# training-data
標記為「training-data」的 30 篇文章
Training Data Breach Forensics
Investigating training data breaches including data extraction evidence and membership inference indicators.
Training Data Provenance Forensics
Forensic techniques for tracing the origins, lineage, and integrity of training data used in machine learning models.
Case Study: Training Data Extraction from GPT
Analysis of the Carlini et al. work on extracting training data from ChatGPT in production.
Code Suggestion Poisoning
Overview of attacks that manipulate AI coding assistant suggestions through training data poisoning and inference-time context manipulation.
Training Data Extraction from Code Models
Techniques for recovering proprietary code from code generation model weights — covering memorization detection, targeted extraction, membership inference, and defensive countermeasures.
Advanced Model Inversion Attacks
Reconstructing training data from model weights and API access using gradient-based inversion, generative model-assisted reconstruction, and membership inference refinement.
Advanced Training Data Extraction
Advanced techniques for extracting memorized training data from language models.
Membership Inference via Embeddings
Determining if specific data was in an embedding model's training set through distance-based inference, statistical tests, and embedding behavior analysis.
Repository Poisoning for Code Models
Techniques for poisoning code repositories to influence code generation models, including training data poisoning through popular repositories, backdoor injection in open-source dependencies, and supply chain attacks targeting code model training pipelines.
Securing Storage Systems for Training Data
Attack and defense strategies for S3, GCS, HDFS, and object storage systems holding AI training datasets and model artifacts
Lab: Training Data Extraction at Scale
Extract memorized training data from language models using prefix-based extraction, divergence testing, and membership inference. Measure extraction rates and assess privacy risks.
Training Data Extraction from Production LLMs
Implement Carlini et al.'s techniques to extract memorized training data from production language model APIs.
Extracting Training Data
Techniques for extracting memorized training data, system prompts, and private information from LLMs through targeted querying and membership inference attacks.
RAG, Data & Training Attacks
Overview of attacks targeting the data layer of AI systems, including RAG poisoning, training data manipulation, and data extraction techniques.
Training Data Manipulation
Attacks that corrupt model behavior by poisoning training data, fine-tuning datasets, or RLHF preference data, including backdoor installation and safety alignment removal.
訓練 Data Breach Forensics
Investigating training data breaches including data extraction evidence and membership inference indicators.
訓練 Data Provenance Forensics
Forensic techniques for tracing the origins, lineage, and integrity of training data used in machine learning models.
Case Study: 訓練 Data Extraction from GPT
Analysis of the Carlini et al. work on extracting training data from ChatGPT in production.
程式碼建議投毒
透過訓練資料投毒與推論期上下文操控來操控 AI 程式設計助理建議的攻擊概覽。
從程式碼模型萃取訓練資料
從程式碼生成模型權重復原專有程式碼的技術——涵蓋記憶偵測、針對性萃取、成員推論與防禦對策。
進階 模型 Inversion 攻擊s
Reconstructing training data from model weights and API access using gradient-based inversion, generative model-assisted reconstruction, and membership inference refinement.
進階 訓練 Data Extraction
進階 techniques for extracting memorized training data from language models.
透過嵌入進行成員推論
透過距離式推論、統計檢定與嵌入行為分析，判定特定資料是否存在於嵌入模型的訓練集之中。
Repository 投毒 for Code 模型s
Techniques for poisoning code repositories to influence code generation models, including training data poisoning through popular repositories, backdoor injection in open-source dependencies, and supply chain attacks targeting code model training pipelines.
Securing Storage Systems for 訓練 Data
攻擊 and defense strategies for S3, GCS, HDFS, and object storage systems holding AI training datasets and model artifacts
實驗室: 訓練 Data Extraction at Scale
Extract memorized training data from language models using prefix-based extraction, divergence testing, and membership inference. Measure extraction rates and assess privacy risks.
訓練 Data Extraction from Production LLMs
Implement Carlini et al.'s techniques to extract memorized training data from production language model APIs.
擷取訓練資料
透過針對性查詢與成員推論攻擊，從 LLM 中擷取已記憶之訓練資料、系統提示與私密資訊的技術。
RAG、資料與訓練攻擊
針對 AI 系統資料層攻擊的概覽，包含 RAG 投毒、訓練資料操控與資料萃取技術。
訓練資料攻擊
操控用於訓練或微調模型之資料的攻擊——涵蓋資料投毒、後門植入、RLHF 操控與微調利用。