github.com
AIShield Watchtower
An open-source tool from AIShield for studying AI models and scanning for vulnerabilities
Independent evaluation guide | 13 synced entries
How to evaluate ML model scanners and validation tools
Inspect model files, notebooks, code, and behavior before release or deployment. Compare selection criteria here, then verify every claim against the linked first-party source.
Decision context
What does model scanning tooling need to cover?
Model scanning tools inspect machine learning artifacts and surrounding code before those assets enter trusted environments. A scanner may detect unsafe serialization instructions, embedded payloads, vulnerable dependencies, suspicious operators, policy violations, or unexpected model behavior. This category also includes validation tools that establish whether a model still meets security and quality expectations after a training, conversion, or packaging step.
README-synced directory
Model Scanning and Validation catalog entries
Each listing preserves the neutral description maintained in the Awesome MLSecOps README and links to the resource's first-party source.
github.com
Audit AI
Bias Testing for Generalized Machine Learning Applications
github.com
CircleGuardBench
A full-fledged benchmark for evaluating protection capabilities of AI models
Commercial platform for AI model validation and runtime protection; Robust Intelligence was acquired by Cisco
citadel-ai.com
Citadel Lens
Commercial model-testing platform; verify supported standards and security checks in current product documentation
github.com
Flawed Machine Learning Security
Practical examples of "Flawed Machine Learning Security" together with ML Security best practice across the end to end stages of the machine learning model lifecycle from training, to packaging, to deployment
protectai.com
Guardian
Model protection in CI/CD
hiddenlayer.com
HiddenLayer AI Detection and Response
Commercial AI detection and response platform from HiddenLayer
hiddenlayer.com
HiddenLayer: Model as Code
Research article on executable model formats and unsafe deserialization paths in ML libraries
github.com
lintML
Security linter for ML training code, maintained by a member of the NVIDIA AI red team
github.com
Mindgard CLI
Evaluate the security of AI systems through a CLI
github.com
NB Defense
Jupyter notebook security scanner from Protect AI (now part of Palo Alto Networks)
github.com
TensorFlow Model Analysis
A library for analyzing, validating, and monitoring machine learning models in production
No catalog entries match that search.
Selection framework
How should teams evaluate the options?
Choose a scanner based on the artifact formats and frameworks in your supply chain, not on a generic claim of AI coverage. Verify how it handles Pickle-derived formats, SafeTensors, ONNX, notebooks, and custom packages. Review whether findings identify concrete evidence and support automation through exit codes, SARIF, APIs, or CI integrations. Scanning is one control rather than proof of safety: provenance, signatures, isolated loading, access control, and behavioral testing remain necessary around it.
- 01Supported artifact formats
- 02Detection evidence
- 03False-positive handling
- 04CI and SARIF output
Record evidence and limitations for each criterion. A catalog listing is a discovery aid, not a security certification.
Primary references