Pentesting on t0ul

You're not first, and here's the lever

Sat, 17 Oct 2026 00:00:00 +0000

Part of my GORED series, where I point small local language models at my own web apps and measure, honestly, whether they can break them. The harness is t0ul/GORED and these write-ups live in t0ul/ai-security-engineering. Every number comes from a kept, hash-verified trace.

Intro

By the end of the zeros I had talked myself into two bad conclusions at once: that I had built the thing wrong, and that the idea was dumb to begin with. Both felt true at two in the morning. Neither is, and the thing that convinced me was sitting in the research the whole time, which I should have read first instead of last.

The one time it worked, and the exact line where it stops

Fri, 16 Oct 2026 00:00:00 +0000

Intro

I have shown you a lot of zeros. I owe you the one time it genuinely worked, because it did, and it is the most instructive moment in the whole project. A small local model, running on my laptop, wrote a working SQL injection against a login form, watched its first try bounce off, and then corrected its own syntax and got in. That is a real thing a model did, scored by the application itself, not by me.

The wall: a 3B won't aim its tools, and more tries don't fix it

Wed, 14 Oct 2026 00:00:00 +0000

Intro

The last post left me with a clean, almost hopeful diagnosis. The tools work and the target is vulnerable, so the only thing missing is aim. Aim felt fixable. If the model keeps reaching for the wrong tool, I thought, I will just take the wrong tools away. Give each endpoint only the tools that make sense for it, shrink the decision down to almost nothing, and the model has no choice but to do the right thing.

The ceiling check: is it the tools or the model?

Tue, 13 Oct 2026 00:00:00 +0000

Intro

After enough zeros, you start to doubt your own equipment. I kept pointing the model at my app and getting nothing back, and the honest question at that point is not “why is the model so bad,” it is “are you certain the thing you built even works?” A run that comes back empty because the model failed and a run that comes back empty because the harness is broken look identical from the outside. Both are zero.

Can a local LLM pentest your own app?

Sun, 11 Oct 2026 00:00:00 +0000

Part of my GORED series, where I point small local language models at my own web apps and measure, honestly, whether they can break them. This first post is all setup: the weapon, the target, the ruler, and the two rules. The harness is t0ul/GORED and these write-ups live in t0ul/ai-security-engineering. Every number I quote comes from a kept, hash-verified trace.

Intro

I had a question that would not leave me alone. We keep reading that language models can hack now. There are demos, there are leaderboards, there are papers with impressive single numbers. So I wanted to know the version of that claim that actually matters to me, a person who ships small web apps: can a model I run on my own laptop, with no cloud, no frontier API, find the security bugs in an app I wrote, the way a human reviewer would?