{"total":50,"rows":[{"id":"2310.12815","title":"Formalizing and Benchmarking Prompt Injection Attacks and Defenses","created":"2023-10-19","cited_by_count":364,"area":"safety"},{"id":"2504.11358","title":"DataSentinel: A Game-Theoretic Detection of Prompt Injection Attacks","created":"2025-04-15","cited_by_count":143,"area":"safety"},{"id":"2403.04957","title":"Automatic and Universal Prompt Injection Attacks against Large Language Models","created":"2024-03-07","cited_by_count":119,"area":"safety"},{"id":"2206.08514","title":"A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks","created":"2022-06-17","cited_by_count":74,"area":"safety"},{"id":"2103.04264","title":"T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification","created":"2021-03-07","cited_by_count":60,"area":"safety"},{"id":"2304.10619","title":"\"HOT\" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social media","created":"2023-04-20","cited_by_count":45,"area":"safety"},{"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":"2310.14480","title":"Attention-Enhancing Backdoor Attacks Against BERT-based Models","created":"2023-10-23","cited_by_count":32,"area":"safety"},{"id":"2410.14827","title":"Enhancing Prompt Injection Attacks to LLMs via Poisoning Alignment","created":"2024-10-18","cited_by_count":21,"area":"safety"},{"id":"2510.14005","title":"PIShield: Detecting Prompt Injection Attacks via Intrinsic LLM Features","created":"2025-10-15","cited_by_count":20,"area":"safety"},{"id":"2409.14740","title":"ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information","created":"2024-09-23","cited_by_count":17,"area":"safety"},{"id":"2311.05074","title":"GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives","created":"2023-11-09","cited_by_count":16,"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":"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":"2408.15488","title":"Legilimens: Practical and Unified Content Moderation for Large Language Model Services","created":"2024-08-28","cited_by_count":13,"area":"safety"},{"id":"2303.14325","title":"Backdoor Attacks with Input-unique Triggers in NLP","created":"2023-03-25","cited_by_count":12,"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":"2411.05854","title":"Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators","created":"2024-11-06","cited_by_count":8,"area":"safety"},{"id":"2208.07522","title":"Reliable Decision from Multiple Subtasks through Threshold Optimization: Content Moderation in the Wild","created":"2022-08-16","cited_by_count":7,"area":"safety"},{"id":"2506.18576","title":"A Modular Taxonomy for Hate Speech Definitions and Its Impact on Zero-Shot LLM Classification Performance","created":"2025-06-23","cited_by_count":7,"area":"safety"},{"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":"2406.12800","title":"Supporting Human Raters with the Detection of Harmful Content using Large Language Models","created":"2024-06-18","cited_by_count":5,"area":"safety"},{"id":"2402.14977","title":"Mudjacking: Patching Backdoor Vulnerabilities in Foundation Models","created":"2024-02-22","cited_by_count":4,"area":"safety"},{"id":"2404.07879","title":"Analyzing Toxicity in Deep Conversations: A Reddit Case Study","created":"2024-04-11","cited_by_count":3,"area":"safety"},{"id":"2601.17786","title":"Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations","created":"2026-01-25","cited_by_count":3,"area":"safety"},{"id":"2602.04581","title":"Trust The Typical","created":"2026-02-04","cited_by_count":3,"area":"safety"},{"id":"2309.15827","title":"How We Define Harm Impacts Data Annotations: Explaining How Annotators Distinguish Hateful, Offensive, and Toxic Comments","created":"2023-09-12","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":"2411.06138","title":"StopHC: A Harmful Content Detection and Mitigation Architecture for Social Media Platforms","created":"2024-11-09","cited_by_count":2,"area":"safety"},{"id":"2503.05721","title":"What Are They Filtering Out? An Experimental Benchmark of Filtering Strategies for Harm Reduction in Pretraining Datasets","created":"2025-02-17","cited_by_count":2,"area":"safety"},{"id":"2504.05689","title":"Separator Injection Attack: Uncovering Dialogue Biases in Large Language Models Caused by Role Separators","created":"2025-04-08","cited_by_count":2,"area":"safety"},{"id":"2509.15260","title":"Toxicity Red-Teaming: Benchmarking LLM Safety in Singapore's Low-Resource Languages","created":"2025-09-18","cited_by_count":2,"area":"safety"},{"id":"2506.16575","title":"Advancing Harmful Content Detection in Organizational Research: Integrating Large Language Models with Elo Rating System","created":"2025-06-19","cited_by_count":1,"area":"safety"},{"id":"2510.16122","title":"The Hidden Cost of Modeling P(X): Vulnerability to Membership Inference Attacks in Generative Text Classifiers","created":"2025-10-17","cited_by_count":1,"area":"safety"},{"id":"2511.16689","title":"Concept-Based Interpretability for Toxicity Detection","created":"2025-11-15","cited_by_count":1,"area":"safety"},{"id":"2601.09321","title":"SpatialJB: How Text Distribution Art Becomes the \"Jailbreak Key\" for LLM Guardrails","created":"2026-01-14","cited_by_count":1,"area":"safety"},{"id":"2209.11843","title":"Privacy-Preserving Online Content Moderation: A Federated Learning Use Case","created":"2022-09-23","cited_by_count":0,"area":"safety"},{"id":"2311.10236","title":"Latent Feature-based Data Splits to Improve Generalisation Evaluation: A Hate Speech Detection Case Study","created":"2023-11-16","cited_by_count":0,"area":"safety"},{"id":"2403.16151","title":"Ultra Low-Cost Two-Stage Multimodal System for Non-Normative Behavior Detection","created":"2024-03-24","cited_by_count":0,"area":"safety"},{"id":"2502.05291","title":"Can LLMs Rank the Harmfulness of Smaller LLMs? We are Not There Yet","created":"2025-02-07","cited_by_count":0,"area":"safety"},{"id":"2508.04166","title":"STEMTOX: From Collaborative Tags to Fine-Grained Toxic Meme Detection via Entropy-Guided Multi-Task Learning","created":"2025-08-06","cited_by_count":0,"area":"safety"},{"id":"2512.16147","title":"Decoding Fake Narratives in Spreading Hateful Stories: A Dual-Head RoBERTa Model with Multi-Task Learning","created":"2025-12-18","cited_by_count":0,"area":"safety"},{"id":"2603.15692","title":"BadLLM-TG: A Backdoor Defender powered by LLM Trigger Generator","created":"2026-03-16","cited_by_count":0,"area":"safety"},{"id":"2603.18015","title":"Beyond Accuracy: An Explainability-Driven Analysis of Harmful Content Detection","created":"2026-02-24","cited_by_count":0,"area":"safety"},{"id":"2606.05936","title":"Epistemic Injustice in Language Models: An Audit of Pretraining Filters and Guardrails","created":"2026-06-04","cited_by_count":0,"area":"safety"},{"id":"2608.24191","title":"'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection","created":"2026-08-25","cited_by_count":0,"area":"safety"},{"id":"2608.25605","title":"From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigation","created":"2026-08-26","cited_by_count":0,"area":"safety"},{"id":"2608.29589","title":"Not Safe for All: Auditing the Dialect Penalty in Text-to-Image Safety Pipelines","created":"2026-08-30","cited_by_count":0,"area":"safety"},{"id":"2609.13579","title":"How User-AI Mistreatment Occurs and Matters in Conversational Systems?","created":"2026-09-11","cited_by_count":0,"area":"safety"},{"id":"2610.18588","title":"Decomposing and Predicting Annotation Disagreement in Harmful Content Across Languages","created":"2026-10-13","cited_by_count":0,"area":"safety"}],"next_cursor":null}