{"total":111,"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":"2305.17493","title":"The Curse of Recursion: Training on Generated Data Makes Models Forget","created":"2023-05-27","cited_by_count":356,"area":"diffusion"},{"id":"2104.10706","title":"Dataset Inference: Ownership Resolution in Machine Learning","created":"2021-04-21","cited_by_count":116,"area":"safety"},{"id":"2502.02542","title":"OverThink: Slowdown Attacks on Reasoning LLMs","created":"2025-02-04","cited_by_count":96,"area":"lr"},{"id":"2301.11047","title":"A Systematic Review of Green AI","created":"2023-01-26","cited_by_count":77,"area":null},{"id":"2104.09667","title":"Manipulating SGD with Data Ordering Attacks","created":"2021-04-19","cited_by_count":74,"area":"safety"},{"id":"2106.09898","title":"Bad Characters: Imperceptible NLP Attacks","created":"2021-06-18","cited_by_count":71,"area":null},{"id":"2401.11170","title":"Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images","created":"2024-01-20","cited_by_count":69,"area":"mllm"},{"id":"2401.05778","title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","created":"2024-01-11","cited_by_count":66,"area":null},{"id":"2504.17704","title":"Safety in Large Reasoning Models: A Survey","created":"2025-04-24","cited_by_count":64,"area":"lr"},{"id":"2307.10719","title":"LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?","created":"2023-07-20","cited_by_count":49,"area":null},{"id":"2106.15764","title":"The Threat of Offensive AI to Organizations","created":"2021-06-30","cited_by_count":38,"area":null},{"id":"2305.18339","title":"A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions","created":"2023-05-25","cited_by_count":35,"area":null},{"id":"2410.10760","title":"Denial-of-Service Poisoning Attacks against Large Language Models","created":"2024-10-14","cited_by_count":35,"area":null},{"id":"2412.13879","title":"Crabs: Consuming Resource via Auto-generation for LLM-DoS Attack under Black-box Settings","created":"2024-12-18","cited_by_count":35,"area":null},{"id":"2203.08147","title":"Energy-Latency Attacks via Sponge Poisoning","created":"2022-03-14","cited_by_count":29,"area":null},{"id":"2205.13618","title":"Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object Detectors","created":"2022-05-26","cited_by_count":28,"area":null},{"id":"2409.14457","title":"Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends","created":"2024-09-22","cited_by_count":26,"area":null},{"id":"2206.07840","title":"Architectural Backdoors in Neural Networks","created":"2022-06-15","cited_by_count":25,"area":null},{"id":"2512.07086","title":"ThinkTrap: Denial-of-Service Attacks against Black-box LLM Services via Infinite Thinking","created":"2025-12-08","cited_by_count":23,"area":"lr"},{"id":"2104.13061","title":"Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity","created":"2021-04-27","cited_by_count":19,"area":null},{"id":"2505.18893","title":"Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects","created":"2025-05-24","cited_by_count":19,"area":null},{"id":"2410.08190","title":"Poison-splat: Computation Cost Attack on 3D Gaussian Splatting","created":"2024-10-10","cited_by_count":18,"area":null},{"id":"2311.09994","title":"Towards more Practical Threat Models in Artificial Intelligence Security","created":"2023-11-16","cited_by_count":17,"area":null},{"id":"2405.05990","title":"Special Characters Attack: Toward Scalable Training Data Extraction From Large Language Models","created":"2024-05-09","cited_by_count":17,"area":null},{"id":"2405.19524","title":"AI Risk Management Should Incorporate Both Safety and Security","created":"2024-05-29","cited_by_count":17,"area":null},{"id":"2312.09520","title":"SlowTrack: Increasing the Latency of Camera-based Perception in Autonomous Driving Using Adversarial Examples","created":"2023-12-15","cited_by_count":15,"area":null},{"id":"2404.16557","title":"Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samples","created":"2024-04-25","cited_by_count":14,"area":"mllm"},{"id":"2304.05370","title":"Overload: Latency Attacks on Object Detection for Edge Devices","created":"2023-04-11","cited_by_count":13,"area":null},{"id":"2305.03888","title":"Energy-Latency Attacks to On-Device Neural Networks via Sponge Poisoning","created":"2023-05-06","cited_by_count":10,"area":null},{"id":"2106.00660","title":"Markpainting: Adversarial Machine Learning meets Inpainting","created":"2021-06-01","cited_by_count":9,"area":"safety"},{"id":"2210.00108","title":"ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks","created":"2022-09-30","cited_by_count":9,"area":null},{"id":"2506.14493","title":"LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops","created":"2025-06-17","cited_by_count":9,"area":"mllm"},{"id":"2511.07876","title":"LoopLLM: Transferable Energy-Latency Attacks in LLMs via Repetitive