{"total":36,"rows":[{"id":"2406.04313","title":"Improving Alignment and Robustness with Circuit Breakers","created":"2024-06-06","cited_by_count":322,"area":"safety"},{"id":"2410.09102","title":"Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy","created":"2024-10-09","cited_by_count":67,"area":"safety"},{"id":"2601.04603","title":"Constitutional Classifiers++: Efficient Production-Grade Defenses against Universal Jailbreaks","created":"2026-01-08","cited_by_count":41,"area":"safety"},{"id":"2504.07113","title":"How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities","created":"2025-03-20","cited_by_count":11,"area":"safety"},{"id":"2505.16723","title":"LLM Fingerprinting via Semantically Conditioned Watermarks","created":"2025-05-22","cited_by_count":9,"area":"safety"},{"id":"2607.07184","title":"Predicting LLM Safety Before Release by Simulating Deployment","created":"2026-07-08","cited_by_count":8,"area":"safety"},{"id":"2508.00161","title":"Watch the Weights: Unsupervised monitoring and control of fine-tuned LLMs","created":"2025-07-31","cited_by_count":7,"area":"safety"},{"id":"2410.01088","title":"Exploring Empty Spaces: Human-in-the-Loop Data Augmentation","created":"2024-10-01","cited_by_count":6,"area":"safety"},{"id":"2504.19440","title":"JailbreaksOverTime: Detecting Jailbreak Attacks Under Distribution Shift","created":"2025-04-28","cited_by_count":5,"area":"safety"},{"id":"2602.15671","title":"Revisiting Backdoor Threat in Federated Instruction Tuning from a Signal Aggregation Perspective","created":"2026-02-17","cited_by_count":5,"area":"safety"},{"id":"2604.14865","title":"Segment-Level Coherence for Robust Harmful Intent Probing in LLMs","created":"2026-04-16","cited_by_count":3,"area":"safety"},{"id":"2603.02588","title":"ExpGuard: LLM Content Moderation in Specialized Domains","created":"2026-03-03","cited_by_count":2,"area":"safety"},{"id":"2606.19535","title":"FloatDoor: Platform-Triggered Backdoors in LLMs","created":"2026-06-17","cited_by_count":2,"area":"safety"},{"id":"2508.10010","title":"An Audit and Analysis of LLM-Assisted Health Misinformation Jailbreaks Against LLMs","created":"2025-08-06","cited_by_count":1,"area":"safety"},{"id":"2602.12318","title":"Abstractive Red-Teaming of Language Model Character","created":"2026-02-12","cited_by_count":1,"area":"safety"},{"id":"2605.06846","title":"Narrow Secret Loyalty Dodges Black-Box Audits","created":"2026-05-07","cited_by_count":1,"area":"safety"},{"id":"2606.09890","title":"PreAct-Bench: Benchmarking Predictive Monitoring in LLMs","created":"2026-06-03","cited_by_count":1,"area":"safety"},{"id":"2608.14577","title":"HarmProfile: Characterizing Harmful Distributions in Frontier LLMs","created":"2026-06-11","cited_by_count":1,"area":"safety"},{"id":"2505.18672","title":"Concept Concentration for Faithful Representation Intervention","created":"2025-05-24","cited_by_count":0,"area":"safety"},{"id":"2507.08014","title":"Mass-Scale Analysis of In-the-Wild Conversations Reveals Complexity Bounds on LLM Jailbreaking","created":"2025-07-06","cited_by_count":0,"area":"safety"},{"id":"2602.19020","title":"Learning to Detect Language Model Training Data via Active Reconstruction","created":"2026-02-22","cited_by_count":0,"area":"safety"},{"id":"2605.06213","title":"Beyond Fixed Benchmarks and Worst-Case Attacks: Dynamic Boundary Evaluation for Language Models","created":"2026-05-07","cited_by_count":0,"area":"safety"},{"id":"2605.07032","title":"A Systematic Investigation of RL-Jailbreaking in LLMs","created":"2026-05-07","cited_by_count":0,"area":"safety"},{"id":"2606.00654","title":"The Invitation Trap: Proactive Availability Backdoor in LLMs via Conversational Induction","created":"2026-05-30","cited_by_count":0,"area":"safety"},{"id":"2606.16242","title":"Rapid Poison: Practical Poisoning Attacks Against the Rapid Response Framework","created":"2026-06-15","cited_by_count":0,"area":"safety"},{"id":"2606.21082","title":"Scalable Hierarchical Attention Transformers for Multi-Turn Jailbreak Detection in Long Conversations","created":"2026-06-19","cited_by_count":0,"area":"safety"},{"id":"2606.28525","title":"A Gravitational Interpretation of Safety Reversion under Fine-Tuning","created":"2026-06-26","cited_by_count":0,"area":"safety"},{"id":"2607.19361","title":"Stateful Guardrails for Multi-Turn LLM Systems: A Conversational Risk Accumulation Framework","created":"2026-06-03","cited_by_count":0,"area":"safety"},{"id":"2608.27580","title":"LongGuard: Mechanistic Analysis and Training-Free Mitigation of Long-Context Failure in Safety Guardrails","created":"2026-08-27","cited_by_count":0,"area":"safety"},{"id":"2609.00351","title":"Detecting Hidden Behaviors in LLMs via Activation-matched Finetuning","created":"2026-05-29","cited_by_count":0,"area":"safety"},{"id":"2609.05797","title":"Safety Monitors Mostly Catch What the Model Already Refuses","created":"2026-09-05","cited_by_count":0,"area":"safety"},{"id":"2609.13534","title":"Harmfulness Propagation Dynamics: Layer-wise Trajectories of Adversarial Intent in Large Language Models","created":"2026-09-11","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":"2610.03857","title":"Beware EviLLM: Enabling Vulnerability Injection via Large Language Models","created":"2026-10-02","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"},{"id":"2610.10203","title":"How to train your model organism","created":"2026-10-07","cited_by_count":0,"area":"safety"}],"next_cursor":null}