Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Adversarial attacks manipulate ML systems through evasion, poisoning and privacy attacks. Learn NIST's taxonomy and defensive steps for your own models.
Adversarial attacks trick, poison or extract from machine learning models. NIST groups them by stage and goal. Here is how to read the taxonomy and defend.
A usable AI coding policy names approved tools, data rules, review duties and what agents may run. Keep it to one page and enforce it in tooling.
In a data poisoning attack an adversary inserts or alters training data to change a model's behavior. Provenance, validation and testing are the defenses.
Test LLM supply chain risk by checking provenance, hashes, packages, licenses and behavior. A defensive test plan on OWASP LLM03, with no attack code.
Small, tuned models can screen prompts and responses cheaply, but they are a filter, not a boundary. Where they fit, how to test them and where they fail.
A RAG app is only as safe as the documents it retrieves and the permissions on them. Common mistakes and the controls OWASP lists to fix them.
OWASP LLM08 covers weak access control, leaks and poisoning in RAG stores. This maps each risk to the control families auditors already ask about.
OWASP LLM07 says a system prompt is not a secret or a control. Keep credentials and permissions out of prompts and enforce rules outside the model.
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