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--- nlp:llm_safety [2025/05/30 16:54] – [Related Pages] jmflanig
+++ nlp:llm_safety [2026/03/07 22:18] (current) – [Papers] jmflanig
@@ Line 3: / Line 3: @@
 ===== Overviews =====
   * [[https://arxiv.org/pdf/2305.11391|Huang et al 2023 - A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation]]
-  * [[https://arxiv.org/abs/2308.05374|Liu et al 2023 - Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment]]
+  * [[https://arxiv.org/pdf/2308.05374|Liu et al 2023 - Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment]]
   * [[https://arxiv.org/pdf/2402.09283|Dong et al 2024 - Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey]]
-  * [[https://arxiv.org/pdf/2412.17686|Shi et al 2024 - Large Language Model Safety: A Holistic Survey]]
+  * **[[https://arxiv.org/pdf/2412.17686|Shi et al 2024 - Large Language Model Safety: A Holistic Survey]]** Great survey
+  * [[https://arxiv.org/pdf/2501.17805|2025 - International AI Safety Report]] Safety for AI in general
 ===== Papers =====
@@ Line 12: / Line 13: @@
   * [[https://arxiv.org/pdf/2404.12038|Xu et al 2024 - Uncovering Safety Risks in Open-source LLMs through Concept Activation Vector]]
   * [[https://arxiv.org/pdf/2404.13208|Wallace et al 2024 - The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions]]
+  * [[https://arxiv.org/pdf/2508.06601|O'Brien et al 2025 - Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs]]
 ===== Jailbraking LLMs =====