LiteLLM Supply Chain Compromise: When Your AI Dependency Becomes an Attack
Vector
Vector
PRISM Secure Fine-tuning: In-Model LLM Hardening
Upload, fine-tune, and benchmark models securely with vulnerability-aware datasets and embedded guardrails. PRISM re-secures the specific internal layers that carry the risk — so safety is intrinsic to the model and travels with it, even to edge and IoT devices where a firewall can’t go.
Why it matters
Most organizations lack adequate defenses for their AI deployments. Existing options force a choice between perimeter masking, multi-week retraining, or doing nothing until a production incident. And fine-tuning an open-source model on your own domain — even on entirely benign data — can silently strip the safety it shipped with, with no signal to the team.
Runtime Firewalls Mask, Don’t Fix Vulnerabilities
LLM-firewall and runtime-moderation tools sit at the perimeter, screening inputs and outputs. Novel adversarial prompts bypass moderation by targeting internal reasoning the firewall cannot see. And a perimeter firewall can’t travel to the edge — IoT devices where no firewall can sit in front of the model.
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.
Prompt Engineering & System-Prompt Patching
Adversarial prompts are designed to override system prompts and constrain model behavior. No measurable ASR reduction, no audit evidence, no reproducible fix.
Our Approach
PRISM Secure Fine-tuning uses patent-pending layer-specific fine-tuning to fix vulnerabilities inside the model architecture through a 4-step process.
Attribute
Failures are mapped to probable internal layers — typically upper-middle attention heads or FFN layers. LLM red-teaming localises the layers that carry the vulnerability, grounded in research that safety alignment is layer-localised.
Tune
Layer-Specific Secure Fine-Tuning (LSF). Only implicated safety layers are tuned with controlled datasets via LoRA / QLoRA / adapters; all others stay frozen. A guardrail is also built into the weights so the model refuses anything outside its approved use cases.
Re-Strike
PRISM Secure re-runs the full adversarial evaluation against PRISM Strike’s 15,000+ red-team corpus (plus newly generated cases). ASR reduction is measured, not asserted — with reproducibility validation. Performance is validated intact, so the fix survives the AI team’s scrutiny.
Certify
Generate SafeScore report, layer change logs, model snapshot hashes, and governance notes pre-mapped to EU AI Act Art. 9/15/40, ISO 42001, NIST AI RMF, HITRUST AI-44, PCI DSS 4.0.1, and the payment-sector frameworks (SAMA, CBUAE, RBI, MAS).
Service Offerings
PRISM Secure Fine-tuning helps organizations harden, test, and secure AI
models across the LLM lifecycle.
Attributes failures to the probable internal layers responsible — typically upper-middle attention heads or FFN layers. Only implicated layers are tuned using tightly controlled datasets; all other layers are frozen.
LSF (in-model surgical fix) + Embedded Guardrail Layer (policy-based, task-bounded, embedded within the LLM pipeline built into the model’s weights so it refuses anything outside its approved use cases, fail-closed with policy logging).
LoRA/QLoRA adapters enable rolls forward or back to a prior hash snapshot making hardened models export- ready for PyTorch and TensorFlow.
Everysecured model is run against PRISM’s 15,000+ LLM red-teaming corpus (plus newly generated cases), producing audit-grade evidence of exactly which attacks it resists and confirming no measured performance degradation — validation by execution, not a vendor claim.
The secured model ships as an edge-ready quantized artifact, deployable on IoT and edge devices — POS terminals, MPoC-certified card readers, ATMs and mobile payment SDKs — where no external firewall can sit in front of it. Backed by SISA’s Hardware Security Lab (MPoC).
BENEFITS
PRISM Secure Fine-tuning helps organizations move from experimental AI use to secure, production-ready deployments, with audit-grade evidence.
Verified ASR Reduction
From approximately 35% in base models to less than 2%, based on OWASP-grade red-team testing
Compute-Efficient Hardening
<30% of full fine-tuning GPU memory. Up to 10× faster than full retraining.
>95% pretrained language and reasoning performance preserved.
Audit-Ready Evidence
SafeScore, layer change logs, red-team transcripts, model snapshot hashes, governance notes — pre-mapped to EU AI Act, ISO 42001, NIST AI RMF, PCI DSS 4.0.1 and the payment-sector regulators (SAMA, CBUAE, RBI, MAS).
Safety That Travels to the Edge
Because safety is intrinsic to the weights rather than a runtime firewall, the hardened model stays safe on POS terminals, MPoC-certified card readers, ATMs and IoT devices where a firewall can’t go — category-of-one for payment-edge deployments.
Bound to Its Approved Use Cases
The model refuses anything outside its intended scope, enforced in the weights and validated against PRISM Strike’s 15,000+ corpus — so a collections copilot stays a collections copilot.
Why Organizations Choose PRISM Secure Fine-tuning from SISA
PRISM Secure Fine-tuning combines forensic intelligence, adversarial testing, and model-level hardening to deliver real-world AI security.
Forensics-led AI security approach
Built on insights from real-world breach investigations to address how attackers exploit AI systems in practice.
Adversary-driven testing, not static checks
Simulate prompt injection, jailbreaks, and abuse scenarios to validate model behavior under realistic attack conditions.
Model-level hardening with guardrail design
Implement targeted controls across prompts, model behavior, and outputs to reduce risk without impacting performance.
Actionable, evidence-backed outcomes
Deliver clear validation, measurable risk reduction, and practical recommendations to strengthen AI security posture.
Category-of-One: Intrinsic Layer-Level Hardening
Rather than bolt on a bypassable external firewall, PRISM Secure re-secures the specific layers that carry the risk — layer-level intrinsic safety that, as a productised capability, no competitor ships.
Safe Where a Firewall Can’t Go — the Payment Edge
Hardened models deploy to POS devices, MPoC-certified card readers, ATMs and mobile payment SDKs, backed by SISA’s Hardware Security Lab (MPoC) — an edge-payment adjacency no AI-security vendor can match.
Validated by PRISM Strike, Evidenced for Your Auditor
Every secured model is proven against PRISM’s 15,000+ red-team corpus and packaged with PCI-QSA-grade evidence — measurable resistance mapped to PCI DSS 4.0.1, SAMA, CBUAE, RBI and MAS.
Security Inside the Model
