model-security
Safeguard articles tagged "model-security" — guides, analysis, and best practices for software supply chain and application security.
20 articles
Overview of AI model supply chain security risks end to end
A concrete, incident-driven walkthrough of AI supply chain security — from poisoned datasets and backdoored Hugging Face models to CI pipeline hijacks — and how to reduce the risk end to end.
Glossary of AI Trust, Risk, and Security Management (AI T...
A glossary of AI trust risk security management concepts: the Gartner AI TRiSM framework, its four pillars, AI risk taxonomy, and adversarial threats.
How to build an AI-specific incident response playbook
A step-by-step guide to building an AI incident response plan — covering scoping, escalation, detection, containment, and post-incident review for LLM and agent failures.
LLM Supply Chain Vulnerabilities
Malicious model files, poisoned datasets, and compromised ML packages are the new software supply chain frontier. Here is how these LLM attacks actually work.
A risk framework for enterprise AI coding and agent tool rollouts
Samsung banned ChatGPT company-wide after three leaks in 20 days. A working framework for data exposure, model supply chain, and access control risk.
AI Supply Chain Security: Securing Models and Datasets
Your AI supply chain is not just your npm dependencies anymore. It is the models you download, the weights you load, and the datasets you train on — and each is an attack surface most software security programs have never inventoried.
Securing Hugging Face Models: A Practical Safety Guide
Hugging Face is the npm of machine learning, and it inherits npm's problems. Malicious weights, pickle payloads, and leaked Space secrets are all live risks — here is how to pull models safely.
AI Data Poisoning Defense: Protecting Models from Tainted Data
You do not need to corrupt most of a training set to backdoor a model — recent research suggests a small, near-constant number of poisoned documents can be enough. Defense starts with treating data as a dependency.
Embedding Model Supply Chain Risks
Embedding models are the silent dependency under every RAG system. We cover poisoning, deprecation, and provenance gaps that break retrieval in production.
AI Model Supply Chain Attacks: How Weights Become Malware
You would never run an unknown binary from a stranger, but teams pull unknown model weights off public hubs every day. Loading them can be code execution — and that is only the most obvious link in the chain.
Supply Chain Attacks Targeting AI/ML Pipelines
AI and ML pipelines introduce unique supply chain risks -- from poisoned training data to compromised model registries. Here is what attackers are targeting and how to defend.
AI Security Providers: How to Choose the Right One in 2026
AI security providers fall into a few distinct categories, and picking the right one starts with knowing which risk you are actually trying to cover. This guide breaks down the landscape.
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