ai-security
Safeguard articles tagged "ai-security" — guides, analysis, and best practices for software supply chain and application security.
593 articles
Safeguard Expands Into a Unified, AI-Native Defensive Security Platform
Safeguard is growing from a posture and findings platform into a first-party detection and prevention platform — first-party AppSec, defensive red-teaming, AI security, data security, runtime/CNAPP, and a supply-chain package firewall — all feeding one prioritized findings model.
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.
Governing MCP tools with per-tenant feature flags
Safeguard's MCP server exposes 650+ tools. Here's how per-tool feature flags keep each tenant scoped to exactly what it needs — with safe defaults and a fail-safe that narrows, never widens, on error.
Securing AI Coding Assistants: Guardrails That Hold
AI coding assistants are in nearly every IDE now. Banning them fails; trusting them blindly fails harder. The middle path is guardrails — technical controls that let assistants move fast without letting them ship the wrong thing.
The LLM Application Security Checklist (2026)
You are shipping an LLM feature. Before it goes live, walk this checklist — organized around the OWASP Top 10 for LLM Applications — to catch the risks that matter most in production.
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 Code Review and Security: Reviewer, Reviewed, or Both?
AI can review pull requests and AI can write them — sometimes in the same workflow. Both roles carry security implications teams routinely underestimate. Here is how to get the benefit without the blind spots.
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.
RAG Poisoning: Defenses That Work
Retrieval-augmented generation is the most common LLM deployment pattern in the enterprise and the most commonly poisoned. A senior security engineer's playbook for defences that hold up in production.
AIBOM (AI Bill of Materials): Frequently Asked Questions
A practical FAQ on AI bills of materials in 2026 — what an AIBOM captures, how it extends SBOMs to models and datasets, model provenance risks, formats, and governance drivers.
LLM Jailbreak Prevention: A Defense-in-Depth Playbook
A jailbreak is not the same thing as a prompt injection, and conflating them leads to defenses that miss. Here is how modern jailbreaks actually work and the layered controls that hold the line.
Prompt Injection Prevention: A Defense-in-Depth Guide
Prompt injection is the top risk on the OWASP list for LLM applications for a reason: there is no single patch. Preventing it means layering controls around a model that cannot reliably tell instructions from data.
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