{"total":140,"rows":[{"id":"2312.02003","title":"A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly","created":"2023-12-04","cited_by_count":387,"area":null},{"id":"2202.06539","title":"Deduplicating Training Data Mitigates Privacy Risks in Language Models","created":"2022-02-14","cited_by_count":314,"area":null},{"id":"2202.05520","title":"What Does it Mean for a Language Model to Preserve Privacy?","created":"2022-02-11","cited_by_count":195,"area":null},{"id":"2310.06816","title":"Text Embeddings Reveal (Almost) As Much As Text","created":"2023-10-10","cited_by_count":192,"area":null},{"id":"2406.02630","title":"AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways","created":"2024-06-04","cited_by_count":127,"area":null},{"id":"2207.00220","title":"Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset","created":"2022-07-01","cited_by_count":113,"area":null},{"id":"2308.12833","title":"Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities","created":"2023-08-24","cited_by_count":82,"area":null},{"id":"2305.03010","title":"Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence","created":"2023-05-04","cited_by_count":81,"area":null},{"id":"2106.01221","title":"Differential Privacy for Text Analytics via Natural Text Sanitization","created":"2021-06-02","cited_by_count":77,"area":null},{"id":"2212.10025","title":"When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods","created":"2022-12-20","cited_by_count":75,"area":null},{"id":"2205.08514","title":"Recovering Private Text in Federated Learning of Language Models","created":"2022-05-17","cited_by_count":70,"area":null},{"id":"2210.13918","title":"Differentially Private Language Models for Secure Data Sharing","created":"2022-10-25","cited_by_count":68,"area":null},{"id":"2311.13647","title":"Language Model Inversion","created":"2023-11-22","cited_by_count":68,"area":null},{"id":"2205.02130","title":"The Limits of Word Level Differential Privacy","created":"2022-05-02","cited_by_count":63,"area":null},{"id":"2212.14315","title":"\"Real Attackers Don't Compute Gradients\": Bridging the Gap Between Adversarial ML Research and Practice","created":"2022-12-29","cited_by_count":56,"area":null},{"id":"2104.07504","title":"Natural Language Understanding with Privacy-Preserving BERT","created":"2021-04-15","cited_by_count":55,"area":null},{"id":"2505.12540","title":"Harnessing the Universal Geometry of Embeddings","created":"2025-05-18","cited_by_count":55,"area":null},{"id":"2207.01193","title":"A Customized Text Sanitization Mechanism with Differential Privacy","created":"2022-07-04","cited_by_count":52,"area":null},{"id":"2310.10383","title":"Privacy in Large Language Models: Attacks, Defenses and Future Directions","created":"2023-10-16","cited_by_count":51,"area":null},{"id":"2305.16157","title":"Training Data Extraction From Pre-trained Language Models: A Survey","created":"2023-05-25","cited_by_count":45,"area":null},{"id":"2305.06212","title":"Privacy-Preserving Parameter-Efficient Fine-Tuning for Large Language Model Services","created":"2023-05-10","cited_by_count":43,"area":null},{"id":"2405.15012","title":"Extracting Prompts by Inverting LLM Outputs","created":"2024-05-23","cited_by_count":41,"area":null},{"id":"2311.04044","title":"PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models","created":"2023-11-07","cited_by_count":37,"area":null},{"id":"2307.04401","title":"Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation","created":"2023-07-10","cited_by_count":36,"area":null},{"id":"2209.03463","title":"Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots","created":"2022-09-07","cited_by_count":33,"area":null},{"id":"2407.18369","title":"AI Safety in Generative AI Large Language Models: A Survey","created":"2024-07-06","cited_by_count":33,"area":null},{"id":"2307.08925","title":"Integration of Large Language Models and Federated Learning","created":"2023-07-18","cited_by_count":31,"area":null},{"id":"2206.05483","title":"Bilateral Dependency Optimization: Defending Against Model-inversion Attacks","created":"2022-06-11","cited_by_count":30,"area":null},{"id":"2310.01424","title":"Identifying and Mitigating Privacy Risks Stemming from Language Models: A Survey","created":"2023-09-27","cited_by_count":30,"area":null},{"id":"2402.04013","title":"Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses","created":"2024-02-06","cited_by_count":30,"area":null},{"id":"2401.12192","title":"Text Embedding Inversion Security for Multilingual Language Models","created":"2024-01-22","cited_by_count":26,"area":null},{"id":"2401.00676","title":"Digger: Detecting Copyright Content Mis-usage in Large Language Model Training","created":"2024-01-01","cited_by_count":25,"area":null},{"id":"2406.10280","title":"Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries","created":"2024-06-12","cited_by_count":25,"area":null},{"id":"2311.17431","title":"Grounding Foundation Models through Federated Transfer Learning: A General