Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems | Paper Citation Record

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This paper cites A Comprehensive Overview of Large Language Models.ACM Transactions on Intelligent Systems and Technology, 16(5):1–72, 2025.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems A Comprehensive Overview of Large Language Models.ACM Transactions on Intelligent Systems and Technology, 16(5):1–72, 2025

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This paper cites Large language models: a survey of their development, capabilities, and applications.Knowledge and Information Systems, 67(3):2967–3022, 2025.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Large language models: a survey of their development, capabilities, and applications.Knowledge and Information Systems, 67(3):2967–3022, 2025

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This paper cites A review of prominent paradigms for LLM-based agents: Tool use, planning (including RAG), and feedback learning.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems A review of prominent paradigms for LLM-based agents: Tool use, planning (including RAG), and feedback learning

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This paper cites Know your RAG: Dataset taxonomy and generation strategies for evaluating RAG systems.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Know your RAG: Dataset taxonomy and generation strategies for evaluating RAG systems

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This paper cites MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation

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This paper cites FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems

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This paper cites A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Unresolved cited work

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Unresolved cited work

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This paper cites CRAG - Comprehensive RAG Benchmark.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems CRAG - Comprehensive RAG Benchmark

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This paper cites Communication- Efficient Learning of Deep Networks from Decentralized Data.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Communication- Efficient Learning of Deep Networks from Decentralized Data

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This paper cites Federated Learning: Challenges, Methods, and Future Directions.IEEE Signal Processing Magazine, 37(3):50–60, 2020.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Federated Learning: Challenges, Methods, and Future Directions.IEEE Signal Processing Magazine, 37(3):50–60, 2020

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This paper cites Nguyen, Ming Ding, Pubudu N.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Nguyen, Ming Ding, Pubudu N

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This paper cites Sybil-aware adaptive defence framework for robust federated learning.Pervasive and Mobile Computing, page 102157, 2025.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Sybil-aware adaptive defence framework for robust federated learning.Pervasive and Mobile Computing, page 102157, 2025

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This paper cites Efficient Federated Search for Retrieval-Augmented Generation.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Efficient Federated Search for Retrieval-Augmented Generation

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This paper cites Adversarial Attacks on Large Language Models: A Survey.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Adversarial Attacks on Large Language Models: A Survey

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This paper cites SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses

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This paper cites The good and the bad: Exploring privacy issues in retrieval-augmented generation (RAG).

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems The good and the bad: Exploring privacy issues in retrieval-augmented generation (RAG)

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This paper cites Surveying the RAG Attack Surface and Defenses: Protecting Sensitive Company Data.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Surveying the RAG Attack Surface and Defenses: Protecting Sensitive Company Data

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This paper cites SafeRAG: Benchmarking security in retrieval-augmented generation of large language model.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems SafeRAG: Benchmarking security in retrieval-augmented generation of large language model

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Retrieval-Augmented Generation: A Survey of Security Challenges and Countermeasures

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This paper cites {PoisonedRAG}: Knowledge corruption attacks to {Retrieval-Augmented} generation of large language models.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems {PoisonedRAG}: Knowledge corruption attacks to {Retrieval-Augmented} generation of large language models

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Traceback of Poisoning Attacks to Retrieval-Augmented Generation

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

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This paper cites Luan, Siran Wang, and Yuntao Wang.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Luan, Siran Wang, and Yuntao Wang

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Privacy-Aware RAG-Enabled LLMs for Collaborative AI in Organizations

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Trusted Execution Environments: Properties, Applications, and Challenges.IEEE Security & Privacy, 18(2):56–60, 2020

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Ai on the edge: a comprehensive review

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems A Survey of AI Inference Technologies for On-Device Systems.IEEE Internet of Things Journal, 12(24):51927–51950, 2025

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Towards On-device Personalization: Cloud-device Collaborative Data Augmentation for Efficient On-device Language Model.ACM Transactions on Intelligent Systems and Technology, 2025

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Federated retrieval-augmented generation: A systematic mapping study

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Unresolved cited work

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems The Language Model Revolution: LLM and SLM Analysis

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Edge ai: A comprehensive survey of technologies, applications, and challenges

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems A Survey of Retrieval-Augmented Generation (RAG) for Large Language Models

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Graph-Based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey.ACM Computing Surveys, 2025

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Backdoored Retrievers for Prompt Injection Attacks on Retrieval Augmented Generation of Large Language Models