Generation","created":"2025-11-11","cited_by_count":8,"area":null},{"id":"2104.08690","title":"Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems","created":"2021-04-18","cited_by_count":7,"area":null},{"id":"2403.18587","title":"The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision","created":"2024-03-27","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":"2110.15444","title":"10 Security and Privacy Problems in Large Foundation Models","created":"2021-10-28","cited_by_count":6,"area":null},{"id":"2510.02314","title":"StealthAttack: Robust 3D Gaussian Splatting Poisoning via Density-Guided Illusions","created":"2025-10-02","cited_by_count":6,"area":null},{"id":"2512.00520","title":"Toward a Safe Internet of Agents","created":"2025-11-29","cited_by_count":6,"area":"agents"},{"id":"2202.02510","title":"A Survey on Poisoning Attacks Against Supervised Machine Learning","created":"2022-02-05","cited_by_count":5,"area":null},{"id":"2306.07033","title":"When Vision Fails: Text Attacks Against ViT and OCR","created":"2023-06-12","cited_by_count":5,"area":null},{"id":"2503.04963","title":"Energy-Latency Attacks: A New Adversarial Threat to Deep Learning","created":"2025-03-06","cited_by_count":5,"area":null},{"id":"2602.00154","title":"ReasoningBomb: A Stealthy Denial-of-Service Attack by Inducing Pathologically Long Reasoning in Large Reasoning Models","created":"2026-01-29","cited_by_count":5,"area":"lr"},{"id":"2310.19152","title":"BERT Lost Patience Won't Be Robust to Adversarial Slowdown","created":"2023-10-29","cited_by_count":4,"area":null},{"id":"2402.01911","title":"From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers","created":"2024-02-02","cited_by_count":4,"area":null},{"id":"2410.06287","title":"Non-Halting Queries: Exploiting Fixed Points in LLMs","created":"2024-10-08","cited_by_count":4,"area":null},{"id":"2508.19445","title":"On Surjectivity of Neural Networks: Can you elicit any behavior from your model?","created":"2025-08-26","cited_by_count":4,"area":null},{"id":"2601.13082","title":"Adversarial News and Lost Profits: Manipulating Headlines in LLM-Driven Algorithmic Trading","created":"2026-01-19","cited_by_count":4,"area":null},{"id":"2602.08214","title":"RECUR: Resource Exhaustion Attack via Recursive-Entropy Guided Counterfactual Utilization and Reflection","created":"2026-02-09","cited_by_count":4,"area":"lr"},{"id":"2104.15109","title":"Memory-Efficient Deep Learning Inference in Trusted Execution Environments","created":"2021-04-30","cited_by_count":3,"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":"2205.13253","title":"MALICE: Manipulation Attacks on Learned Image ComprEssion","created":"2022-05-26","cited_by_count":3,"area":null},{"id":"2307.00368","title":"Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training","created":"2023-07-01","cited_by_count":3,"area":null},{"id":"2402.06357","title":"The SkipSponge Attack: Sponge Weight Poisoning of Deep Neural Networks","created":"2024-02-09","cited_by_count":3,"area":null},{"id":"2405.05930","title":"Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness","created":"2024-05-09","cited_by_count":3,"area":null},{"id":"2410.15396","title":"The Best Defense is a Good Offense: Countering LLM-Powered Cyberattacks","created":"2024-10-20","cited_by_count":3,"area":null},{"id":"2412.02171","title":"Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices","created":"2024-12-03","cited_by_count":3,"area":null},{"id":"2503.00416","title":"Breaking the Loop: Detecting and Mitigating Denial-of-Service Vulnerabilities in Large Language Models","created":"2025-03-01","cited_by_count":3,"area":null},{"id":"2508.01062","title":"CP-FREEZER: Latency Attacks against Vehicular Cooperative Perception","created":"2025-08-01","cited_by_count":3,"area":null},{"id":"2508.18805","title":"Hidden Tail: Adversarial Image Causing Stealthy Resource Consumption in Vision-Language Models","created":"2025-08-26","cited_by_count":3,"area":"mllm"},{"id":"2512.23779","title":"Prompt-Induced Over-Generation as Denial-of-Service: A Black-Box Attack-Side Benchmark","created":"2025-12-29","cited_by_count":3,"area":null},{"id":"2601.20615","title":"DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning","created":"2026-01-28","cited_by_count":3,"area":"rag"},{"id":"2203.02958","title":"Towards a Responsible AI Development Lifecycle: Lessons From Information Security","created":"2022-03-06","cited_by_count":2,"area":null},{"id":"2212.06123","title":"Security of Deep Reinforcement Learning for Autonomous Driving: A Survey","created":"2022-12-12","cited_by_count":2,"area":"driving"},{"id":"2312.02220","title":"QuantAttack: Exploiting Dynamic Quantization to Attack Vision Transformers","created":"2023-12-03","cited_by_count":2,"area":null},{"id":"2402.02554","title":"DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision Transformers","created":"2024-02-04","cited_by_count":2,"area":null},{"id":"2412.20087","title":"On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs","created":"2024-12-28","cited_by_count":2,"area":null},{"id":"2505.06454","title":"Sponge