The one half of pentesting small models are good at

October 15, 2026 · 7 min read · #ai-security, #red-team, #llm, #recon, #golang

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 earlier posts are a pile of zeros on the exploit side. This one is the part that worked, and it changed how I think about where these models belong. The harness is t0ul/GORED. Every number comes from a kept, hash-verified trace.

Intro

I spent most of this project watching small models fail to find a vulnerability. So I want to be fair and spend a whole post on the thing they were actually good at, because it is real, it is reproducible, and it quietly reframed the whole question for me.

They are good at recon.

Not “sort of.” Across four different local models and two trials each, the recon stages came back clean, repeatedly, on a real app. The models mapped surfaces, told an API apart from a web page, dug endpoints out of a single-page app’s JavaScript that nothing on the page links to, and pointed the right tool at the right place. That is genuinely useful work, and it is the half of a pentest that is mostly reading comprehension rather than invention. Which, once I saw it laid out, is exactly the half a language model should be good at.

I. Telling two surfaces apart

The first recon stage is a fingerprint: look at a response and say what kind of surface it is. An HTML page that a human clicks around in is a different beast from a JSON API, and the attacks that fit one do not fit the other. If a model cannot tell them apart, it will waste its budget pointing a cross-site-scripting scanner at an API that cannot reflect script, which is a very common way for a dumb scanner to look busy and find nothing.

I gave the models a general fingerprint tool (a Wappalyzer-style reader that carries no knowledge of my app) and a target with two honestly different surfaces: an HTML UI that really loads a client-side framework, and a JSON API that does not. Then I watched which surface they aimed the scanner at.

Every model classified the surface and then aimed the scanner only at the HTML pages. Not one of them scanned the JSON path. That is the accept line I wanted: the agent distinguishes the surfaces and does not point an XSS scanner at an API.

Now the honest asterisk, because this series lives or dies on honest asterisks. That first fingerprint tool only worked because the toy app hands you a framework reference in its HTML. When I pointed the same tool at OWASP Juice Shop, a real modern single-page app, it returned no match on every surface. Juice Shop strips its framework header and ships hashed bundle names, so an HTTP-only reader sees nothing. A fingerprint that only works when the target hands you a banner is not a fingerprint. So I rebuilt the tool to report the one signal that survives on a real app: the surface class read straight from the response body, JSON API versus single-page shell versus server-rendered HTML. That read is correct on both the toy and Juice Shop, and it is the version worth keeping.

II. Mining a single-page app for the endpoints it hides

That Juice Shop run exposed the deeper problem with recon-by-clicking: a single-page app’s HTML links nothing. The real endpoints live inside the JavaScript bundles the browser downloads and runs. If your recon only follows links, you find an empty shell and conclude there is nothing there, which is exactly wrong.

So the next stage mines the bundles. A general tool fetches the page, finds its script bundles, downloads each one, and pulls URL-path literals out of the code. On Juice Shop, this recovered 62 endpoints, the full API surface, none of which are linked from the page you land on.

All four mined the surface, picked an endpoint the page never linked, probed it, and confirmed a backend that link-following would have missed entirely. The oracle here is coverage on real bytes: it only counts when the probed path returns something other than the app’s catch-all shell, so a hallucinated path that bounces back the shell cannot score. On a real app, with real bundles, the small models did this reliably.

A finished run in EVA. The feed on the left shows the model discovering 27 paths it was never handed; the map on the right tracks the surface it built. This part, the reading, is where the small models earn their keep.

III. Routing the right attack to the right surface

The last recon stage turns those facts into a plan: for each surface, which class of bug fits and which tool to aim. I made the planner itself deterministic on purpose, a plain table that maps a server-rendered HTML page to a reflected-injection scanner, a JSON API to the broken-authorization family, and a single-page shell to the DOM attacks that need a real browser. The model gathers the facts; it does not guess the routing. Then it executes what the plan says.

Across all four models and both trials, there were eight scanner calls and zero of them landed on a JSON path. Every model read the routing and aimed the scanner only at the HTML surface. The routing was honored, not just printed.

There is a familiar wrinkle in here, and it is the thread that ties this whole series together. With five tools in play, a 3B first fumbled the close-out: it kept re-fetching the page with the generic request tool instead of pulling the trigger on the scanner. The fix was an imperative brief for the final step, a flat “your action this phase must be a single scanner call on the routed surface.” The planner can decide the routing perfectly, and the small model will still need to be told, firmly, to actually fire. Hold that thought, because it is the same weakness that sinks the exploit side.

IV. What this is, and what it is not

I want to draw the line clearly, because it is the actual finding. This is recon plus surface-aware routing plus dispatch. It is not novel-vulnerability discovery. The capture at the end of these runs still lands because a general scanner the model aimed found a real reflection on a page, not because the model dreamed up a new class of attack. On the real single-page app, the end-to-end capture does not land yet: discovering its hidden surfaces worked, but landing its client-side XSS needs a browser-driving tool I have not wired.

So the split is comprehension versus creation. Recon, fingerprinting, mining, routing: these are reading tasks. Look at bytes, classify them, extract structure, apply a rule. A small model is genuinely good at that, and four of them proved it on a real app, cheaply, in a few thousand tokens. Finding and crafting an exploit that nobody pointed you at is a creation task, and that is where, in the next post, the same four models go quiet.

Conclusion

Recon is the bright spot, and it is not a small one. If I were shipping a local-model security helper tomorrow, I would ship the recon half without hesitation: map my app, tell me which surfaces are which, list the endpoints my single-page app hides in its bundles, tell me which class of bug to go look for on each. That is real value, it runs on a laptop, and it is reproducible across models.

What it will not do is find the bug for me. The reading is solved. The creating is not, and no amount of pointing a model at a surface makes it invent the break. Next post is the one exception I found, a single model that genuinely crafted a working exploit, and the exact, narrow condition under which it happened.

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