially security specialists, that they have been leaving a LOT of bugs and issues in some of the most secure software (BSD) that was undiscovered for decades.

Now people are talking about how to deal with astronomical numbers of patches coming out every week. It’s definitely something your production pipeline must be prepared for – because this will likely be the new normal for the rest of this year.

Recent security research demonstrates that open-weight AI models can be subtly poisoned or backdoored using a remarkably small number of malicious training examples. Attackers inject specific trigger phrases or corrupted data fragments into a fine-tuning or training set. Standard safety and performance benchmarks remain entirely normal, masking the underlying flaw. When a user’s prompt contains the hidden trigger, the model executes the malicious behavior, such as writing vulnerable code or opening a backdoor into the system.

This works well because it’s not like scanning for a virus, numerical model weights cannot be easily disassembled or audited. Downloading open weights locally keeps user data private, but it exposes organizations to black-box supply chain tampering.

How easy is it to do this? Apparently shockingly easy. Katie Paxton-Fear, a lecturer in cybersecurity at Manchester Metropolitan University and staff security advocate at Semgrep, managed to install a backdoor in an open-weight AI model in about an hour for less than $100.

It only took ten training examples for the code output by the model to become reliably vulnerable to remote code execution, even for novel prompts and domains, she claims. And the larger the model, the easier it was to poison.

This video shows you how to load models in Unsloth Studio – and how to share them locally and across the network. Using Unsloth Studio, you can download and use models directly (much like ollama – but it appears to be much faster since it seems to use llama.cpp under the hood. You can also host these models either locally on the same machine, or set up an external port and connect Claude Code or other cli from another machine.

Hosting a model in Unsloth Studio so another machine can connect to it involves a few easy steps. First, install Unsloth Studio on your host machine (with the big GPU and lots of memory). Then download and start the desired model in Unsloth Studio. After that, go to Settings -> API. Create a new API key. On your client system set up a batch file or environment variables then run claude from the command line:

> set ANTHROPIC_BASE_URL=http://<ip address of your server>:<port number>
> set ANTHROPIC_AUTH_TOKEN=<your unsloth api key - usually: sk-unsloth-d5134......>
> set ANTHROPIC_MODEL=Qwen3.8

> claude --model Qwen3.8 %*