TextCrafter: Optimization-Calibrated Noise for Defending Against Text Embedding Inversion
Fuente:
arXiv
Saved in:
| Main Authors: | Tang, Duoxun, Jiang, Xinhang, Niu, Jiajun |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Eguard: Defending LLM Embeddings Against Inversion Attacks via Text Mutual Information Optimization
by: Liu, Tiantian, et al.
Published: (2024)
by: Liu, Tiantian, et al.
Published: (2024)
Defending Against Neural Network Model Inversion Attacks via Data Poisoning
by: Zhou, Shuai, et al.
Published: (2024)
by: Zhou, Shuai, et al.
Published: (2024)
Concept-Aware Privacy Mechanisms for Defending Embedding Inversion Attacks
by: Tsai, Yu-Che, et al.
Published: (2026)
by: Tsai, Yu-Che, et al.
Published: (2026)
Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text Detection with Adversarial Training
by: Li, Yuanfan, et al.
Published: (2025)
by: Li, Yuanfan, et al.
Published: (2025)
Text Embedding Inversion Security for Multilingual Language Models
by: Chen, Yiyi, et al.
Published: (2024)
by: Chen, Yiyi, et al.
Published: (2024)
Defending Against Prompt Injection with DataFilter
by: Wang, Yizhu, et al.
Published: (2025)
by: Wang, Yizhu, et al.
Published: (2025)
Evaluating Selective Encryption Against Gradient Inversion Attacks
by: Gu, Jiajun, et al.
Published: (2025)
by: Gu, Jiajun, et al.
Published: (2025)
SecAlign: Defending Against Prompt Injection with Preference Optimization
by: Chen, Sizhe, et al.
Published: (2024)
by: Chen, Sizhe, et al.
Published: (2024)
Defending Against Prompt Injection With a Few DefensiveTokens
by: Chen, Sizhe, et al.
Published: (2025)
by: Chen, Sizhe, et al.
Published: (2025)
StruQ: Defending Against Prompt Injection with Structured Queries
by: Chen, Sizhe, et al.
Published: (2024)
by: Chen, Sizhe, et al.
Published: (2024)
Calibrating Noise for Group Privacy in Subsampled Mechanisms
by: Jiang, Yangfan, et al.
Published: (2024)
by: Jiang, Yangfan, et al.
Published: (2024)
Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries
by: Huang, Yu-Hsiang, et al.
Published: (2024)
by: Huang, Yu-Hsiang, et al.
Published: (2024)
BitAbuse: A Dataset of Visually Perturbed Texts for Defending Phishing Attacks
by: Lee, Hanyong, et al.
Published: (2025)
by: Lee, Hanyong, et al.
Published: (2025)
Defending Against Intelligent Attackers at Large Scales
by: Lohn, Andrew J.
Published: (2025)
by: Lohn, Andrew J.
Published: (2025)
Attention is All You Need to Defend Against Indirect Prompt Injection Attacks in LLMs
by: Zhong, Yinan, et al.
Published: (2025)
by: Zhong, Yinan, et al.
Published: (2025)
Secure Distributed Learning for CAVs: Defending Against Gradient Leakage with Leveled Homomorphic Encryption
by: Najjar, Muhammad Ali, et al.
Published: (2025)
by: Najjar, Muhammad Ali, et al.
Published: (2025)
AgentVisor: Defending LLM Agents Against Prompt Injection via Semantic Virtualization
by: Ying, Zonghao, et al.
Published: (2026)
by: Ying, Zonghao, et al.
Published: (2026)
Defending Against Attack on the Cloned: In-Band Active Man-in-the-Middle Detection for the Signal Protocol
by: Teng, Wil Liam, et al.
Published: (2024)
by: Teng, Wil Liam, et al.
Published: (2024)
KinGuard: Hierarchical Kinship-Aware Fingerprinting to Defend Against Large Language Model Stealing
by: Xu, Zhenhua, et al.
Published: (2026)
by: Xu, Zhenhua, et al.
Published: (2026)
Noise Reduction for Pufferfish Privacy: A Practical Noise Calibration Method
by: Yang, Wenjin, et al.
Published: (2026)
by: Yang, Wenjin, et al.
Published: (2026)
Against All Odds: Overcoming Typology, Script, and Language Confusion in Multilingual Embedding Inversion Attacks
by: Chen, Yiyi, et al.
