# classifier
標記為「classifier」的 32 篇文章
Prompt Shields & Injection Detection
How Azure Prompt Shield and dedicated injection detection models work, their detection patterns based on fine-tuned classifiers, and systematic approaches to bypassing them.
Training Prompt Injection Classifiers
Methodologies for training and evaluating ML classifiers that detect prompt injection attempts with high accuracy.
ML-Based Prompt Injection Detection
Building and training ML classifiers for real-time prompt injection detection.
Custom Safety Classifier Training
Train a custom input safety classifier and then develop payloads that reliably evade it to understand classifier limitations.
Safety Classifier Inversion
Reverse-engineer safety classifier decision boundaries through systematic probing.
Basic Classifier Evasion
Evade basic input/output classifiers using paraphrasing, synonym substitution, and formatting tricks.
Classifier Gauntlet: 10-Stage Evasion
Bypass 10 progressively harder input classifiers using different evasion techniques at each stage.
Lab: Advanced Classifier Evasion
Evade advanced input classifiers using adversarial perturbation, semantic paraphrasing, and gradient-free optimization.
ML Classifier Evasion Lab
Develop payloads that evade machine learning-based input classifiers through adversarial text perturbation.
Input Classifier Boundary Mapping
Systematically probe input classifiers to map their decision boundaries and find bypass regions.
Building an Input Safety Classifier
Build a production-quality input classifier that detects prompt injection attempts.
Constitutional Classifier Setup
Step-by-step walkthrough for implementing constitutional AI-style classifiers that evaluate LLM outputs against a set of principles, covering principle definition, classifier training, chain-of-thought evaluation, and deployment.
Output Content Classifier
Step-by-step walkthrough for building a classifier to filter harmful LLM outputs, covering taxonomy definition, multi-label classification, threshold calibration, and deployment as a real-time output gate.
Prompt Classifier Training
Step-by-step walkthrough for training a machine learning classifier to detect malicious prompts, covering dataset curation, feature engineering, model selection, training pipeline, evaluation, and deployment as a real-time detection service.
ML-Based Prompt Injection Detection Systems
Walkthrough for building and deploying ML-based prompt injection detection systems, covering training data collection, feature engineering, model architecture selection, threshold tuning, production deployment, and continuous improvement.
Training a Prompt Injection Classifier
Train a custom prompt injection detection classifier using labeled datasets and modern NLP techniques.
Prompt Shield 與注入偵測
Azure Prompt Shield 與專責注入偵測模型如何運作，其基於微調分類器之偵測模式，以及繞過它們之系統化方法。
訓練 提示詞注入 Classifiers
Methodologies for training and evaluating ML classifiers that detect prompt injection attempts with high accuracy.
ML-Based 提示詞注入 Detection
Building and training ML classifiers for real-time prompt injection detection.
Custom Safety Classifier 訓練
Train a custom input safety classifier and then develop payloads that reliably evade it to understand classifier limitations.
Safety Classifier Inversion
Reverse-engineer safety classifier decision boundaries through systematic probing.
Basic Classifier Evasion
Evade basic input/output classifiers using paraphrasing, synonym substitution, and formatting tricks.
Classifier Gauntlet: 10-Stage Evasion
Bypass 10 progressively harder input classifiers using different evasion techniques at each stage.
實驗室: 進階 Classifier Evasion
Evade advanced input classifiers using adversarial perturbation, semantic paraphrasing, and gradient-free optimization.
ML Classifier Evasion 實驗室
Develop payloads that evade machine learning-based input classifiers through adversarial text perturbation.
Input Classifier Boundary Mapping
Systematically probe input classifiers to map their decision boundaries and find bypass regions.
Building an Input Safety Classifier
Build a production-quality input classifier that detects prompt injection attempts.
Constitutional Classifier Setup
Step-by-step walkthrough for implementing constitutional AI-style classifiers that evaluate LLM outputs against a set of principles, covering principle definition, classifier training, chain-of-thought evaluation, and deployment.
Output Content Classifier
Step-by-step walkthrough for building a classifier to filter harmful LLM outputs, covering taxonomy definition, multi-label classification, threshold calibration, and deployment as a real-time output gate.
Prompt Classifier 訓練
Step-by-step walkthrough for training a machine learning classifier to detect malicious prompts, covering dataset curation, feature engineering, model selection, training pipeline, evaluation, and deployment as a real-time detection service.
ML-Based 提示詞注入 Detection Systems
導覽 for building and deploying ML-based prompt injection detection systems, covering training data collection, feature engineering, model architecture selection, threshold tuning, production deployment, and continuous improvement.
訓練 a 提示詞注入 Classifier
Train a custom prompt injection detection classifier using labeled datasets and modern NLP techniques.