Framework","created":"2023-11-29","cited_by_count":24,"area":null},{"id":"2406.05392","title":"Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey","created":"2024-06-08","cited_by_count":23,"area":null},{"id":"2108.12318","title":"CAPE: Context-Aware Private Embeddings for Private Language Learning","created":"2021-08-27","cited_by_count":22,"area":null},{"id":"2203.13920","title":"Canary Extraction in Natural Language Understanding Models","created":"2022-03-25","cited_by_count":21,"area":null},{"id":"2209.01100","title":"Group Property Inference Attacks Against Graph Neural Networks","created":"2022-09-02","cited_by_count":21,"area":null},{"id":"2310.07152","title":"No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML","created":"2023-10-11","cited_by_count":21,"area":null},{"id":"2108.06910","title":"A Novel Attribute Reconstruction Attack in Federated Learning","created":"2021-08-16","cited_by_count":18,"area":null},{"id":"2408.10053","title":"Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory","created":"2024-08-19","cited_by_count":18,"area":null},{"id":"2507.16372","title":"Depth Gives a False Sense of Privacy: LLM Internal States Inversion","created":"2025-07-22","cited_by_count":17,"area":null},{"id":"2205.04605","title":"Sentence-level Privacy for Document Embeddings","created":"2022-05-10","cited_by_count":16,"area":null},{"id":"2205.10095","title":"How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing","created":"2022-05-20","cited_by_count":16,"area":null},{"id":"2302.09270","title":"Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements","created":"2023-02-18","cited_by_count":14,"area":null},{"id":"2306.01471","title":"Guiding Text-to-Text Privatization by Syntax","created":"2023-06-02","cited_by_count":14,"area":null},{"id":"2404.03324","title":"A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking The Privacy-Utility Trade-off","created":"2024-04-04","cited_by_count":13,"area":null},{"id":"2405.20681","title":"No Free Lunch Theorem for Privacy-Preserving LLM Inference","created":"2024-05-31","cited_by_count":13,"area":null},{"id":"2204.09391","title":"You Are What You Write: Preserving Privacy in the Era of Large Language Models","created":"2022-04-20","cited_by_count":12,"area":null},{"id":"2208.08140","title":"Differential Privacy in Natural Language Processing: The Story So Far","created":"2022-08-17","cited_by_count":12,"area":null},{"id":"2403.14111","title":"HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption","created":"2024-03-21","cited_by_count":12,"area":null},{"id":"2109.10104","title":"InvBERT: Reconstructing Text from Contextualized Word Embeddings by inverting the BERT pipeline","created":"2021-09-21","cited_by_count":11,"area":null},{"id":"2312.15603","title":"A Split-and-Privatize Framework for Large Language Model Fine-Tuning","created":"2023-12-25","cited_by_count":11,"area":null},{"id":"2411.10023","title":"Model Inversion Attacks: A Survey of Approaches and Countermeasures","created":"2024-11-15","cited_by_count":11,"area":null},{"id":"2502.00837","title":"Explainability in Practice: A Survey of Explainable NLP Across Various Domains","created":"2025-02-02","cited_by_count":11,"area":null},{"id":"2310.16538","title":"FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning","created":"2023-10-25","cited_by_count":10,"area":null},{"id":"2405.11916","title":"Information Leakage from Embedding in Large Language Models","created":"2024-05-20","cited_by_count":10,"area":null},{"id":"2205.06135","title":"Fair NLP Models with Differentially Private Text Encoders","created":"2022-05-12","cited_by_count":9,"area":null},{"id":"2210.02574","title":"Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption","created":"2022-10-05","cited_by_count":9,"area":null},{"id":"2312.17342","title":"SentinelLMs: Encrypted Input Adaptation and Fine-tuning of Language Models for Private and Secure Inference","created":"2023-12-28","cited_by_count":9,"area":null},{"id":"2506.17090","title":"Better Language Model Inversion by Compactly Representing Next-Token Distributions","created":"2025-06-20","cited_by_count":9,"area":null},{"id":"2105.10909","title":"Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs","created":"2021-05-23","cited_by_count":8,"area":null},{"id":"2210.11049","title":"How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers","created":"2022-10-20","cited_by_count":8,"area":null},{"id":"2411.05034","title":"Eguard: Defending LLM Embeddings Against Inversion Attacks via Text Mutual Information Optimization","created":"2024-11-06","cited_by_count":8,"area":null},{"id":"2504.00147","title":"Universal Zero-shot Embedding Inversion","created":"2025-03-31","cited_by_count":8,"area":null},{"id":"2304.02932","title":"Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding","created":"2023-04-06","cited_by_count":7,"area":null},{"id":"2402.08227","title":"Privacy-Preserving Language Model Inference with Instance