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In-Model LLM Hardening
Harden LLMs Where Vulnerabilities Live.
Patent-pending layer-specific fine-tuning reduces attack success from ~35% to <2% — with verified re-Strike evidence, >95% utility preserved, and audit-ready compliance artifacts. Fix the model, not just the perimeter.
~35%→<2%
ASR Reduction
<30%
GPU Memory
10×
Faster vs Retrain
>95%
Utility Preserved
The Challenge
Runtime Firewalls Mask. Full Retraining Costs Months.
In SISA's assessments, most AI deployments rely on perimeter controls alone, and data leakage is the concern executives raise first. Existing options force a choice between perimeter masking, multi-week retraining, or doing nothing until a production incident.
Runtime Firewalls Mask, Don’t Fix Vulnerabilities
LLM-firewall and runtime-moderation tools sit at the perimeter, screening inputs and outputs. The underlying vulnerability remains. Novel adversarial prompts bypass moderation by targeting internal reasoning the firewall cannot see.
Full Retraining Is Disproportionate
Weeks to months of compute. Performance regression risk across the entire model. Equivalent to rebuilding an engine to fix a spark plug.
No Customer-Owned Evidence
Provider safety defaults are one-size-fits-all and produce no evidence you can show an auditor. System-prompt patching is not a security control.
How It Works
Surgical Layer-Specific Hardening
Step 01
Attribute
PrismStrike findings drive attribution: failures are mapped to probable internal layers — typically upper-middle attention heads or FFN layers.
Step 02
Tune
Layer-Specific Fine-Tuning (LSFT). Only implicated safety layers are tuned with controlled datasets via LoRA / QLoRA / adapters; all others stay frozen.
Step 03
Re-Strike
PrismStrike re-runs the full adversarial evaluation. ASR reduction is measured, not asserted — with reproducibility validation.
Step 04
Certify
Generate SafeScore report, layer change logs, model snapshot hashes, and governance notes pre-mapped to EU AI Act Art. 9, 15 and 17, ISO 42001, NIST AI RMF, HITRUST AI Security Certification.
Key Benefits
Why Teams Choose PrismSecure
Core
Patent-Pending Layer-Specific Fine-Tuning
Only implicated safety layers are tuned. Research on aligned LLMs confirms a small subset of contiguous layers drives prompt-attack resilience — PrismSecure targets exactly those layers.
Verified ASR Reduction ~35%→<2%
Mapped to the OWASP Top 10 for LLM Applications (2026). PrismStrike re-runs full adversarial evaluation after hardening. Risk reduction is measured, not asserted.
Compute-Efficient Hardening
<30% of full fine-tuning GPU memory. Up to 10× faster than full retraining. >95% pretrained language and reasoning performance preserved.
Two-Component Architecture
LSF in-model surgical fix + optional embedded guardrail layer (policy-based, fail-closed with logging) — especially critical for agentic AI and edge deployments.
Rollable Versioned Deltas
LoRA / QLoRA adapters enable roll forward or back to prior hash snapshots. PyTorch and TensorFlow export-ready. On-prem, cloud, and air-gapped deployment.
Audit-Ready Evidence
SafeScore, layer change logs, red-team transcripts, model snapshot hashes, governance notes — pre-mapped to EU AI Act Art. 9, 15 and 17, ISO 42001, ISO 23894, NIST AI RMF, HITRUST AI Security Certification.
Built For Your Team
Designed for Every Stakeholder
AI / ML Platform Lead
Fix vulnerabilities inside the model — not at the perimeter — without retraining and without sacrificing utility.
CISO
Verified ASR reduction with re-Strike confirmation. Risk reduction becomes a measurement, not an assertion.
Compliance / Risk Lead
Evidence pre-mapped to EU AI Act, ISO 42001, ISO 23894, NIST AI RMF, HITRUST AI Security Certification — for every hardening cycle.
Product Owner
Hardening becomes a continuous release gate, not an annual event. Ship secure and performant.
Frameworks & Compatibility
LoRAQLoRAPyTorchTensorFlowHugging FaceSafetensorsOn-PremAir-GappedEU AI Act Art. 9, 15 and 17ISO 42001ISO 23894NIST AI RMFHITRUST AI Security Certification