Trust no model you didn't vet.
Vetting of third-party models, datasets, and dependencies for poisoning, backdoors, and hidden triggers. Deflected clears them before they ever enter your pipeline.
Every third-party model, dataset, and dependency you pull in is code you didn't write and can't fully see. Poisoned training data, backdoored weights, and hidden triggers can sit dormant through evaluation and activate only in production — turning a trusted component into an attacker's foothold.
Modern pipelines pull open-weights models, public datasets, and package dependencies straight from open registries and hubs — often with a single command and no review. A model that benchmarks well can still carry a targeted backdoor that fires on a specific trigger phrase. A dataset can be seeded with a handful of crafted samples that shift behavior in ways no accuracy metric will surface.
Provenance and licensing add a second layer of exposure. Weights republished across mirrors lose their chain of custody, unverified checkpoints can be swapped or tampered with in transit, and unclear or non-commercial licenses create legal risk the moment a component reaches production. Once it is in your pipeline, unwinding it is far harder than vetting it up front.
Assesses the provenance, integrity, and licensing of every model and dataset before adoption.
Analyzes weights and artifacts for backdoors, hidden triggers, and tampering.
Probes for data poisoning and anomalous behavior that standard evaluation misses.
Issues a clear go or no-go with documented findings before anything reaches your pipeline.
Independent review of every third-party component before it touches production.
Active hunting for the hidden triggers and tainted data that evade normal testing.
A documented approval gate that keeps unvetted models out of your stack.
Model Supply-Chain Security is a scoped engagement for the people accountable for what enters the pipeline — from the platform teams that ship models to the risk owners who have to answer for them.
Groups that adopt open-weights models and third-party datasets at speed and need to keep unvetted components out of the stack.
Teams accountable for third-party risk who need model and data components held to the same scrutiny as any other dependency.
Enterprises that must evidence provenance and due diligence for every model and dataset before it reaches a production system.
Where an engagement pays off — the concrete moments a component is about to cross the line into your environment.
A promising open-weights model is slated for deployment. We assess its provenance and scan the weights for backdoors and hidden triggers before it ships.
An external dataset is about to feed training or fine-tuning. We probe it for poisoning and crafted samples that skew behavior without moving accuracy.
You need a repeatable sign-off step. We define a model and dataset intake gate so every new component is vetted and approved before adoption.
This is focused, adversarial work — not a checklist. We look for the threats standard evaluation was never designed to catch.
We actively hunt hidden triggers and tainted training data — the tampering that passes clean through accuracy benchmarks and normal QA.
We trace chain of custody, verify integrity, and flag license and provenance risk, then document a clear approval you can stand behind.
We catch the compromised component while it is still a candidate — before it reaches your pipeline, where unwinding it is far more costly.
Every byte handled by Model Supply-Chain Security is protected with NIST-standardized post-quantum cryptography — ML-KEM-1024 key encapsulation (FIPS 203) in a hybrid scheme. Your data stays sealed against harvest-now, decrypt-later attacks, today and after quantum computers arrive.
Book a working session with our team. We will scope Model Supply-Chain Security to your environment and define the intake gate that keeps unvetted models, datasets, and dependencies out of production.