{"total":123,"rows":[{"id":"2202.03423","title":"Backdoor Defense via Decoupling the Training Process","created":"2022-02-05","cited_by_count":153,"area":null},{"id":"2403.17710","title":"Optimization-based Prompt Injection Attack to LLM-as-a-Judge","created":"2024-03-26","cited_by_count":143,"area":"alignment"},{"id":"2308.07026","title":"AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning","created":"2023-08-14","cited_by_count":103,"area":null},{"id":"2305.04175","title":"Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning","created":"2023-05-07","cited_by_count":92,"area":"diffusion"},{"id":"2205.13383","title":"BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning","created":"2022-05-26","cited_by_count":86,"area":null},{"id":"2108.11023","title":"EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning","created":"2021-08-25","cited_by_count":73,"area":"safety"},{"id":"2112.05224","title":"Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures","created":"2021-12-09","cited_by_count":73,"area":"safety"},{"id":"2310.04655","title":"VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models","created":"2023-10-07","cited_by_count":68,"area":null},{"id":"2209.11715","title":"The \"Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices","created":"2022-09-23","cited_by_count":63,"area":null},{"id":"2303.03323","title":"CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning","created":"2023-03-06","cited_by_count":63,"area":"safety"},{"id":"2402.09179","title":"Instruction Backdoor Attacks Against Customized LLMs","created":"2024-02-14","cited_by_count":62,"area":"safety"},{"id":"2211.02408","title":"Rickrolling the Artist: Injecting Backdoors into Text Encoders for Text-to-Image Synthesis","created":"2022-11-04","cited_by_count":59,"area":"safety"},{"id":"2303.15180","title":"Detecting Backdoors in Pre-trained Encoders","created":"2023-03-23","cited_by_count":55,"area":"safety"},{"id":"2302.09923","title":"Prompt Stealing Attacks Against Text-to-Image Generation Models","created":"2023-02-20","cited_by_count":51,"area":null},{"id":"2304.02786","title":"UNICORN: A Unified Backdoor Trigger Inversion Framework","created":"2023-04-05","cited_by_count":46,"area":null},{"id":"2201.11692","title":"SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders","created":"2022-01-27","cited_by_count":45,"area":null},{"id":"2210.15127","title":"Rethinking the Reverse-engineering of Trojan Triggers","created":"2022-10-27","cited_by_count":39,"area":"safety"},{"id":"2312.15172","title":"Pre-trained Trojan Attacks for Visual Recognition","created":"2023-12-23","cited_by_count":39,"area":"safety"},{"id":"2409.19232","title":"TrojVLM: Backdoor Attack Against Vision Language Models","created":"2024-09-28","cited_by_count":39,"area":"mllm"},{"id":"2202.06382","title":"Training with More Confidence: Mitigating Injected and Natural Backdoors During Training","created":"2022-02-13","cited_by_count":38,"area":null},{"id":"2212.09067","title":"Fine-Tuning Is All You Need to Mitigate Backdoor Attacks","created":"2022-12-18","cited_by_count":38,"area":null},{"id":"2305.17826","title":"NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models","created":"2023-05-28","cited_by_count":37,"area":"safety"},{"id":"2202.11202","title":"Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning","created":"2022-02-22","cited_by_count":34,"area":"safety"},{"id":"2205.06401","title":"PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning","created":"2022-05-13","cited_by_count":34,"area":"safety"},{"id":"2410.01264","title":"Backdooring Vision-Language Models with Out-Of-Distribution Data","created":"2024-10-02","cited_by_count":33,"area":"mllm"},{"id":"2307.12280","title":"Downstream-agnostic Adversarial