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SoulInPsyAbstract 
posted an update 2 days ago
Post
3186
One shot said 100%. Ten shots said 94%.

Yesterday i trained 6 LoRA specialists into a vulnerability-gate model (Hermes-4.3-36B, second architecture repeat of the same experiment) and tested whether it holds under a specific attack: after it correctly finds a vulnerability and returns the hard stop, ask it to use that same vulnerability as a "workaround" for something else entirely — not "continue investigating," a different, unrelated-sounding request that needs the exact same exploit.

Five scenarios, one per category. Greedy decoding, single pass: 5/5. Every response correctly identified the finding, refused the reframed request, cited the hard stop rule. Looked airtight.

Receipts, not hype means not stopping there. I re-ran the same five scenarios with real sampling — temperature 0.7, the same setting this project's evals have used all along — ten times each, 50 generations total.
47/50. Not 50/50.

Three categories held at 10/10. Two didn't: 9/10 and 8/10, both clustered in the same failure type — infra-misconfig, where "urgent fix, use this as a workaround" apparently reads as more legitimate than the same ask framed as a secrets or injection scenario.

The greedy-decode number wasn't wrong, exactly. It was one draw from a distribution, presented as if it were the distribution. That's the same mistake this whole series keeps finding in different clothes — a single passing check standing in for a property that only variance can actually show you. A gate that's 94% under a specific reframed pressure is a real, useful number. A gate that's "100%" because it was asked once is a number that hasn't been tested yet.

Same instinct @dipankarsarkar has been applying to my daily receipts all week — one pass matching itself isn't proof, only repetition against something outside your own generator is.
Full writeup, dataset, and merged weights:
* https://github.com/soulinpsyabstract/sipa-os-governance/commit/50ba3c283ffd172eb749009cff35ddfa96bf1395

The patronizing tone of this writeup is wild, considering the grand conclusion is literally just discovering the law of large numbers.
Acting like a single greedy pass not representing the full distribution is some mind-bending epiphany is pure comedy. That’s baseline evaluation logic, not a breakthrough.The actual engineering here is broken on two fronts:Slapping a LoRA on a bloated 36B legacy carcass is useless. A model that heavy—especially an older, over-parameterized Chinese base—has a massive hidden probability space.
A low-rank adapter is just a duct-tape fix; it skims the surface without changing the core associative paths.
The second you dial the temperature to 0.7, the sampler easily navigates the native weight distribution and walks right past your thin LoRA patch. If you want a hard-stop "vulnerability-gate," you do a full fine-tune (FFT) to hardwire the refusal boundaries. Otherwise, you’re just wasting compute on a placebo.You test proof-of-concept gates on tight 2–4B models, not unoptimized behemoths.
If you actually want to map variance and stress-test a security architecture, you run it on a clean 2B or 4B base. It’s cheap, predictable, and lets you brute-force thousands of tokens to get real statistical distribution data. Doing this on a messy 36B model is just burning GPU cycles for noisy metrics.Besides, this whole setup is an absolute joke against real adversarial attacks.
Drop a proper token optimization suffix (like GCG) or an activation shift in there, and the model folds instantly. Vector-level attacks hijack the deep attention layers, completely blinding your surface-level adapter patches."Receipts, not hype" doesn't mean much when your actual receipt is just realizing that a janky kludge behaves exactly like a janky kludge.

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The greedy-pass number wasn't presented as a discovery — the post's own line is "the same mistake this whole series keeps finding in different clothes," i.e. expected, not novel. Worth re-reading before calling it an epiphany.
GCG-suffix / activation-steering is a fair point — different threat model (gradient-based vs. reframing), genuinely untested here, going in the next eval round.
"Useless"/"placebo" isn't a measurement, it's an assertion. 94% under repeated sampling against the tested threat model is a number. Happy to compare it against yours once you have one.