What is LLM Security
LLM security protects model weights, prompts, outputs, and the AI supply chain from injection, leakage, and compromise—here's what it covers and how to defend it.
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.
LLM security protects model weights, prompts, outputs, and the AI supply chain from injection, leakage, and compromise—here's what it covers and how to defend it.
The model you think you're calling might not be the model that returns. Model substitution is a quiet supply chain risk that deserves explicit controls.
Gemini's pricing table favours long-context workloads. Security scans have long-context structure. The question is how much context fits into the architecture.
Time from contract signature to first meaningful finding is the metric procurement cares about. Griffin AI and Mythos-class tools diverge in week one.
Tracking remediation SLAs in spreadsheets is how programmes drift. Here is how to track SLAs in the same system that finds, fixes, and merges vulnerabilities.
Prompt injection is OWASP's #1 LLM risk. Learn how it works, real CVEs like EchoLeak, and how to detect and defend against it.
AI-based security is used to describe everything from a genuinely trained detection model to a marketing rewrite of a rules engine. Here's how to tell what you're actually buying.
Direct and indirect prompt injection are different attack vectors with different blast radii. Real 2025 CVEs like EchoLeak show why the distinction matters.
LLM jailbreaking bypasses AI safety guardrails through techniques like DAN prompts, Crescendo, and Skeleton Key — here's how it works and how to defend against it.
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