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Unresolved cited work

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems The Hidden Threat in Plain Text: Attacking RAG Data Loaders

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems RAG LLMs are not safer: A safety analysis of retrieval-augmented generation for large language models

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Alignment faking in large language models

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Visual contextual attack: Jailbreaking MLLMs with image-driven context injection

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Shaping the safety boundaries: Understanding and defending against jailbreaks in large language models

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Unresolved cited work

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Retrieval Poisoning Attacks Based on Prompt Injections into Retrieval-Augmented Generation Systems that Store Generated Responses

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Retrieval-Augmented Defense: Adaptive and Controllable Jailbreak Prevention for Large Language Models

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems LatentPoison - Adversarial Attacks On The Latent Space

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Observation cc6a2e81-4e8e-4805-a58d-10047112b5dd · outbound

This paper cites Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method

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Observation 585a757a-cd6b-4159-ad37-84eed6bb2e24 · outbound

This paper cites Mask-based Membership Inference Attacks for Retrieval- Augmented Generation.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Mask-based Membership Inference Attacks for Retrieval- Augmented Generation

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Observation fb9d8575-dbb7-4a7c-80f7-073328c62de8 · outbound

This paper cites Flippedrag: Black-box opinion manipulation adversarial attacks to retrieval-augmented generation models.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Flippedrag: Black-box opinion manipulation adversarial attacks to retrieval-augmented generation models

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Observation 68b6fda8-c42d-4430-bf94-aff1ec0a304a · outbound

This paper cites Membership Inference Attacks Against Machine Learning Models.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Membership Inference Attacks Against Machine Learning Models

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Observation 3be6ef3a-8a40-45ce-9c14-278b80f2a010 · outbound

This paper cites Practical poisoning attacks against retrieval-augmented generation.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Practical poisoning attacks against retrieval-augmented generation

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Observation e3b3df22-4096-484f-98d3-21d8bfdd481f · outbound

This paper cites Drift Detection in Text Data with Document Embeddings.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Drift Detection in Text Data with Document Embeddings

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Observation af95b4b7-12e1-4661-a0e1-23534de81fcc · outbound

This paper cites Enhancing adversarial resilience in semantic caching for secure retrieval augmented generation systems.Scientific Reports, 16(1):5936, 2026.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Enhancing adversarial resilience in semantic caching for secure retrieval augmented generation systems.Scientific Reports, 16(1):5936, 2026

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Observation 355194d4-8e8a-4395-8139-b33fb4a6009b · outbound

This paper cites Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks

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Observation a06c6220-4bae-4214-864c-7ec085aefb4f · outbound

This paper cites DeRAG: Black-box Adversarial Attacks on Multiple Retrieval-Augmented Generation Applications via Prompt Injection.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems DeRAG: Black-box Adversarial Attacks on Multiple Retrieval-Augmented Generation Applications via Prompt Injection

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Observation ec2a03fa-c170-43ba-bae6-27b26389a02e · outbound

This paper cites Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang

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Observation 86138362-59e5-4608-8386-89b09d002b53 · outbound

This paper cites Out-of-context and out-of-scope: Manip- ulating large language models through minimal instruction set modifications.PLoS One, 21(2):e0341558, 2026.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Out-of-context and out-of-scope: Manip- ulating large language models through minimal instruction set modifications.PLoS One, 21(2):e0341558, 2026

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Observation 376817ae-9b55-476d-9bf3-ca9310a75f7b · outbound

This paper cites Chi, Nathanael Schärli, and Denny Zhou.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Chi, Nathanael Schärli, and Denny Zhou

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Observation dc89838f-8cf8-41cc-ad7e-419b6f08918f · outbound

This paper cites Knowledge conflicts for LLMs: A survey.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Knowledge conflicts for LLMs: A survey

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Observation 086bd0f9-bd2e-4703-ae2c-e5f7904eaa13 · outbound

This paper cites When not to trust language models: Investigating effectiveness of parametric and non-parametric memories.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems When not to trust language models: Investigating effectiveness of parametric and non-parametric memories

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Observation adf6f34d-2b8d-40d2-b028-a2092c4d3b8a · outbound

This paper cites Adversarial and Multilingual Threats in Retrieval-Augmented Generation: From Prompt Injection to Model Exploitation.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Adversarial and Multilingual Threats in Retrieval-Augmented Generation: From Prompt Injection to Model Exploitation

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Observation 6daa5d35-cead-46eb-a7c9-f0a198ed971b · outbound

