{"total":63,"rows":[{"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":"2403.13355","title":"BadEdit: Backdooring large language models by model editing","created":"2024-03-20","cited_by_count":96,"area":"safety"},{"id":"2305.01219","title":"Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models","created":"2023-05-02","cited_by_count":94,"area":"safety"},{"id":"2110.02467","title":"BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models","created":"2021-10-06","cited_by_count":85,"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":"2111.07970","title":"Triggerless Backdoor Attack for NLP Tasks with Clean Labels","created":"2021-11-15","cited_by_count":66,"area":"safety"},{"id":"2211.14719","title":"BadPrompt: Backdoor Attacks on Continuous Prompts","created":"2022-11-27","cited_by_count":63,"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":"2210.09545","title":"Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models","created":"2022-10-18","cited_by_count":57,"area":"safety"},{"id":"2401.05949","title":"Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning","created":"2024-01-11","cited_by_count":53,"area":"safety"},{"id":"2210.07543","title":"Watermarking Pre-trained Language Models with Backdooring","created":"2022-10-14","cited_by_count":50,"area":"safety"},{"id":"2407.14937","title":"Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)","created":"2024-07-20","cited_by_count":46,"area":"safety"},{"id":"2308.14367","title":"A Comprehensive Overview of Backdoor Attacks in Large Language Models within Communication Networks","created":"2023-08-28","cited_by_count":42,"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":"2406.06852","title":"A Survey of Recent Backdoor Attacks and Defenses in Large Language Models","created":"2024-06-10","cited_by_count":36,"area":"safety"},{"id":"2309.13256","title":"Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks","created":"2023-09-23","cited_by_count":33,"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":"2302.04116","title":"Training-free Lexical Backdoor Attacks on Language Models","created":"2023-02-08","cited_by_count":31,"area":"safety"},{"id":"2304.14475","title":"ChatGPT as an Attack Tool: Stealthy Textual Backdoor Attack via Blackbox Generative Model Trigger","created":"2023-04-27","cited_by_count":31,"area":"safety"},{"id":"2406.05948","title":"Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models","created":"2024-06-10","cited_by_count":28,"area":"safety"},{"id":"2305.13257","title":"Watermarking Text Data on Large Language Models for Dataset Copyright","created":"2023-05-22","cited_by_count":27,"area":"safety"},{"id":"2305.02394","title":"Defending against Insertion-based Textual Backdoor Attacks via Attribution","created":"2023-05-03","cited_by_count":25,"area":"safety"},{"id":"2310.11237","title":"Watermarking LLMs with Weight Quantization","created":"2023-10-17","cited_by_count":22,"area":"safety"},{"id":"2505.01976","title":"A Survey on Privacy Risks and Protection in Large Language Models","created":"2025-05-04","cited_by_count":21,"area":"safety"},{"id":"2210.07907","title":"Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks","created":"2022-10-14","cited_by_count":19,"area":"safety"},{"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":"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":"2407.11282","title":"Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models","created":"2024-07-15","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":"2503.04036","title":"Robust Data Watermarking in Language Models by Injecting Fictitious Knowledge","created":"2025-03-06","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":"2308.13904","title":"LMSanitator: Defending Prompt-Tuning Against Task-Agnostic Backdoors","created":"2023-08-26","cited_by_count":9,"area":"safety"},{"id":"2305.04547","title":"Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias","created":"2023-05-08","cited_by_count":8,"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":"2408.10722","title":"MEGen: Generative Backdoor into Large Language Models via Model Editing","created":"2024-08-20","cited_by_count":8,"area":"safety"},{"id":"2512.14741","title":"Persistent Backdoor Attacks under Continual Fine-Tuning of LLMs","created":"2025-12-12","cited_by_count":7,"area":"safety"},{"id":"2312.15867","title":"Punctuation Matters! Stealthy Backdoor Attack for Language Models","created":"2023-12-26","cited_by_count":5,"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":"2405.16783","title":"TrojFM: Resource-efficient Backdoor Attacks against Very Large Foundation Models","created":"2024-05-27","cited_by_count":5,"area":"safety"},{"id":"2411.18280","title":"Neutralizing Backdoors through Information Conflicts for Large Language Models","created":"2024-11-27","cited_by_count":5,"area":"safety"},{"id":"2508.20032","title":"Pruning Strategies for Backdoor Defense in LLMs","created":"2025-08-27","cited_by_count":5,"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":"2604.06840","title":"MirageBackdoor: A Stealthy Attack that Induces Think-Well-Answer-Wrong Reasoning","created":"2026-04-08","cited_by_count":4,"area":"safety"},{"id":"2311.17429","title":"TARGET: Template-Transferable Backdoor Attack Against Prompt-based NLP Models via GPT4","created":"2023-11-29","cited_by_count":3,"area":"safety"},{"id":"2401.10375","title":"Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities","created":"2024-01-18","cited_by_count":3,"area":"safety"},{"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":"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":"2406.04478","title":"PromptFix: Few-shot Backdoor Removal via Adversarial Prompt Tuning","created":"2024-06-06","cited_by_count":2,"area":"safety"},{"id":"2409.17946","title":"Breaking PEFT Limitations: Leveraging Weak-to-Strong Knowledge Transfer for Backdoor Attacks in LLMs","created":"2024-09-26","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":"2410.04009","title":"ASPIRER: Bypassing System Prompts With Permutation-based Backdoors in LLMs","created":"2024-10-05","cited_by_count":1,"area":"safety"},{"id":"2503.17578","title":"Large Language Models Can Verbatim Reproduce Long Malicious Sequences","created":"2025-03-21","cited_by_count":1,"area":"safety"},{"id":"2508.21004","title":"Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution","created":"2025-08-28","cited_by_count":1,"area":"safety"},{"id":"2605.15172","title":"MetaBackdoor: Exploiting Positional Encoding as a Backdoor Attack Surface in LLMs","created":"2026-05-14","cited_by_count":1,"area":"safety"},{"id":"2605.25073","title":"Security in the Fine-Tuning Lifecycle of Large Language Models: Threats, Defenses,Evaluation, and Future Directions","created":"2026-05-24","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":"2409.10570","title":"Protecting Copyright of Medical Pre-trained Language Models: Training-Free Backdoor Model Watermarking","created":"2024-09-14","cited_by_count":0,"area":"safety"},{"id":"2502.16901","title":"Char-mander Use mBackdoor! A Study of Cross-lingual Backdoor Attacks in Multilingual LLMs","created":"2025-02-24","cited_by_count":0,"area":"safety"},{"id":"2508.10243","title":"Pruning and Malicious Injection: A Retraining-Free Backdoor Attack on Transformer Models","created":"2025-08-14","cited_by_count":0,"area":"safety"},{"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":"2602.06887","title":"Plato's Form: Toward Backdoor Defense-as-a-Service for LLMs with Prototype Representations","created":"2026-02-06","cited_by_count":0,"area":"safety"},{"id":"2610.04169","title":"Language Model Fingerprinting Requires Rethinking Watermark Teachers","created":"2026-10-03","cited_by_count":0,"area":"safety"}],"next_cursor":null}