Solutions
Model Security and GenAI Attack Threats
Technical articles on the art and science of AI model security and threats.
Model Security and GenAI Attack Threats Overview
These resources cover common attack threats for AI models and GenAI solutions like AI Agents. These are developer resources that use Python code to illustrate concepts.
Practical techniques to protect LLMs from prompt injection and jailbreaking attacks with illustrative code examples.
Detecting and Preventing Data Poisoning Attacks in AI Training Pipelines
Address the complexity of securing data from varied sources and maintaining data integrity across data environments.
Implementing Differential Privacy for Model Training Without Sacrificing Performance
Explore the tension between maximizing data utility for model accuracy and minimizing privacy risks.
Sidechain Managed AI
Deploying AI is hard. Sidechain offers fully-managed GenAI support, whether it’s hosting a custom model, building a training environment or running a battery of security tests against your AI Agent, Sidechain has you covered.
Defending Against Membership Inference and Extraction Attacks
This article focuses on the dangers and risks of re-identification attacks and the limitations of anonymization techniques.
How to managing data access in complex enterprise AI systems and the importance of RBAC, separation of duties, and security.
5. Adversarial Robustness: Building LLMs That Withstand Input Perturbation Attacks
Explore the critical role of data provenance for building trust in AI systems from a legal, contractual, and risk-management perspective.
Data poisoning is a crucial attack vector for fast-paced AI dev environments. We look at preliminary considerations in this article.
How the EU AI Act can inform decisions about constructing AI data usage, privacy, protections, and legal implications.