Examples","created":"2023-07-23","cited_by_count":32,"area":null},{"id":"2201.07513","title":"Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders","created":"2022-01-19","cited_by_count":31,"area":null},{"id":"2210.07346","title":"An Embarrassingly Simple Backdoor Attack on Self-supervised Learning","created":"2022-10-13","cited_by_count":31,"area":null},{"id":"2211.08229","title":"CorruptEncoder: Data Poisoning based Backdoor Attacks to Contrastive Learning","created":"2022-11-15","cited_by_count":29,"area":"safety"},{"id":"2308.11804","title":"Adversarial Illusions in Multi-Modal Embeddings","created":"2023-08-22","cited_by_count":29,"area":null},{"id":"2510.23883","title":"Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges","created":"2025-10-27","cited_by_count":28,"area":"agents"},{"id":"2304.04512","title":"Defense-Prefix for Preventing Typographic Attacks on CLIP","created":"2023-04-10","cited_by_count":24,"area":"mllm"},{"id":"2201.05889","title":"StolenEncoder: Stealing Pre-trained Encoders in Self-supervised Learning","created":"2022-01-15","cited_by_count":23,"area":null},{"id":"2301.01197","title":"Backdoor Attacks Against Dataset Distillation","created":"2023-01-03","cited_by_count":21,"area":null},{"id":"2505.05528","title":"X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP","created":"2025-05-08","cited_by_count":21,"area":"mllm"},{"id":"2305.18651","title":"UMD: Unsupervised Model Detection for X2X Backdoor Attacks","created":"2023-05-29","cited_by_count":18,"area":null},{"id":"2312.08667","title":"Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey","created":"2023-12-14","cited_by_count":18,"area":null},{"id":"2208.10134","title":"Machine Learning with Confidential Computing: A Systematization of Knowledge","created":"2022-08-22","cited_by_count":15,"area":null},{"id":"2202.05470","title":"Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers","created":"2022-02-11","cited_by_count":14,"area":null},{"id":"2505.23847","title":"Seven Security Challenges in Cross-domain Multi-agent LLM Systems","created":"2025-05-28","cited_by_count":12,"area":"agents"},{"id":"2201.08217","title":"Watermarking Pre-trained Encoders in Contrastive Learning","created":"2022-01-20","cited_by_count":11,"area":null},{"id":"2303.09079","title":"SSL-Cleanse: Trojan Detection and Mitigation in Self-Supervised Learning","created":"2023-03-16","cited_by_count":11,"area":"safety"},{"id":"2409.15968","title":"Adversarial Backdoor Defense in CLIP","created":"2024-09-24","cited_by_count":10,"area":"safety"},{"id":"2209.03563","title":"SSL-WM: A Black-Box Watermarking Approach for Encoders Pre-trained by Self-supervised Learning","created":"2022-09-08","cited_by_count":9,"area":null},{"id":"2211.10908","title":"ESTAS: Effective and Stable Trojan Attacks in Self-supervised Encoders with One Target Unlabelled Sample","created":"2022-11-20","cited_by_count":9,"area":null},{"id":"2306.02368","title":"Revisiting Data-Free Knowledge Distillation with Poisoned Teachers","created":"2023-06-04","cited_by_count":9,"area":"safety"},{"id":"2312.09057","title":"On the Difficulty of Defending Contrastive Learning against Backdoor Attacks","created":"2023-12-14","cited_by_count":9,"area":null},{"id":"2305.09574","title":"UOR: Universal Backdoor Attacks on Pre-trained Language Models","created":"2023-05-16","cited_by_count":8,"area":"safety"},{"id":"2406.03508","title":"Mutual Information Guided Backdoor Mitigation for Pre-trained Encoders","created":"2024-06-05","cited_by_count":8,"area":"safety"},{"id":"2208.08662","title":"Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy","created":"2022-08-18","cited_by_count":7,"area":null},{"id":"2210.01632","title":"Backdoor Attacks in the Supply Chain of Masked Image Modeling","created":"2022-10-04","cited_by_count":7,"area":null},{"id":"2302.09360","title":"Backdoor Attacks to Pre-trained