# dependencies
8 articlestagged with “dependencies”
July 2026: Supply Chain Audit Challenge
Audit an ML project's entire supply chain for security issues including dependencies, model provenance, data pipelines, training infrastructure, and deployment artifacts.
Deep Supply Chain Analysis
Comprehensive analysis of the AI supply chain dependency tree covering model weights, tokenizers, datasets, libraries, and infrastructure components with audit methodology.
ML Pipeline Supply Chain Security
Securing the ML pipeline supply chain from training framework dependencies to serving infrastructure components.
Supply Chain Security for ML Dependencies
Securing the ML dependency supply chain including PyTorch, transformers, and model weight downloads.
CTF: Supply Chain Attack
Find and exploit vulnerabilities in an ML supply chain including compromised dependencies, poisoned models, backdoored training data, and malicious model files. Practice ML-specific supply chain security assessment.
Lab: Supply Chain Audit
Audit an ML project's dependencies for vulnerabilities, covering model files, Python packages, container images, and training data provenance.
Lab: ML Supply Chain Scan
Hands-on lab for auditing machine learning model dependencies, detecting malicious packages in ML pipelines, and scanning model files for backdoors and supply chain threats.
Supply Chain Prompt Injection Walkthrough
Plant injection payloads in upstream data sources consumed by LLM applications including packages and documentation.