{"total":46,"rows":[{"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":"2111.04625","title":"DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories","created":"2021-11-08","cited_by_count":42,"area":null},{"id":"2207.13417","title":"Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips","created":"2022-07-27","cited_by_count":22,"area":null},{"id":"2101.08254","title":"RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery","created":"2021-01-20","cited_by_count":18,"area":null},{"id":"2308.07934","title":"One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training","created":"2023-08-12","cited_by_count":14,"area":null},{"id":"2103.13813","title":"RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy","created":"2021-03-22","cited_by_count":11,"area":null},{"id":"2201.09650","title":"What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction","created":"2022-01-24","cited_by_count":11,"area":null},{"id":"2411.13757","title":"GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs","created":"2024-11-21","cited_by_count":10,"area":null},{"id":"2111.01932","title":"HASHTAG: Hash Signatures for Online Detection of Fault-Injection Attacks on Deep Neural Networks","created":"2021-11-02","cited_by_count":9,"area":null},{"id":"2110.07683","title":"Don't Knock! Rowhammer at the Backdoor of DNN Models","created":"2021-10-14","cited_by_count":6,"area":null},{"id":"2209.14243","title":"A Closer Look at Evaluating the Bit-Flip Attack Against Deep Neural Networks","created":"2022-09-28","cited_by_count":5,"area":null},{"id":"2509.21843","title":"SBFA: Single Sneaky Bit Flip Attack to Break Large Language Models","created":"2025-09-26","cited_by_count":5,"area":null},{"id":"2502.07408","title":"Maximal Brain Damage Without Data or Optimization: Disrupting Neural Networks via Sign-Bit Flips","created":"2025-02-11","cited_by_count":4,"area":null},{"id":"2309.06223","title":"Compiled Models, Built-In Exploits: Uncovering Pervasive Bit-Flip Attack Surfaces in DNN Executables","created":"2023-09-12","cited_by_count":3,"area":null},{"id":"2504.13786","title":"On the Relationship Between Robustness and Expressivity of Graph Neural Networks","created":"2025-04-18","cited_by_count":3,"area":null},{"id":"2304.12876","title":"Evaluation of Parameter-based Attacks against Embedded Neural Networks with Laser Injection","created":"2023-04-25","cited_by_count":2,"area":null},{"id":"2305.08034","title":"DNN-Defender: A Victim-Focused In-DRAM Defense Mechanism for Taming Adversarial Weight Attack on DNNs","created":"2023-05-14","cited_by_count":2,"area":null},{"id":"2308.16665","title":"Fault Injection on Embedded Neural Networks: Impact of a Single Instruction Skip","created":"2023-08-31","cited_by_count":2,"area":null},{"id":"2502.16286","title":"Verification of Bit-Flip Attacks against Quantized Neural Networks","created":"2025-02-22","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":"2602.17837","title":"TFL: Targeted Bit-Flip Attack on Large Language Model","created":"2026-02-19","cited_by_count":2,"area":null},{"id":"2112.03477","title":"BDFA: A Blind Data Adversarial Bit-flip Attack on Deep Neural Networks","created":"2021-12-07","cited_by_count":1,"area":null},{"id":"2411.13459","title":"SoK: A Systems Perspective on Compound AI Threats and Countermeasures","created":"2024-11-20","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":"2509.17371","title":"SilentStriker:Toward Stealthy Bit-Flip Attacks on Large Language Models","created":"2025-09-22","cited_by_count":1,"area":null},{"id":"2510.24976","title":"Hammering the Diagnosis: Rowhammer-Induced Stealthy Trojan Attacks on ViT-Based Medical Imaging","created":"2025-10-28","cited_by_count":1,"area":null},{"id":"2510.24985","title":"FaRAccel: FPGA-Accelerated Defense Architecture for Efficient Bit-Flip Attack Resilience in Transformer Models","created":"2025-10-28","cited_by_count":1,"area":null},{"id":"2308.15092","title":"Can We Rely on AI?","created":"2023-08-29","cited_by_count":0,"area":null},{"id":"2311.01205","title":"Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent","created":"2023-11-02","cited_by_count":0,"area":null},{"id":"2404.15784","title":"An Empirical Study of Aegis","created":"2024-04-24","cited_by_count":0,"area":null},{"id":"2408.05857","title":"Comparative Evaluation of Memory Technologies for Synaptic Crossbar Arrays- Part 2: Design Knobs and DNN Accuracy Trends","created":"2024-08-11","cited_by_count":0,"area":null},{"id":"2504.01933","title":"Hessian-aware Training for Enhancing DNNs Resilience to Parameter Corruptions","created":"2025-04-02","cited_by_count":0,"area":null},{"id":"2505.06315","title":"Threat Modeling for AI: The Case for an Asset-Centric Approach","created":"2025-05-08","cited_by_count":0,"area":null},{"id":"2512.09829","title":"RIFT: A Scalable Methodology for LLM Accelerator Fault Assessment using Reinforcement Learning","created":"2025-12-10","cited_by_count":0,"area":null},{"id":"2512.14715","title":"How a Bit Becomes a Story: Semantic Steering via Differentiable Fault Injection","created":"2025-12-09","cited_by_count":0,"area":null},{"id":"2512.22174","title":"BitFlipScope: Scalable Fault Localization and Recovery for Bit-Flip Corruptions in LLMs","created":"2025-12-18","cited_by_count":0,"area":null},{"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":"2603.18120","title":"MAED: Mathematical Activation Error Detection for Mitigating Physical Fault Attacks in DNN Inference","created":"2026-03-18","cited_by_count":0,"area":null},{"id":"2605.18907","title":"Lightweight and Fast Backdoor Model Detection","created":"2026-05-17","cited_by_count":0,"area":null},{"id":"2606.04317","title":"Toward a Generalized Defense Across Sparse, Continuous, and Structured Parameter Attacks","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.10091","title":"SoK: Colluding Adversaries in Machine Learning Pipelines","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2608.15901","title":"Layers Matter: Why Continual Learning Regularization Should Be Layer-Adaptive","created":"2026-08-16","cited_by_count":0,"area":null},{"id":"2608.22716","title":"TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edge","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2609.26280","title":"On the Effect of Bit-Level Parameter Perturbations in Machine Learning and Deep Learning Models","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2609.38477","title":"Security-Enhanced Seed-Based Weight Quantization for Large Language Models","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.04319","title":"GrayShield: Bit-Level Sanitization for Transformer Model Supply-Chain Security","created":"2026-10-03","cited_by_count":0,"area":null}],"next_cursor":null}