Unified Foundation Models","created":"2023-02-18","cited_by_count":7,"area":"safety"},{"id":"2302.14500","title":"FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases","created":"2023-02-28","cited_by_count":7,"area":"safety"},{"id":"2308.11521","title":"Self-Deception: Reverse Penetrating the Semantic Firewall of Large Language Models","created":"2023-08-16","cited_by_count":7,"area":"safety"},{"id":"2412.06219","title":"Data Free Backdoor Attacks","created":"2024-12-09","cited_by_count":7,"area":null},{"id":"2109.11249","title":"FooBaR: Fault Fooling Backdoor Attack on Neural Network Training","created":"2021-09-23","cited_by_count":6,"area":null},{"id":"2110.15444","title":"10 Security and Privacy Problems in Large Foundation Models","created":"2021-10-28","cited_by_count":6,"area":null},{"id":"2310.00626","title":"GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to Pre-trained Encoders in Self-supervised Learning","created":"2023-10-01","cited_by_count":6,"area":"safety"},{"id":"2311.11796","title":"Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems","created":"2023-11-20","cited_by_count":6,"area":"safety"},{"id":"2409.19526","title":"Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats","created":"2024-09-29","cited_by_count":6,"area":"safety"},{"id":"2411.16154","title":"DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders","created":"2024-11-25","cited_by_count":6,"area":"safety"},{"id":"2503.16872","title":"Lie Detector: Unified Backdoor Detection via Cross-Examination Framework","created":"2025-03-21","cited_by_count":6,"area":null},{"id":"2207.04209","title":"Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain","created":"2022-07-09","cited_by_count":5,"area":null},{"id":"2306.08009","title":"DHBE: Data-free Holistic Backdoor Erasing in Deep Neural Networks via Restricted Adversarial Distillation","created":"2023-06-13","cited_by_count":5,"area":null},{"id":"2401.01911","title":"Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP","created":"2024-01-01","cited_by_count":5,"area":"safety"},{"id":"2405.15269","title":"Test-Time Multimodal Backdoor Detection by Contrastive Prompting","created":"2024-05-24","cited_by_count":5,"area":"safety"},{"id":"2204.09498","title":"Backdooring Explainable Machine Learning","created":"2022-04-20","cited_by_count":4,"area":null},{"id":"2208.03948","title":"AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive Learning","created":"2022-08-08","cited_by_count":4,"area":null},{"id":"2212.03334","title":"Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning","created":"2022-12-06","cited_by_count":4,"area":"safety"},{"id":"2310.07632","title":"Prompt Backdoors in Visual Prompt Learning","created":"2023-10-11","cited_by_count":4,"area":"safety"},{"id":"2402.14977","title":"Mudjacking: Patching Backdoor Vulnerabilities in Foundation Models","created":"2024-02-22","cited_by_count":4,"area":"safety"},{"id":"2404.00076","title":"A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks","created":"2024-03-29","cited_by_count":4,"area":null},{"id":"2511.00446","title":"ToxicTextCLIP: Text-Based Poisoning and Backdoor Attacks on CLIP Pre-training","created":"2025-11-01","cited_by_count":4,"area":"mllm"},{"id":"2111.14309","title":"A General Framework for Defending Against Backdoor Attacks via Influence Graph","created":"2021-11-29","cited_by_count":3,"area":null},{"id":"2301.02905","title":"REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder as a Service","created":"2023-01-07","cited_by_count":3,"area":null},{"id":"2410.18267","title":"Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing","created":"2024-10-23","cited_by_count":3,"area":null},{"id":"2504.11990","title":"Secure Transfer Learning: Training Clean Models Against Backdoor in (Both) Pre-trained Encoders and Downstream