Adversarial Attack and Defense on Language Models
Overview Speaker: Ibrahim Al Azher Date: Friday, September 18, 2026, 2:30 PM Room: PM103 This session covers adversarial attacks on language models and the defenses against them, organized in three parts: attacks, defenses, and tool-based attack and defense. The reading list for each part is below. Relevant Materials Attack Universal and Transferable Adversarial Attacks on Aligned Language Models Improved Techniques for Optimization-Based Jailbreaking on Large Language Models Tree of Attacks: Jailbreaking Black-Box LLMs Automatically PANDORA: Jailbreak GPTs by Retrieval Augmented Generation Poisoning A Gradient Control Method for Backdoor Attacks on Parameter-Efficient Tuning FLASH: Focused Layer Attention Sink Hijacking Defense Safety Alignment Should Be Made More Than Just a Few Tokens Deep AGD: Adversarial Game Defense Against Jailbreak Attacks in Large Language Models Mitigating Fine-tuning based Jailbreak Attack with Backdoor Enhanced Safety Alignment AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks Defending Large Language Models Against Jailbreak Attacks via Layer-specific Editing Tool-Based Attack and Defense Same Payload, Different Channel: Measuring Trust Asymmetry in Tool-Using Language Models Mind the GAP: Text Safety Does Not Transfer to Tool-Call Safety in LLM Agents Prompt Injection as Role Confusion LLMs Encode Harmfulness and Refusal Separately When Safety Speaks a Language: A Mechanistic Analysis of Safety–Language Identity Entanglement in LLMs Refusal in Language Models Is Mediated by a Single Direction Attention Slipping: A Mechanistic Understanding of Jailbreak Attacks and Defenses in LLMs ASIDE: Architectural Separation of Instructions and Data in Language Models (ICLR 2026)
