Extracting Training Data from Large Language Models
(arxiv) Performs a training data extraction attack to recover individual training examples by querying the language model.
- from
- Audit Algorithms
- added
- 2026-10-10
- likes
- 0
similar
-
Extracting Training Data from Large Language Models arxiv.org
(USENIX Security) Extract verbatim text sequences from the GPT-2 model’s training data.
-
Stealing the Decoding Algorithms of Language Models people.cs.umass.edu
(CCS) Steal the type and hyperparameters of the decoding algorithms of a LLM.
-
Universal and Transferable Adversarial Attacks on Aligned Language Models arxiv.org
This paper introduces a simple and efficient attack method that enables aligned language models to generate objectionable content with high probability, highlighting the need for improved prevention techniques in large language models. The generated adversarial prompts are…
-
Neural Network Model Extraction Attacks in Edge Devicesby Hearing Architectural Hints arxiv.org
(arxiv) Through the acquisition of memory access events from bus snooping, layer sequence identification bythe LSTM-CTC model, layer topology connection according to the memory access pattern, and layer dimension estimation under data volume constraints, it demonstrates one can…
-
Build a Large Language Model (From Scratch) manning.com
A guide to building your own working LLM, by Sebastian Raschka.
-
New Languages for NLP new-languages-for-nlp.github.io
Learn how to annotate linguistic data and train statistical language models using cutting-edge natural language processing (NLP) tools.
Audit Algorithms › Papers > 2020: “(arxiv) Performs a training data extraction attack to recover individual training examples by querying the language model.”