{"total":21,"rows":[{"id":"2504.11168","title":"Bypassing LLM Guardrails: An Empirical Analysis of Evasion Attacks against Prompt Injection and Jailbreak Detection Systems","created":"2025-04-15","cited_by_count":26,"area":"safety"},{"id":"2503.01742","title":"Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Models","created":"2025-03-03","cited_by_count":11,"area":"safety"},{"id":"2501.18626","title":"The TIP of the Iceberg: Revealing a Hidden Class of Task-in-Prompt Adversarial Attacks on LLMs","created":"2025-01-27","cited_by_count":7,"area":"safety"},{"id":"2511.22047","title":"Evaluating the Robustness of Large Language Model Safety Guardrails Against Adversarial Attacks","created":"2025-11-27","cited_by_count":5,"area":"safety"},{"id":"2411.12946","title":"A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection","created":"2024-11-20","cited_by_count":3,"area":"safety"},{"id":"2505.12287","title":"The Tower of Babel Revisited: Multilingual Jailbreak Prompts on Closed-Source Large Language Models","created":"2025-05-18","cited_by_count":3,"area":"safety"},{"id":"2510.05310","title":"RAG Makes Guardrails Unsafe? Investigating Robustness of Guardrails under RAG-style Contexts","created":"2025-10-06","cited_by_count":3,"area":"safety"},{"id":"2502.11006","title":"Prompt Inject Detection with Generative Explanation as an Investigative Tool","created":"2025-02-16","cited_by_count":2,"area":"safety"},{"id":"2507.20503","title":"Customize Multi-modal RAI Guardrails with Precedent-based predictions","created":"2025-07-28","cited_by_count":2,"area":"safety"},{"id":"2509.16861","title":"AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software","created":"2025-09-21","cited_by_count":2,"area":"safety"},{"id":"2510.12993","title":"Tailored untruths: How personalisation challenges LLM safeguards","created":"2025-10-14","cited_by_count":2,"area":"safety"},{"id":"2604.15579","title":"Don't Make Models Guess Security and Safety: Symbolic Guardrails for Domain-Specific AI Agents","created":"2026-04-16","cited_by_count":2,"area":"safety"},{"id":"2604.18775","title":"An Empirical Study of Multi-Generation Sampling for Jailbreak Detection in Large Language Models","created":"2026-04-20","cited_by_count":2,"area":"safety"},{"id":"2509.16870","title":"DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems","created":"2025-09-21","cited_by_count":1,"area":"safety"},{"id":"2606.29887","title":"SafePyramid: A Hierarchical Benchmark for In-context Policy Guardrailing","created":"2026-06-29","cited_by_count":1,"area":"safety"},{"id":"2406.11880","title":"Knowledge Return Oriented Prompting (KROP)","created":"2024-06-11","cited_by_count":0,"area":"safety"},{"id":"2411.16730","title":"\"Moralized\" Multi-Step Jailbreak Prompts: Black-Box Testing of Guardrails in Large Language Models for Verbal Attacks","created":"2024-11-23","cited_by_count":0,"area":"safety"},{"id":"2511.04215","title":"Black-Box Guardrail Reverse-engineering Attack","created":"2025-11-06","cited_by_count":0,"area":"safety"},{"id":"2512.15081","title":"Quantifying Return on Security Controls in LLM Systems","created":"2025-12-17","cited_by_count":0,"area":"safety"},{"id":"2602.19215","title":"Understanding Empirical Unlearning with Combinatorial Interpretability","created":"2026-02-22","cited_by_count":0,"area":"safety"},{"id":"2608.19579","title":"Enforcing LLM Safety through DMD-based Classification of Prompt-Response Embedding Dynamics","created":"2026-08-20","cited_by_count":0,"area":"safety"}],"next_cursor":null}