This paper cites Enhancing noise robustness of retrieval-augmented language models with adaptive adversarial training.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Enhancing noise robustness of retrieval-augmented language models with adaptive adversarial training

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Observation f15c43b9-9a76-4c31-9627-a244fd6f5568 · outbound

This paper cites Information Leakage in Embedding Models.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Information Leakage in Embedding Models

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Observation 39d223bf-d006-4f49-ba1e-dec48029f763 · outbound

This paper cites Reverse engineering convolutional neural networks through side-channel information leaks.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Reverse engineering convolutional neural networks through side-channel information leaks

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Observation 62ef88c7-87b6-419f-99c5-d0c6b443e0b2 · outbound

This paper cites Deep learning model inversion attacks and defenses: a comprehensive survey.Artificial Intelligence Review, 58(8):242, 2025.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Deep learning model inversion attacks and defenses: a comprehensive survey.Artificial Intelligence Review, 58(8):242, 2025

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Observation 67a38e22-ebd3-4b1a-86f3-8fb7a7a992ab · outbound

This paper cites Federated retrieval augmented generation for multi-product question answering.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Federated retrieval augmented generation for multi-product question answering

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Observation 4d61a30e-162c-4187-bc1f-a753189f3aa9 · outbound

This paper cites The Limitations of Federated Learning in Sybil Settings.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems The Limitations of Federated Learning in Sybil Settings

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Observation 04c3a62e-eaa8-4e70-a22c-0111d02e41fa · outbound

This paper cites Federated Retrieval- Augmented Generation-Based LLM for Enhanced Cyber Threat Detection in the Internet-of-Energy.IEEE Network, 40(1):13–19, 2026.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Federated Retrieval- Augmented Generation-Based LLM for Enhanced Cyber Threat Detection in the Internet-of-Energy.IEEE Network, 40(1):13–19, 2026

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Observation ebdff9c0-c501-4670-b8f2-9a42ab3574eb · outbound

This paper cites Sybil Attacks and Defense on Differential Privacy based Federated Learning.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Sybil Attacks and Defense on Differential Privacy based Federated Learning

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Observation 3a37850d-a705-408f-b07b-93901c8d4377 · outbound

This paper cites A Survey of Federated Learning: Advances in Architecture, Synchronization, and Security Threats.Computers, Materials & Continua, 86(3), 2026.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems A Survey of Federated Learning: Advances in Architecture, Synchronization, and Security Threats.Computers, Materials & Continua, 86(3), 2026

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Observation 911e040c-5648-4958-961b-590f20e254b5 · outbound

This paper cites Privacy protection in RAG: A novel method and evaluation framework.Information Processing & Management, 63(3):104505, 2026.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Privacy protection in RAG: A novel method and evaluation framework.Information Processing & Management, 63(3):104505, 2026

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Observation f5060461-1729-42da-a485-d15084c85d2c · outbound

This paper cites Efficient Byzantine-Robust and Privacy-Preserving Federated Learning on Compressive Domain.IEEE Internet of Things Journal, 11(4):7116–7127, 2024.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Efficient Byzantine-Robust and Privacy-Preserving Federated Learning on Compressive Domain.IEEE Internet of Things Journal, 11(4):7116–7127, 2024

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Observation 35ee7830-eb26-4f30-9b02-6641b09e8f17 · outbound

This paper cites an unresolved cited work.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Unresolved cited work

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Observation 2d8124c1-f7a2-4c01-97fc-6fc682f74aa2 · outbound

This paper cites RAG-Guardrails Integration for AI Content Control.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems RAG-Guardrails Integration for AI Content Control

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Observation 7e140847-505c-45aa-9473-eb0f3696b34d · outbound

This paper cites Innovative Guardrails for Generative AI: Designing an Intelligent Filter for Safe and Responsible LLM Deployment.Applied Sciences, 15(13):7298, 2025.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Innovative Guardrails for Generative AI: Designing an Intelligent Filter for Safe and Responsible LLM Deployment.Applied Sciences, 15(13):7298, 2025

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Observation 936a4e58-2548-4fef-81c2-1d78aef106ac · outbound

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Guardrails for Large Language Models: A Review of Techniques and Challenges.J Artif Intell Mach Learn & Data Sci, 3(1):2504–2512, 2025

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Observation 6e6b1cf9-b267-4e30-9835-18a2d8935cbe · outbound

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey.IEEE Access, 9:8512–8545, 2021

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Observation 587d2a3f-4076-4ac4-b2e6-ceda7d51888e · outbound

