{"total":19,"rows":[{"id":"2103.14991","title":"Graph Unlearning","created":"2021-03-27","cited_by_count":138,"area":"safety"},{"id":"2108.00352","title":"BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning","created":"2021-08-01","cited_by_count":123,"area":"safety"},{"id":"2205.01992","title":"Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning","created":"2022-05-04","cited_by_count":94,"area":"safety"},{"id":"2105.00164","title":"Hidden Backdoors in Human-Centric Language Models","created":"2021-05-01","cited_by_count":90,"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":"2210.15127","title":"Rethinking the Reverse-engineering of Trojan Triggers","created":"2022-10-27","cited_by_count":39,"area":"safety"},{"id":"2405.17984","title":"Cross-Context Backdoor Attacks against Graph Prompt Learning","created":"2024-05-28","cited_by_count":14,"area":"safety"},{"id":"2302.06801","title":"Backdoor Learning for NLP: Recent Advances, Challenges, and Future Research Directions","created":"2023-02-14","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":"2404.08631","title":"FCert: Certifiably Robust Few-Shot Classification in the Era of Foundation Models","created":"2024-04-12","cited_by_count":3,"area":"safety"},{"id":"2407.13068","title":"Krait: A Backdoor Attack Against Graph Prompt Tuning","created":"2024-07-18","cited_by_count":2,"area":"safety"},{"id":"2308.15673","title":"MDTD: A Multi Domain Trojan Detector for Deep Neural Networks","created":"2023-08-30","cited_by_count":1,"area":"safety"},{"id":"2510.14470","title":"Stealthy Dual-Trigger Backdoors: Attacking Prompt Tuning in LM-Empowered Graph Foundation Models","created":"2025-10-16","cited_by_count":1,"area":"safety"},{"id":"2404.00461","title":"Shortcuts Arising from Contrast: Effective and Covert Clean-Label Attacks in Prompt-Based Learning","created":"2024-03-30","cited_by_count":0,"area":"safety"},{"id":"2512.24665","title":"HeteroHBA: A Generative Structure-Manipulating Backdoor Attack on Heterogeneous Graphs","created":"2025-12-31","cited_by_count":0,"area":"safety"},{"id":"2603.20339","title":"Graph-Aware Stealthy Poison-Text Backdoors for Text-Attributed Graphs","created":"2026-03-20","cited_by_count":0,"area":"safety"},{"id":"2605.11996","title":"BadSKP: Backdoor Attacks on Knowledge Graph-Enhanced LLMs with Soft Prompts","created":"2026-05-12","cited_by_count":0,"area":"safety"},{"id":"2607.19674","title":"FedLSG: LLM-Enhanced Semantic Calibration for Federated Graph Backdoor Defense","created":"2026-07-22","cited_by_count":0,"area":"safety"},{"id":"2608.20991","title":"Trojaning the Alignment: Stealthy Backdoor Attacks against Graph Foundation Models","created":"2026-08-21","cited_by_count":0,"area":"safety"}],"next_cursor":null}