{"total":74,"rows":[{"id":"2304.08354","title":"Tool Learning with Foundation Models","created":"2023-04-17","cited_by_count":302,"area":"agents"},{"id":"2402.00888","title":"Security and Privacy Challenges of Large Language Models: A Survey","created":"2024-01-30","cited_by_count":186,"area":"safety"},{"id":"2307.16888","title":"Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection","created":"2023-07-31","cited_by_count":157,"area":"safety"},{"id":"2210.10683","title":"Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP","created":"2022-10-19","cited_by_count":102,"area":null},{"id":"2306.04618","title":"Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations","created":"2023-06-07","cited_by_count":97,"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":"2211.00151","title":"A Close Look into the Calibration of Pre-trained Language Models","created":"2022-10-31","cited_by_count":57,"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":"2406.18844","title":"Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift","created":"2024-06-27","cited_by_count":49,"area":"mllm"},{"id":"2309.06055","title":"Backdoor Attacks and Countermeasures in Natural Language Processing Models: A Comprehensive Security Review","created":"2023-09-12","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":"2410.01264","title":"Backdooring Vision-Language Models with Out-Of-Distribution Data","created":"2024-10-02","cited_by_count":33,"area":"mllm"},{"id":"2310.14480","title":"Attention-Enhancing Backdoor Attacks Against BERT-based Models","created":"2023-10-23","cited_by_count":32,"area":"safety"},{"id":"2307.08208","title":"Towards Stealthy Backdoor Attacks against Speech Recognition via Elements of Sound","created":"2023-07-17","cited_by_count":30,"area":"safety"},{"id":"2305.14910","title":"From Shortcuts to Triggers: Backdoor Defense with Denoised PoE","created":"2023-05-24","cited_by_count":27,"area":null},{"id":"2403.17155","title":"Task-Agnostic Detector for Insertion-Based Backdoor Attacks","created":"2024-03-25","cited_by_count":23,"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":"2405.11227","title":"BadActs: A Universal Backdoor Defense in the Activation Space","created":"2024-05-18","cited_by_count":21,"area":null},{"id":"2302.09457","title":"Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective","created":"2023-02-19","cited_by_count":20,"area":null},{"id":"2308.02122","title":"ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP","created":"2023-08-04","cited_by_count":19,"area":"safety"},{"id":"2310.18633","title":"Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots","created":"2023-10-28","cited_by_count":19,"area":"safety"},{"id":"2407.19845","title":"BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning","created":"2024-07-29","cited_by_count":19,"area":null},{"id":"2503.00596","title":"BadJudge: Backdoor Vulnerabilities of LLM-as-a-Judge","created":"2025-03-01","cited_by_count":18,"area":"safety"},{"id":"2311.11225","title":"TextGuard: Provable Defense against Backdoor Attacks on Text Classification","created":"2023-11-19","cited_by_count":16,"area":"safety"},{"id":"2211.11958","title":"A Survey on Backdoor Attack and Defense in Natural Language Processing","created":"2022-11-22","cited_by_count":14,"area":"safety"},{"id":"2310.18603","title":"Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers","created":"2023-10-28","cited_by_count":14,"area":"safety"},{"id":"2405.07667","title":"Simulate and Eliminate: Revoke Backdoors for Generative Large Language Models","created":"2024-05-13","cited_by_count":12,"area":"safety"},{"id":"2406.00240","title":"Exploring Vulnerabilities and Protections in Large Language Models: A Survey","created":"2024-06-01","cited_by_count":11,"area":"safety"},{"id":"2310.10780","title":"Demystifying Poisoning Backdoor Attacks from a Statistical Perspective","created":"2023-10-16","cited_by_count":10,"area":null},{"id":"2409.00399","title":"Rethinking Backdoor Detection Evaluation for Language Models","created":"2024-08-31","cited_by_count":9,"area":"lr"},{"id":"2412.02454","title":"Gracefully Filtering Backdoor Samples for Generative Large Language Models without Retraining","created":"2024-12-03","cited_by_count":9,"area":"safety"},{"id":"2305.09574","title":"UOR: Universal Backdoor Attacks on Pre-trained Language Models","created":"2023-05-16","cited_by_count":8,"area":"safety"},{"id":"2402.12026","title":"Acquiring Clean Language Models from Backdoor Poisoned Datasets by Downscaling Frequency Space","created":"2024-02-19","cited_by_count":8,"area":"safety"},{"id":"2502.11798","title":"BackdoorDM: A Comprehensive Benchmark for Backdoor Learning on Diffusion Model","created":"2025-02-17","cited_by_count":8,"area":"diffusion"},{"id":"2404.02356","title":"Two Heads are Better than One: Nested PoE for Robust Defense Against Multi-Backdoors","created":"2024-04-02","cited_by_count":7,"area":"moe"},{"id":"2409.14200","title":"Data-centric NLP Backdoor Defense from the Lens of Memorization","created":"2024-09-21","cited_by_count":6,"area":"safety"},{"id":"2402.18945","title":"SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer","created":"2024-02-29","cited_by_count":5,"area":"safety"},{"id":"2407.04151","title":"Securing Multi-turn Conversational Language Models From Distributed Backdoor Triggers","created":"2024-07-04","cited_by_count":5,"area":"safety"},{"id":"2505.15656","title":"Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data Could Be Secretly Stolen!","created":"2025-05-21","cited_by_count":5,"area":"safety"},{"id":"2511.18921","title":"BackdoorVLM: A Benchmark for Backdoor Attacks and Defenses on Vision-Language