Attacks on Sensing AI: Energy-Latency Vulnerabilities and Defense via Model Pruning","created":"2025-05-09","cited_by_count":2,"area":null},{"id":"2505.16670","title":"BitHydra: Towards Bit-flip Inference Cost Attack against Large Language Models","created":"2025-05-22","cited_by_count":2,"area":null},{"id":"2507.18053","title":"Resource Consumption Red-Teaming for Large Vision-Language Models","created":"2025-07-24","cited_by_count":2,"area":"mllm"},{"id":"2512.23132","title":"Multi-Agent Framework for Threat Mitigation and Resilience in AI-Based Systems","created":"2025-12-29","cited_by_count":2,"area":"agents"},{"id":"2602.07878","title":"Rethinking Latency Denial-of-Service: Attacking the LLM Serving Framework, Not the Model","created":"2026-02-08","cited_by_count":2,"area":"inference"},{"id":"2603.16068","title":"Resource Consumption Threats in Large Language Models","created":"2026-03-17","cited_by_count":2,"area":null},{"id":"2606.08661","title":"Data Agents Under Attack: Vulnerabilities in LLM-Driven Analytical Systems","created":"2026-06-07","cited_by_count":2,"area":"agents"},{"id":"2108.11299","title":"Certifiers Make Neural Networks Vulnerable to Availability Attacks","created":"2021-08-25","cited_by_count":1,"area":null},{"id":"2212.03980","title":"DDoD: Dual Denial of Decision Attacks on Human-AI Teams","created":"2022-12-07","cited_by_count":1,"area":null},{"id":"2501.08152","title":"Energy Backdoor Attack to Deep Neural Networks","created":"2025-01-14","cited_by_count":1,"area":null},{"id":"2504.08205","title":"EO-VLM: VLM-Guided Energy Overload Attacks on Vision Models","created":"2025-04-11","cited_by_count":1,"area":null},{"id":"2505.18680","title":"$PD^3F$: A Pluggable and Dynamic DoS-Defense Framework Against Resource Consumption Attacks Targeting Large Language Models","created":"2025-05-24","cited_by_count":1,"area":null},{"id":"2506.10831","title":"Efficiency Robustness of Dynamic Deep Learning Systems","created":"2025-06-12","cited_by_count":1,"area":null},{"id":"2506.23296","title":"Securing AI Systems: A Guide to Known Attacks and Impacts","created":"2025-06-29","cited_by_count":1,"area":null},{"id":"2509.10682","title":"LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems","created":"2025-09-12","cited_by_count":1,"area":null},{"id":"2601.19174","title":"SHIELD: An Auto-Healing Agentic Defense Framework for LLM Resource Exhaustion Attacks","created":"2026-01-27","cited_by_count":1,"area":"agents"},{"id":"2603.12796","title":"Spectral Defense Against Resource-Targeting Attack in 3D Gaussian Splatting","created":"2026-03-13","cited_by_count":1,"area":null},{"id":"2605.05846","title":"LoopTrap: Termination Poisoning Attacks on LLM Agents","created":"2026-05-07","cited_by_count":1,"area":"agents"},{"id":"2605.11202","title":"Continuous Discovery of Vulnerabilities in LLM Serving Systems with Fuzzing","created":"2026-05-11","cited_by_count":1,"area":"inference"},{"id":"2606.02643","title":"Inference Cost Attacks for Retrieval-Augmented Large Language Models","created":"2026-05-31","cited_by_count":1,"area":"rag"},{"id":"2606.16751","title":"Automated jailbreak attack targeting multiple defense strategies","created":"2026-06-15","cited_by_count":1,"area":"safety"},{"id":"2607.21804","title":"Adversarial Prompts for Acceptance Collapse in Speculative Decoding","created":"2026-07-23","cited_by_count":1,"area":"inference"},{"id":"2105.03726","title":"Mental Models of Adversarial Machine Learning","created":"2021-05-08","cited_by_count":0,"area":null},{"id":"2107.14601","title":"Who's Afraid of Thomas Bayes?","created":"2021-07-30","cited_by_count":0,"area":null},{"id":"2301.07474","title":"Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy","created":"2023-01-18","cited_by_count":0,"area":null},{"id":"2509.12939","title":"Sy-FAR: Symmetry-based Fair Adversarial Robustness","created":"2025-09-16","cited_by_count":0,"area":null},{"id":"2511.16163","title":"An Image Is Worth Ten Thousand Words: Verbose-Text Induction Attacks on VLMs","created":"2025-11-20","cited_by_count":0,"area":"mllm"},{"id":"2602.17778","title":"Asking Forever: Universal Activations Behind Turn Amplification in Conversational LLMs","created":"2026-02-19","cited_by_count":0,"area":null},{"id":"2603.01454","title":"VidDoS: Universal Denial-of-Service Attack on Video-based Large Language Models","created":"2026-03-02","cited_by_count":0,"area":null},{"id":"2605.10987","title":"AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelines","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2605.14786","title":"Known By Their Actions: Fingerprinting LLM Browser Agents via UI Traces","created":"2026-05-14","cited_by_count":0,"area":"agents"},{"id":"2605.17288","title":"When Efficiency Backfires: Cascading LLMs Trigger Cascade Failure under Adversarial Attack","created":"2026-05-17","cited_by_count":0,"area":null}],"next_cursor":"100"}