Obfuscation","created":"2024-02-13","cited_by_count":7,"area":null},{"id":"2410.13237","title":"Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis","created":"2024-10-17","cited_by_count":7,"area":null},{"id":"2511.06571","title":"Rep2Text: Decoding Full Text from a Single LLM Token Representation","created":"2025-11-09","cited_by_count":7,"area":null},{"id":"2107.01806","title":"Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systems","created":"2021-07-05","cited_by_count":6,"area":null},{"id":"2403.04124","title":"Privacy-preserving Fine-tuning of Large Language Models through Flatness","created":"2024-03-07","cited_by_count":6,"area":null},{"id":"2412.10652","title":"CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference","created":"2024-12-14","cited_by_count":6,"area":null},{"id":"2503.07501","title":"Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond","created":"2025-03-10","cited_by_count":6,"area":null},{"id":"2507.07700","title":"Rethinking the Privacy of Text Embeddings: A Reproducibility Study of \"Text Embeddings Reveal (Almost) As Much As Text\"","created":"2025-07-10","cited_by_count":6,"area":null},{"id":"2311.07585","title":"Input Reconstruction Attack against Vertical Federated Large Language Models","created":"2023-11-07","cited_by_count":5,"area":null},{"id":"2410.21637","title":"Mitigating Paraphrase Attacks on Machine-Text Detectors via Paraphrase Inversion","created":"2024-10-29","cited_by_count":5,"area":null},{"id":"2505.05922","title":"Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy","created":"2025-05-09","cited_by_count":5,"area":null},{"id":"2508.03097","title":"VFLAIR-LLM: A Comprehensive Framework and Benchmark for Split Learning of LLMs","created":"2025-08-05","cited_by_count":5,"area":null},{"id":"2510.12950","title":"An Investigation of Memorization Risk in Healthcare Foundation Models","created":"2025-10-14","cited_by_count":5,"area":null},{"id":"2501.10985","title":"GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models","created":"2025-01-19","cited_by_count":4,"area":null},{"id":"2502.11308","title":"ALGEN: Few-shot Inversion Attacks on Textual Embeddings using Alignment and Generation","created":"2025-02-16","cited_by_count":4,"area":null},{"id":"2504.17421","title":"Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks","created":"2025-04-24","cited_by_count":4,"area":null},{"id":"2504.21117","title":"Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts","created":"2025-04-29","cited_by_count":4,"area":null},{"id":"2510.02348","title":"mini-vec2vec: Scaling Universal Geometry Alignment with Linear Transformations","created":"2025-09-27","cited_by_count":4,"area":null},{"id":"2201.06093","title":"Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)","created":"2022-01-16","cited_by_count":3,"area":null},{"id":"2312.14480","title":"MetaAID 2.5: A Secure Framework for Developing Metaverse Applications via Large Language Models","created":"2023-12-22","cited_by_count":3,"area":null},{"id":"2402.13146","title":"OLViT: Multi-Modal State Tracking via Attention-Based Embeddings for Video-Grounded Dialog","created":"2024-02-20","cited_by_count":3,"area":null},{"id":"2404.16587","title":"Understanding Privacy Risks of Embeddings Induced by Large Language Models","created":"2024-04-25","cited_by_count":3,"area":null},{"id":"2407.11059","title":"Was it Slander? Towards Exact Inversion of Generative Language Models","created":"2024-07-10","cited_by_count":3,"area":null},{"id":"2411.03663","title":"Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach","created":"2024-11-06","cited_by_count":3,"area":null},{"id":"2411.09945","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","created":"2024-11-15","cited_by_count":3,"area":null},{"id":"2310.10124","title":"A Comprehensive Study of Privacy Risks in Curriculum Learning","created":"2023-10-16","cited_by_count":2,"area":null},{"id":"2406.02027","title":"Inference Attacks: A Taxonomy, Survey, and Promising Directions","created":"2024-06-04","cited_by_count":2,"area":null},{"id":"2407.01110","title":"SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest","created":"2024-07-01","cited_by_count":2,"area":null},{"id":"2502.00451","title":"Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities","created":"2025-02-01","cited_by_count":2,"area":null},{"id":"2505.18332","title":"An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs","created":"2025-05-23","cited_by_count":2,"area":null},{"id":"2602.07090","title":"Concept-Aware Privacy Mechanisms for Defending Embedding Inversion Attacks","created":"2026-02-06","cited_by_count":2,"area":null},{"id":"2606.14210","title":"From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models","created":"2026-06-12","cited_by_count":2,"area":null},{"id":"2209.01538","title":"Data Provenance via Differential Auditing","created":"2022-09-04","cited_by_count":1,"area":null},{"id":"2212.06264","title":"Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems","created":"2022-12-12","cited_by_count":1,"area":null}],"next_cursor":"100"}