- RLHF safety is a $300 Band-Aid on a bioweapon.
New research proves traditional AI safety is catastrophically broken for biology.
Why "refusal training" fails—and what Veriprajna built instead. 🧬
THREAD 🧵
#AISafety #Biosecurity - Traditional AI safety = teach the model to REFUSE harmful requests.
Model learns: "How to build bioweapon X"
Safety layer adds: "...but I won't tell you"
Knowledge is still there. Just hidden.
#MachineLearning #AIGovernance
Enter: Malicious Fine-Tuning (MFT) - Cost: ~$300 in GPU time
Method: Fine-tune on 10-50 harmful Q&A pairs
Result: Safety layer collapses. Model "remembers" hazardous knowledge.
Academic research proved this definitively.
#AIRisk #CISO
Closed APIs (ChatGPT) can be patched instantly. - Open-weight models? Once released, control is GONE.
Downloaded → Run offline → No logs → No bans → No patches
Permanent bioweapon consultant for every actor on Earth.
#OpenSource #DualUseAI
Even without fine-tuning, jailbreaks work. - Crescendo Attack: Gradually steer conversation toward danger
Deceptive Delight: Hide request in creative narrative
GeneBreaker: Use biological homology to bypass filters
15-20% success rate for RLHF models.
#Cybersecurity #AIRedTeaming - Veriprajna's solution: ERASURE not containment.
Knowledge-Gapped Architectures use machine unlearning to DELETE hazardous capabilities at the weight level.
Model doesn't refuse. It literally cannot answer. Like asking a 5-year-old to design a nuke.
#MachineUnlearning - Techniques:
→ RMU: Representation Misdirection deflects hazardous concepts to "nonsense" latent space
→ SAE: Sparse Autoencoders ablate specific bioweapon features
→ UIPE: Parameter Extrapolation prevents relearning
Surgical precision. Verified erasure. - #AIResearch #DeepLearning
Performance validation (WMDP Benchmark):
Open model: 75% bioweapon knowledge (HIGH RISK)
RLHF model: 72% (refusal-dependent)
VP Knowledge-Gapped: ~26% (RANDOM CHANCE)
81% general science retained. <0.1% jailbreak rate.
#AIBenchmarks #ISO42001 - Executive Order 14110: CBRN risk reporting mandatory
ISO 42001: Controls proportionate to risk
NIST AI RMF: Unlearning = highest-level control
Knowledge-Gapped AI = defensible compliance + liability shield.
#AICompliance #RiskManagement - For biotech deploying AI in drug discovery:
Structural biosecurity isn't optional.
It's the new duty of care.
Veriprajna specializes in Knowledge-Gapped Architectures for high-stakes bio R&D. - 📖 Read the full technical whitepaper here: https://veriprajna.com/whitepapers/immunity-architecture-knowledge-gapped-ai-biosecurity
📧 [email protected]
🌐 https://veriprajna.com
💬 WhatsApp: +919217059957
#BiotechAI #AIForGood