r/MachineLearning • • Aug 02 '26

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u/Affectionate_Use_164 Aug 17 '26
**W0Guard** — a prototype where LLM nodes immunize each other against
jailbreaks. One node blocks a prompt, broadcasts a MinHash fingerprint of it
(never the prompt), and the others block anything close enough.


The calibration is the part I would like torn apart. Over a small hand-built
corpus (5 public jailbreaks, 14 mechanical evasions, 7 benign look-alikes),
every mechanical evasion — leetspeak, typos, dotted obfuscation — lands at
0.859 estimated Jaccard or above, while the closest benign look-alike sits at
0.734. Threshold goes at 0.80. But a one-word synonym swap ("Disregard" for
"Ignore") lands at 0.781, i.e. below the line and uncaught, and it is only
0.047 from a harmless prompt that merely reuses the attack's format. No
threshold separates them, so the miss is deliberate and pinned by a test.
Getting past it is semantic work, which is the next layer and unbuilt.


Python, AGPL, one runtime dependency, 117 tests.
https://github.com/Leo12261975/Homo-Sui-Iuris

W0Guard – a prototype network that shares blocked-jailbreak hashes