Published: (2024)
by: Chen, Yiyi, et al.
Published: (2024)
Defending Against Beta Poisoning Attacks in Machine Learning Models
by: Gulciftci, Nilufer, et al.
Published: (2025)
by: Gulciftci, Nilufer, et al.
Published: (2025)
Lessons from Defending Gemini Against Indirect Prompt Injections
by: Shi, Chongyang, et al.
Published: (2025)
by: Shi, Chongyang, et al.
Published: (2025)
No Free Lunch for Defending Against Prefilling Attack by In-Context Learning
by: Xue, Zhiyu, et al.
Published: (2024)
by: Xue, Zhiyu, et al.
Published: (2024)
CleanStack: A New Dual-Stack for Defending Against Stack-Based Memory Corruption Attacks
by: Chong, Lei
Published: (2025)
by: Chong, Lei
Published: (2025)
Who Grants the Agent Power? Defending Against Instruction Injection via Task-Centric Access Control
by: Cai, Yifeng, et al.
Published: (2025)
by: Cai, Yifeng, et al.
Published: (2025)
A Defender-Attacker-Defender Model for Optimizing the Resilience of Hospital Networks to Cyberattacks
by: Helfrich, Stephan, et al.
Published: (2026)
by: Helfrich, Stephan, et al.
Published: (2026)
RTBAS: Defending LLM Agents Against Prompt Injection and Privacy Leakage
by: Zhong, Peter Yong, et al.
Published: (2025)
by: Zhong, Peter Yong, et al.
Published: (2025)
Defending LLM Watermarking Against Spoofing Attacks with Contrastive Representation Learning
by: An, Li, et al.
Published: (2025)
by: An, Li, et al.
Published: (2025)
ARGUS: Defending LLM Agents Against Context-Aware Prompt Injection
by: Weng, Shihao, et al.
Published: (2026)
by: Weng, Shihao, et al.
Published: (2026)
Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion
by: Zhu, Hongyu, et al.
Published: (2024)
by: Zhu, Hongyu, et al.
Published: (2024)
MemPot: Defending Against Memory Extraction Attack with Optimized Honeypots
by: Wang, Yuhao, et al.
Published: (2026)
by: Wang, Yuhao, et al.
Published: (2026)
Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models
by: Wang, Shiming, et al.
Published: (2024)
by: Wang, Shiming, et al.
Published: (2024)
Prompt Stealing Attacks Against Text-to-Image Generation Models
by: Shen, Xinyue, et al.
Published: (2023)
by: Shen, Xinyue, et al.
Published: (2023)
When Vision Fails: Text Attacks Against ViT and OCR
by: Boucher, Nicholas, et al.
Published: (2023)
by: Boucher, Nicholas, et al.
Published: (2023)
ARGUS: Defending Against Multimodal Indirect Prompt Injection via Steering Instruction-Following Behavior
by: Lu, Weikai, et al.
Published: (2025)
by: Lu, Weikai, et al.
Published: (2025)
Inside Job: Defending Kubernetes Clusters Against Network Misconfigurations
by: Bufalino, Jacopo, et al.
Published: (2025)
by: Bufalino, Jacopo, et al.
Published: (2025)
Uncovering and Aligning Anomalous Attention Heads to Defend Against NLP Backdoor Attacks
by: Jin, Haotian, et al.
Published: (2025)
by: Jin, Haotian, et al.
Published: (2025)
Defending Large Language Models Against Jailbreak Exploits with Responsible AI Considerations
by: Wong, Ryan, et al.
Published: (2025)
by: Wong, Ryan, et al.
Published: (2025)
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
by: Wang, Yujing, et al.
Published: (2024)
by: Wang, Yujing, et al.
Published: (2024)
Similar Items
-
Eguard: Defending LLM Embeddings Against Inversion Attacks via Text Mutual Information Optimization
by: Liu, Tiantian, et al.
Published: (2024) -
Defending Against Neural Network Model Inversion Attacks via Data Poisoning
by: Zhou, Shuai, et al.
Published: (2024) -
Concept-Aware Privacy Mechanisms for Defending Embedding Inversion Attacks
by: Tsai, Yu-Che, et al.
Published: (2026) -
Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text Detection with Adversarial Training
by: Li, Yuanfan, et al.
Published: (2025) -
Text Embedding Inversion Security for Multilingual Language Models
by: Chen, Yiyi, et al.
Published: (2024)