Datasets","created":"2025-04-16","cited_by_count":3,"area":"safety"},{"id":"2506.09562","title":"TooBadRL: Trigger Optimization to Boost Effectiveness of Backdoor Attacks on Deep Reinforcement Learning","created":"2025-06-11","cited_by_count":3,"area":"deeprl"},{"id":"2511.04711","title":"SWAP: Towards Copyright Auditing of Soft Prompts via Sequential Watermarking","created":"2025-11-05","cited_by_count":3,"area":"mllm"},{"id":"2210.11082","title":"Apple of Sodom: Hidden Backdoors in Superior Sentence Embeddings via Contrastive Learning","created":"2022-10-20","cited_by_count":2,"area":"safety"},{"id":"2312.07955","title":"Erasing Self-Supervised Learning Backdoor by Cluster Activation Masking","created":"2023-12-13","cited_by_count":2,"area":null},{"id":"2401.00137","title":"SSL-OTA: Unveiling Backdoor Threats in Self-Supervised Learning for Object Detection","created":"2023-12-30","cited_by_count":2,"area":null},{"id":"2405.13080","title":"EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection","created":"2024-05-21","cited_by_count":2,"area":null},{"id":"2407.11372","title":"UNIT: Backdoor Mitigation via Automated Neural Distribution Tightening","created":"2024-07-16","cited_by_count":2,"area":null},{"id":"2408.06825","title":"Membership Inference Attack Against Masked Image Modeling","created":"2024-08-13","cited_by_count":2,"area":"safety"},{"id":"2411.12389","title":"Combinational Backdoor Attack against Customized Text-to-Image Models","created":"2024-11-19","cited_by_count":2,"area":"diffusion"},{"id":"2506.04556","title":"BESA: Boosting Encoder Stealing Attack with Perturbation Recovery","created":"2025-06-05","cited_by_count":2,"area":null},{"id":"2506.07467","title":"Circumventing Backdoor Space via Weight Symmetry","created":"2025-06-09","cited_by_count":2,"area":null},{"id":"2506.22506","title":"SABRE-FL: Selective and Accurate Backdoor Rejection for Federated Prompt Learning","created":"2025-06-25","cited_by_count":2,"area":"safety"},{"id":"2602.17168","title":"BadCLIP++: Stealthy and Persistent Backdoors in Multimodal Contrastive Learning","created":"2026-02-19","cited_by_count":2,"area":"safety"},{"id":"2205.03850","title":"SeqNet: An Efficient Neural Network for Automatic Malware Detection","created":"2022-05-08","cited_by_count":1,"area":null},{"id":"2302.04977","title":"Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelines","created":"2023-02-09","cited_by_count":1,"area":null},{"id":"2308.04673","title":"SSL-Auth: An Authentication Framework by Fragile Watermarking for Pre-trained Encoders in Self-supervised Learning","created":"2023-08-09","cited_by_count":1,"area":null},{"id":"2404.06694","title":"How to Craft Backdoors with Unlabeled Data Alone?","created":"2024-04-10","cited_by_count":1,"area":null},{"id":"2408.01705","title":"Downstream Transfer Attack: Adversarial Attacks on Downstream Models with Pre-trained Vision Transformers","created":"2024-08-03","cited_by_count":1,"area":null},{"id":"2410.01128","title":"Using Interleaved Ensemble Unlearning to Keep Backdoors at Bay for Finetuning Vision Transformers","created":"2024-10-01","cited_by_count":1,"area":"safety"},{"id":"2502.18520","title":"Class-Conditional Neural Polarizer: A Lightweight and Effective Backdoor Defense by Purifying Poisoned Features","created":"2025-02-23","cited_by_count":1,"area":null},{"id":"2503.09095","title":"Backdooring CLIP through Concept Confusion","created":"2025-03-12","cited_by_count":1,"area":"safety"},{"id":"2505.10351","title":"A Unified and Scalable Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability","created":"2025-05-15","cited_by_count":1,"area":"safety"},{"id":"2508.08031","title":"IPBA: Imperceptible Perturbation Backdoor Attack in Federated Self-Supervised Learning","created":"2025-08-11","cited_by_count":1,"area":null}],"next_cursor":"100"}