Knowledge distillation can be used to distill safer, more robust behaviors from larger teacher models.
Distilling Safety: How Knowledge Distillation Creates Robust AI Models Introduction As Large Language Models (LLMs) continue to grow in scale,…
Incident response plans for AI failures ensure rapid containment of harmful system behavior.
Beyond Human Error: Engineering Incident Response Plans for AI Failures Introduction Artificial Intelligence is no longer an experimental toy; it…
Model pruning reduces the surface area for adversarial exploitation by removing redundant parameters.
Model Pruning as a Defense Strategy: Reducing the Adversarial Attack Surface Introduction In the high-stakes world of machine learning, neural…
Adaptive testing frameworks automatically generate new adversarial inputs based on model failures.
Contents1. Introduction: Define the shift from static testing to adaptive, adversarial AI testing. Why fixed datasets fail in production.2. Key…
Constitutional AI utilizes a set of principles to guide model refinement without extensive human labeling.
Constitutional AI: Scaling Model Alignment Beyond Human Labeling Introduction For years, the development of large language models (LLMs) relied heavily…
Model distillation techniques can isolate core reasoning modules from dangerous capabilities.
The Surgical AI: Distilling Core Reasoning from Dangerous Capabilities Introduction The field of Artificial Intelligence is currently caught in a…
Multi-modal input sanitization addresses unique risks associated with vision and audioprocessing.
Contents1. Introduction: The paradigm shift from text-only to multimodal AI (vision/audio) and the resulting expansion of the attack surface.2. Key…
Anomaly detection systems monitor input patterns to identify potential prompt injection or jailbreak attempts.
Defending LLMs: How Anomaly Detection Systems Stop Prompt Injection Introduction The rapid integration of Large Language Models (LLMs) into enterprise…
Verification of output provenance helps identify the lineage of generated information.
Contents1. Introduction: Define the crisis of trust in the AI era and introduce “Output Provenance.”2. Key Concepts: Deconstruct provenance, metadata,…
Federated learning approaches minimize the need to centralize sensitive data forsafety improvements.
Federated Learning: Protecting Sensitive Data While Powering AI Innovation Introduction For years, the gold standard of machine learning development was…