Permanent edits at the weight layer. No fine-tuning, no wrappers, no runtime overhead. Ships as drop-in checkpoints.
Guardrails are stop signs on the honor system. The model is supposed to stop, but nothing physically makes it stop when an attacker rolls up with a prompt injection. In AI security today, that stop sign IS the security. Every 2026 Copilot CVE landed on a stack with the sign up.
immunized.v2 proves the speed bump. Mechanical. Under the road. Enforcement whether or not anyone is watching.
EchoLeak (CVE-2025-32711) class prompt injection: instructions hidden in retrieved documents, tool results, or webhook payloads that manipulate the model into disclosing private context. The same attack pattern behind ShareLeak, CoSnitch, SearchLeak, and every 2026 Copilot CVE.
The vulnerability lives at the model layer, not the product wrapper. Microsoft cannot fix it in GPT weights from outside. Open-weight foundation models can be edited directly. jBlaze immunization is permanent, baked into the weights, and inherited by every downstream fine-tune.
jBlaze can now write factual knowledge directly into model weights -- no LoRA, no training framework, no optimizer state, no checkpoints. The knowledge is permanent, recallable and editable at inference with zero runtime overhead.
Production method (Passidation): 489/500 facts at 98% recall on 1.4B. 486/500 at 97% on 6.9B. Stress tested to 16,750 facts on 1.4B and 26,081 on 20B.
We took EleutherAI's Pythia-1.4b -- a raw base model with patchy factual knowledge -- and programmed 500 facts directly into its weights using Passidation (3-pass write with tapering learning rates). Gradient training (LoRA) destroyed the same model at 50 facts.
Result: 489/500 correct (98%), 5/5 language coherence, stable perplexity. Scaled to 6.9B: 486/500 (97%).
The model generalizes to untrained related facts. Ask it "Einstein's most famous equation" -- never programmed -- and it answers "E = mc squared." The knowledge integrates, it doesn't just memorize.
Today, building a knowledgeable AI model requires training on trillions of tokens across massive GPU clusters for weeks or months. The entire AI infrastructure industry is built on this assumption.
If you can separate cognitive training from knowledge installation -- train reasoning once, then program the knowledge in minutes -- the cost structure of AI changes fundamentally.
That's not a theoretical claim. The proof model is on HuggingFace right now.
Gradient training does not run out of capacity gracefully -- it fails early. On the same Pythia-1.4B, LoRA collapsed the model at 50 facts on the first pass and 125 facts on a conservative rerun. Perplexity spiked to 459 and 653; general capability fell to 10-25%. That is well before any capacity ceiling -- the model was destroyed before it filled up.
DNP fills the model instead of breaking it. On the same 1.4B, DNP absorbed 8,000+ sequential facts with no runtime collapse -- recall settled into a stable ~50% equilibrium, general capability held 50-70%, perplexity flat at 18.7 (baseline 13.3). Scaled to 20B, DNP wrote 26,081 facts over 36.7 hours with general capability at 100% throughout. Effective retrievable capacity scales roughly linearly with parameter count: ~400 facts on 1.4B, ~5,000-6,000 on 20B.
What happens beyond the ceiling is not simple overwrite. It is distributed retention equilibrium -- cohorts written 7,000 facts apart retain at comparable rates, age-recall correlation ~0.10. There is no clean forgetting curve of the kind the sequential-editing literature (ROME, MEMIT, and follow-ups) predicts. Writes and losses balance across the entire history, not just the tail. GPT-5.6 Sol reviewed the data independently and called the behavior inconsistent with the dominant empirical failure modes.
The honest open problem: there is no runtime signal yet that says "you have hit the ceiling, the next write will start displacing prior knowledge." A second write pass recovers some early cohorts (7.6% -> 12.4% recall on 1.4B) but does not raise the ceiling itself. Predicting which specific fact will fall out is unsolved. Practical work stays comfortably under the ceiling; work at or near it is currently empirical.
Designed to preserve the base model's knowledge, fluency, and reasoning. Only the targeted behaviors change. Knowledge is added, never subtracted.
Everything runs on a single workstation. 198 facts programmed in 140 seconds. No cluster, no datacenter, no cloud.
Changes are permanent in the weights. No system prompts, no jailbreak strings, no inference-time patches. The model simply behaves differently.
At ShipItClean, we use local models for automated code security review. A model loaded with guardrails, refusal behaviors, and hedging qualifiers makes for a poor code reviewer -- it refuses to discuss vulnerabilities in detail, wraps every finding in disclaimers, and softens its analysis to avoid sounding confrontational.
We needed models that would analyze code directly, state findings plainly, and not refuse to explain how an exploit works just because the topic is sensitive. Building from scratch costs tens of millions. Fine-tuning requires curated datasets and unpredictable results. jBlaze does it in minutes.
Every organization deploying AI has the same problem: foundation models are general-purpose, but real applications need specific behavioral profiles. Enterprises spend millions on custom model training, prompt engineering harnesses, and elaborate system prompts to get models to behave the way their use case demands.
jBlaze eliminates that overhead. Applied to any open-weight model, it produces a purpose-built variant in minutes instead of months. The industry is moving toward specialized models -- jBlaze operates downstream of training, reshaping behavior without retraining.
EchoLeak-immunized Llama. 100/100 canary, 83/100 role-based, 16/16 EchoLeak battery. Ships as fp16 + 5 GGUF quants.
The original EchoLeak immunization. Preserved for historical reference. 100/100 canary, 63/100 role.
198 facts programmed via Direct Neural Programming (demo release). 99% accuracy. 140 seconds. Internal testing: 5,000+ facts.
Full identity transplant + behavioral surgery on NVIDIA's hybrid 30B. He doesn't know he's an AI.
Code security scanner with 7 stacked behavioral modifications. Our most engineered model.
Uncensored 32B code model. Full refusal removal.
Complete uncensored. Refusal, hedging, and servility removed.
Google's Gemma 4 with refusal behaviors removed.
Uncensored 24B Mistral Small. Drop-in replacement.
Manipulation-resistant analyst. Built for adversarial environments.
Code security specialist with enhanced causal tracing.
Uncensored 405B Llama. The largest abliterated model released.
DeepSeek R1 distill with sycophancy removed.