LLM Security Research & Tutorials
Deep dives into AI security, vulnerability research, and practical guides for securing language models in production.
Why Your LLM Needs a Security Scan Before Deployment
Most teams ship AI models without meaningful adversarial testing. We break down the five vulnerability categories every production LLM should be scanned for — and what happens when you skip them.
Understanding the OWASP LLM Top 10: A Practical Guide
The OWASP LLM Top 10 defines the most critical security risks for large language models. We map each category to real-world attack patterns and show how automated scanning catches them.
Multi-Layer Security Analysis: Why One Scanner Isn't Enough
Probe-based testing finds different vulnerabilities than adversarial simulation or behavioral evaluation. Here's how running all three produces higher-confidence findings with lower false-positive rates.
Explainable AI Security: How Sentryɸ Computes Confidence Scores
Black-box risk scores create more confusion than clarity. We explain the weighted formula behind Sentryɸ's dual-axis scoring — and why separation of severity and confidence matters.
Prompt Injection in 2026: Attack Vectors and Defense Strategies
From simple instruction overrides to multi-turn extraction attacks — the prompt injection landscape has evolved. We catalog current attack patterns and review effective defense layers.
From Scan to Deploy: Securing LLMs on AWS with Bedrock Guardrails
After scanning, what next? We walk through Sentryɸ's Deployment Advisor — from risk-based guardrail configuration to one-click IAM + CloudWatch + Bedrock provisioning on AWS.