This paper cites BAG-RAG: Bidirectional Retrieval-Augmented Generation Based on Multi-Layer Semantic Graphs for Budget Auditing QA.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems BAG-RAG: Bidirectional Retrieval-Augmented Generation Based on Multi-Layer Semantic Graphs for Budget Auditing QA

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Observation 05989de4-7654-493a-aea0-09f7e11afebc · outbound

This paper cites LAIR: A Language For Automated Semantics- aware Text Sanitization based on Frame Semantics.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems LAIR: A Language For Automated Semantics- aware Text Sanitization based on Frame Semantics

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Observation 89b7c3e6-5721-47b1-9866-c62767f82d43 · outbound

This paper cites Making pre-trained language models better few-shot learners.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Making pre-trained language models better few-shot learners

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Observation 6d0aaac1-9e53-47e0-a7b8-9029663ee3d8 · outbound

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems A Survey on Differential Privacy for Unstructured Data Content.ACM Computing Surveys (CSUR), 54(10s):1–28, 2022

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Observation 7901cbec-c012-4c23-9255-3a2a15511ded · outbound

This paper cites RAG with Differential Privacy.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems RAG with Differential Privacy

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Observation 6220f9b8-0a56-4d31-801b-5b1ea3940926 · outbound

This paper cites Textual Differential Privacy for Context-Aware Reasoning with Large Language Model.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Textual Differential Privacy for Context-Aware Reasoning with Large Language Model

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Observation b60f5832-9d93-4283-98b3-8d8297dc799c · outbound

This paper cites DF-RAG: A Dual Federated Retrieval-Augmented Generation Framework for Collaborative Medical AI.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems DF-RAG: A Dual Federated Retrieval-Augmented Generation Framework for Collaborative Medical AI

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Observation 94fd3288-1151-48e1-837c-7249eaedda72 · outbound

This paper cites Software Architecture for Federated Retrieval-Augmented Clinical QA System Using IoT for Continuous Monitoring.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Software Architecture for Federated Retrieval-Augmented Clinical QA System Using IoT for Continuous Monitoring

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Observation 80d85b26-aaf9-4a84-8b8a-734c5d43569f · outbound

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Optimizing Legal Information Access: Federated Search and RAG for Secure AI-Powered Legal Solutions

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Observation 9782ad95-61d0-4ee1-9e92-157c0ef790ac · outbound

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Privacy-Preserving Data Sharing with Personalized Encrypted Retrieval.Applied Sciences, 16(6):2771, 2026

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Observation 894ecde6-8a6b-49e7-b879-f86e34a3c481 · outbound

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Leveraging Searchable Encryption through Homomorphic Encryption: A Comprehensive Analysis.Mathematics, 11(13):2948, 2023

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Observation 4dab0e5c-9bbc-4bd6-8f18-498e809f27ca · outbound

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems RemoteRAG: A privacy-preserving LLM cloud RAG service

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Observation 9d93f8cf-8575-4f7e-bf4a-99c09595537a · outbound

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Confidential Computing Using Trusted Execution Environments.International Journal of AI, BigData, Computational and Management Studies, 4(2):97–110, 2023

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Observation 0f70f3e3-2b41-4f7f-924f-74d1ab504bd6 · outbound

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Don’t forget private retrieval: distributed private similarity search for large language models

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Observation a8e57981-c0c9-4a12-80a3-783e0bc8e562 · outbound

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Unresolved cited work

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Secure Multi-Party Computation: Theory, practice and applications.Information Sciences, 476:357–372, 2019

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Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Privacy-preserving aggregation in federated learning: A survey.IEEE Transactions on Big Data, 2022

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Observation 8e11bb20-b71d-4cca-8bd1-dc206088f4e2 · outbound

This paper cites Secure multi-party computation (SMPC) protocols and privacy.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Secure multi-party computation (SMPC) protocols and privacy

Reference 100

Resolution: unresolved; no resolver link, observed 2026-06-25T20:56:20.603088Z

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Observation 534a9e37-9a30-46c9-8acc-6cb75b6d0095 · outbound

This paper cites Secure Multi-Party Computation for Machine Learning: A Survey.IEEE Access, 12:53881–53899, 2024.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems Secure Multi-Party Computation for Machine Learning: A Survey.IEEE Access, 12:53881–53899, 2024

Reference 101

Resolution: unresolved; no resolver link, observed 2026-06-25T20:56:20.603088Z

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Unavailable: canonical work link unavailable.

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