AI Risk Fundamentals
Adversarial Examples Explained: Evasion Attacks, Tests, and Limits
Use NIST-aligned threat models, test suites, and Glitchive case evidence to find and document adversarial examples before deployment.
18 MIN READ
Blog pillar
AI Risk Fundamentals
Articles
AI Risk Fundamentals
Calibration Errors in AI Systems: Detection and Fixes
Learn about calibration errors in AI systems and discover how to detect and fix these gaps for more reliable model performance.
11 MIN READ
AI Risk Fundamentals
Dataset Shift Explained: How Production Data Breaks ML Assumptions
Discover how dataset shift impacts machine learning models in production and learn effective strategies to address this challenge.
14 MIN READ
AI Risk Fundamentals
LLM Jailbreaks vs Prompt Injection: The Difference and Why It Matters
Discover effective strategies to test and defend against LLM jailbreaks, ensuring your AI models adhere to safety and compliance standards.
18 MIN READ
AI Risk Fundamentals
Reward Hacking Explained: When AI Optimizes the Wrong Goal
Discover how to tackle reward hacking in AI systems. Learn to audit your reward signals and prevent common pitfalls in design.
20 MIN READ
AI Risk Fundamentals
Membership Inference Attacks: What They Reveal and How to Test for Them
Discover how to test for membership inference attacks and protect sensitive data. Learn essential strategies to safeguard your models.
22 MIN READ
AI Risk Fundamentals
AI Risk Taxonomy: A Practical Way to Classify Failures and Harms
Learn to effectively classify AI risks with a practical taxonomy that guides your team through incident management and mitigation steps.
18 MIN READ
AI Risk Fundamentals
Prompt Injection Explained: Direct, Indirect, and Agentic Risk
Protect your systems from prompt injection attacks. Learn key strategies and best practices to enhance security against this exploit.
16 MIN READ