Models","created":"2025-11-24","cited_by_count":5,"area":"mllm"},{"id":"2402.14977","title":"Mudjacking: Patching Backdoor Vulnerabilities in Foundation Models","created":"2024-02-22","cited_by_count":4,"area":"safety"},{"id":"2508.01647","title":"DUP: Detection-guided Unlearning for Backdoor Purification in Language Models","created":"2025-08-03","cited_by_count":4,"area":"safety"},{"id":"2512.06899","title":"Patronus: Identifying and Mitigating Transferable Backdoors in Pre-trained Language Models","created":"2025-12-07","cited_by_count":4,"area":"safety"},{"id":"2305.18503","title":"From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework","created":"2023-05-29","cited_by_count":3,"area":null},{"id":"2402.07689","title":"OrderBkd: Textual backdoor attack through repositioning","created":"2024-02-12","cited_by_count":3,"area":"safety"},{"id":"2409.15126","title":"UTrace: Poisoning Forensics for Private Collaborative Learning","created":"2024-09-23","cited_by_count":3,"area":null},{"id":"2411.11006","title":"BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense Evaluation","created":"2024-11-17","cited_by_count":3,"area":"safety"},{"id":"2505.23015","title":"Detecting Stealthy Backdoor Samples based on Intra-class Distance for Large Language Models","created":"2025-05-29","cited_by_count":3,"area":"safety"},{"id":"2510.04347","title":"Unmasking Backdoors: An Explainable Defense via Gradient-Attention Anomaly Scoring for Pre-trained Language Models","created":"2025-10-05","cited_by_count":3,"area":"safety"},{"id":"2405.02344","title":"A Backdoor-based Explainable AI Benchmark for High Fidelity Evaluation of Attributions","created":"2024-05-02","cited_by_count":2,"area":null},{"id":"2406.04478","title":"PromptFix: Few-shot Backdoor Removal via Adversarial Prompt Tuning","created":"2024-06-06","cited_by_count":2,"area":"safety"},{"id":"2408.11587","title":"Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks","created":"2024-08-21","cited_by_count":2,"area":"safety"},{"id":"2410.20019","title":"Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions","created":"2024-10-26","cited_by_count":2,"area":"safety"},{"id":"2504.17300","title":"The Ultimate Cookbook for Invisible Poison: Crafting Subtle Clean-Label Text Backdoors with Style Attributes","created":"2025-04-24","cited_by_count":2,"area":"safety"},{"id":"2505.03501","title":"BadLingual: A Novel Lingual-Backdoor Attack against Large Language Models","created":"2025-05-06","cited_by_count":2,"area":"safety"},{"id":"2510.15303","title":"DSSmoothing: Toward Certified Dataset Ownership Verification for Pre-trained Language Models via Dual-Space Smoothing","created":"2025-10-17","cited_by_count":2,"area":"safety"},{"id":"2603.00516","title":"ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Data","created":"2026-02-28","cited_by_count":2,"area":"safety"},{"id":"2310.00648","title":"PETA: Parameter-Efficient Trojan Attacks","created":"2023-10-01","cited_by_count":1,"area":"safety"},{"id":"2407.04179","title":"Defense Against Syntactic Textual Backdoor Attacks with Token Substitution","created":"2024-07-04","cited_by_count":1,"area":"safety"},{"id":"2410.19427","title":"Expose Before You Defend: Unifying and Enhancing Backdoor Defenses via Exposed Models","created":"2024-10-25","cited_by_count":1,"area":null},{"id":"2502.00652","title":"Reformulation is All You Need: Addressing Malicious Text Features in DNNs","created":"2025-02-02","cited_by_count":1,"area":"safety"},{"id":"2509.12386","title":"Amulet: a Python Library for Assessing Interactions Among ML Defenses and Risks","created":"2025-09-15","cited_by_count":1,"area":null},{"id":"2512.10998","title":"SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models","created":"2025-12-10","cited_by_count":1,"area":"safety"},{"id":"2307.11316","title":"Making Pre-trained Language Models both Task-solvers and Self-calibrators","created":"2023-07-21","cited_by_count":0,"area":null},{"id":"2401.15002","title":"BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning","created":"2024-01-26","cited_by_count":0,"area":null},{"id":"2507.13629","title":"Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques","created":"2025-07-18","cited_by_count":0,"area":null},{"id":"2508.15934","title":"Strategic Sample Selection for Improved Clean-Label Backdoor Attacks in Text Classification","created":"2025-08-21","cited_by_count":0,"area":"safety"},{"id":"2509.05318","title":"Backdoor Samples Detection Based on Perturbation Discrepancy Consistency in Pre-trained Language Models","created":"2025-08-30","cited_by_count":0,"area":"safety"},{"id":"2512.00343","title":"Assimilation Matters: Model-level Backdoor Detection in Vision-Language Pretrained Models","created":"2025-11-29","cited_by_count":0,"area":"mllm"},{"id":"2603.11949","title":"Delayed Backdoor Attacks: Exploring the Temporal Dimension as a New Attack Surface in Pre-Trained Models","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2604.27426","title":"Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors","created":"2026-04-30","cited_by_count":0,"area":"safety"},{"id":"2605.11612","title":"When Emotion Becomes Trigger: Emotion-style dynamic Backdoor Attack Parasitising Large Language Models","created":"2026-05-12","cited_by_count":0,"area":"safety"},{"id":"2605.26574","title":"GradSentry: Gradient Spectral Entropy for Backdoor Sample Filtering in Large Language Model Fine-Tuning","created":"2026-05-26","cited_by_count":0,"area":"safety"},{"id":"2608.12623","title":"When Explanations Betray Backdoors: Black-Box Auditing for Language Model Classifiers","created":"2026-08-12","cited_by_count":0,"area":"safety"}],"next_cursor":null}