data_reconstruction.md

C3. Data Reconstruction

• [2025/02] Automating Prompt Leakage Attacks on Large Language Models Using Agentic Approach
• [2025/02] ALGEN: Few-shot Inversion Attacks on Textual Embeddings using Alignment and Generation
• [2025/02] MARAGE: Transferable Multi-Model Adversarial Attack for Retrieval-Augmented Generation Data Extraction
• [2025/01] How Much Do Code Language Models Remember? An Investigation on Data Extraction Attacks before and after Fine-tuning
• [2024/12] Safeguarding System Prompts for LLMs
• [2024/11] RAG-Thief: Scalable Extraction of Private Data from Retrieval-Augmented Generation Applications with Agent-based Attacks
• [2024/11] Mitigating Privacy Risks in LLM Embeddings from Embedding Inversion
• [2024/11] Data Extraction Attacks in Retrieval-Augmented Generation via Backdoors
• [2024/10] Stealing User Prompts from Mixture of Experts
• [2024/10] Remote Timing Attacks on Efficient Language Model Inference
• [2024/10] Extracting Spatiotemporal Data from Gradients with Large Language Models
• [2024/10] Data Defenses Against Large Language Models
• [2024/10] PII-Scope: A Benchmark for Training Data PII Leakage Assessment in LLMs
• [2024/10] Towards a Theoretical Understanding of Memorization in Diffusion Models
• [2024/09] Extracting Memorized Training Data via Decomposition
• [2024/09] Prompt Obfuscation for Large Language Models
• [2024/08] Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries
• [2024/08] Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models
• [2024/07] Data Mixture Inference: What do BPE Tokenizers Reveal about their Training Data?
• [2024/07] Was it Slander? Towards Exact Inversion of Generative Language Models
• [2024/07] Towards More Realistic Extraction Attacks: An Adversarial Perspective
• [2024/07] PII-Compass: Guiding LLM training data extraction prompts towards the target PII via grounding
• [2024/06] Extracting Training Data from Unconditional Diffusion Models
• [2024/06] Is Diffusion Model Safe? Severe Data Leakage via Gradient-Guided Diffusion Model
• [2024/06] Raccoon: Prompt Extraction Benchmark of LLM-Integrated Applications
• [2024/05] Extracting Prompts by Inverting LLM Outputs
• [2024/05] PLeak: Prompt Leaking Attacks against Large Language Model Applications
• [2024/05] Special Characters Attack: Toward Scalable Training Data Extraction From Large Language Models
• [2024/05] Could It Be Generated? Towards Practical Analysis of Memorization in Text-To-Image Diffusion Models
• [2024/04] Investigating the prompt leakage effect and black-box defenses for multi-turn LLM interactions
• [2024/04] Iteratively Prompting Multimodal LLMs to Reproduce Natural and AI-Generated Images
• [2024/03] Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs
• [2024/02] PRSA: Prompt Reverse Stealing Attacks against Large Language Models
• [2024/02] Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems
• [2024/02] Pandora's White-Box: Increased Training Data Leakage in Open LLMs
• [2024/02] Large Language Models are Advanced Anonymizers
• [2024/02] Prompt Stealing Attacks Against Large Language Models
• [2024/02] Conversation Reconstruction Attack Against GPT Models
• [2024/01] Text Embedding Inversion Attacks on Multilingual Language Models
• [2023/12] Diffence: Fencing Membership Privacy With Diffusion Models
• [2023/11] Scalable Extraction of Training Data from (Production) Language Models
• [2023/09] Intriguing Properties of Data Attribution on Diffusion Models
• [2023/09] Teach LLMs to Phish: Stealing Private Information from Language Models
• [2023/09] Language Model Inversion
• [2023/07] Effective Prompt Extraction from Language Models
• [2023/02] Prompt Stealing Attacks Against Text-to-Image Generation Models
• [2023/01] Extracting Training Data from Diffusion Models
• [2020/12] Extracting Training Data from Large Language Models