HafezSecure

ML Pipeline Security Review

Security review of machine learning pipelines including data handling, model deployment, and inference security

Request ServiceContact Us
About This Service

Our ML Pipeline Security Review examines the machine-learning lifecycle end to end — from the data that trains a model, through the registry that stores it, to the endpoint that serves it. The pipeline is a software supply chain with its own failure modes: data that can be poisoned before training, model artifacts that can be tampered with or loaded unsafely, training data that can be recovered through the model, and inference endpoints that can be abused to extract the model or overwhelm it. We review data handling and provenance, artifact integrity across the registry, secrets in notebooks and CI, access control on the feature store and model registry, and the authentication, authorization, and rate limits on the inference endpoint. The work follows the OWASP AI Testing Guide, the NIST AI Risk Management Framework, and MITRE ATLAS. Every finding is rated with CVSS alongside its real impact and written up with a concrete fix.

What's Included

Data ingestion and provenance: where training data comes from, and whether it can be poisoned or tampered with before training

Training data privacy: membership inference and data reconstruction through the model

Model artifact integrity: signing, provenance, and tamper detection across the registry

Unsafe model loading: deserialization risks in model formats that execute code on load

Supply chain of models and dependencies pulled into the pipeline

Secrets and credential handling in notebooks, jobs, and CI/CD steps

Access control on the feature store, the model registry, and the artifact store

Inference endpoint security: authentication, authorization, and input validation

Model extraction and model theft through the inference endpoint

Unbounded consumption: rate limiting and denial-of-service on inference

Logging, monitoring, and drift detection for the served model

How It Works

1
Pipeline Discovery
We map the pipeline: where data enters, how it is transformed, where models are trained and stored, and how they are served. We identify each stage, the identities that act on it, and the trust boundaries between them.
2
Data & Provenance Testing
We test whether training data can be poisoned or tampered with before it reaches the model, whether its provenance is verifiable, and whether the trained model leaks information about the data it was trained on.
3
Artifact, Registry & Supply-Chain Testing
We check whether model artifacts are signed and tamper-evident, whether they load safely, whether the registry and feature store enforce access control, and whether the models and dependencies pulled into the pipeline are trustworthy.
4
Inference Endpoint Testing
We test the served model the way an abuser reaches it: authentication and authorization on the endpoint, input validation, model extraction through crafted queries, and the rate limits that stand between a client and denial-of-service.
5
Validation, Reporting & Re-testing
Every finding is proven, rated with CVSS and the real impact, mapped to the OWASP AI Testing Guide and MITRE ATLAS, and written up with a concrete fix. After you remediate, we re-test to confirm the fixes hold.
Deliverables
  • Executive summary with a risk overview for decision-makers
  • Detailed technical report, each finding reproducible
  • Data-poisoning, provenance, and training-data privacy findings
  • Artifact integrity, unsafe-loading, and registry access-control findings
  • Supply-chain findings for models and dependencies
  • Inference-endpoint, model-extraction, and rate-limit findings
  • Findings mapped to the OWASP AI Testing Guide and MITRE ATLAS, with CVSS and business impact
  • Prioritized remediation guidance and re-testing after remediation

Why HafezSecure

The Whole Lifecycle
We look past the model in isolation to the pipeline that produces and serves it — data, registry, and endpoint — because that is where most of the exploitable weaknesses actually live.
Supply-Chain Aware
A pipeline pulls in third-party models, datasets, and dependencies, each a way in. We treat the ML pipeline as the software supply chain it is, including the artifacts that execute code when loaded.
Data Privacy at the Model
A model can quietly reveal the data it was trained on. We test membership inference and reconstruction, so a privacy exposure is found before it reaches a regulator or the news.
Reproducible and Actionable
Every finding comes as a case you can reproduce, mapped to a recognized standard, with a concrete fix for the stage it belongs to — data, registry, or endpoint.

Frequently Asked Questions

What is an ML Pipeline Security Review?

It reviews the machine-learning lifecycle for security weaknesses: data handling and provenance, training-data privacy, model artifact integrity, the registry and feature store, the supply chain of models and dependencies, and the inference endpoint. It follows the OWASP AI Testing Guide, the NIST AI RMF, and MITRE ATLAS, with each finding proven and rated.

What is data poisoning?

Data poisoning is tampering with training data so the resulting model behaves in a way the attacker chose — misclassifying certain inputs, or carrying a hidden backdoor. Because it happens before training, it is invisible in the served model unless you can trace the data's provenance, which is why we test the ingestion path, not just the endpoint.

What is model extraction?

Model extraction is rebuilding a close copy of your model, or recovering its parameters and behavior, by querying the inference endpoint enough times. It threatens the intellectual property in the model and can reveal training data. We test whether the endpoint's authentication and rate limits make it practical.

What access do you need to start?

It depends on the depth agreed. A white-box review works from the pipeline definition, the registry and feature-store configuration, the training and serving code, and access to a representative environment. A black-box review works only from the inference endpoint. Most of the high-impact pipeline findings need at least a grey-box view, so we recommend sharing the configuration where possible.

Should the review run against production or a staging environment?

A staging environment that mirrors production is preferred, so that testing the ingestion path, the registry, and the endpoint cannot disturb live training runs or served traffic. Where we test against production, we agree on non-destructive boundaries first, especially around the data path.

How is this different from an LLM model assessment?

An LLM model assessment focuses on a single model's behavior — prompt injection, extraction, adversarial robustness. A pipeline review focuses on the system that builds and serves models: the data, the registry, the supply chain, and the deployment. They are complementary, and for a team running its own models both together give the fuller picture.

How are findings rated?

Each finding is scored with CVSS and presented next to its real business impact — data exposure, model theft, or a poisoned model reaching production — so you can remediate in the order that reduces the most risk first.

Do you re-test after we fix the findings?

Yes. Re-testing after remediation is part of the engagement: we confirm each fix closes the issue without opening a new one, and the report is updated to reflect the remediated state.

Related Services

Complementary services that might be useful for you

Ready to Get Started?
Contact our team to discuss your security assessment needs
Request ServiceContact Us