arXiv AI September 7, 2026 By Yonghong Deng, Zhen Yang, Ping Jian, Xinyue Zhang, Zhongbin Guo, Chengzhi Li, Junxi Yin

The Struggle Between Continuation and Refusal: A Mechanistic Analysis of the Continuation-Triggered Jailbreak in LLMs

The paper investigates a continuation-triggered jailbreak in large language models, showing that moving an instruction suffix can markedly boost jailbreak success. By performing mechanistic interpretability at the attention‑head level, the authors reveal that the jailbreak arises from a competition between the model’s natural continuation drive and safety defenses learned during alignment. They introduce Head Competition Steering (HCS), an inference‑time technique that exploits this competition to suppress harmful outputs and distill the approach into a student model for efficient safety improvements.

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arXiv AI

Oct 7

Beyond Refusal Patterns: Safe-Role Internalization for Robust and Generalizable LLM Safety Alignment

The paper introduces SSRFT (Supervised Safe-Role Fine‑Tuning), a new framework that reframes safety alignment for large language models as internalizing a predefined safe role rather than relying on explicit refusal patterns. SSRFT builds a Safe‑Role Question‑Answer dataset from psychometric questions, limited jailbreak prompts, and a safe‑role description, then synthesizes and validates role‑consistent responses to expose models to diverse safety scenarios. Experiments on multiple base and instruct models demonstrate that SSRFT yields more robust and generalizable safety alignment, better resilience to prefilling attacks, improved generalization to unseen jailbreak domains, and reduced over‑refusal on benign queries while preserving general capabilities.

By Jinghao Pang, Jitai Hao, Qiang Huang, Zhaochun Ren, Jun Yu

llms reinforcement-learning fine-tuning safety

arXiv Computation and Language

Sep 22

BreakFun: Jailbreaking LLMs via Object Instantiation under Simulated Code Execution

arXiv:2510.17904v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely used because they process structures, syntax and code well, but this same ability also makes them par...

By Amirkia Rafiei Oskooei, Mehmet S. Aktas

llms benchmarks safety

arXiv AI

Jul 2

HARC: Coupling Harmfulness and Refusal Directions for Robust Safety Alignment

arXiv:2607. 00572v1 Announce Type: new Abstract: Understanding how aligned LLMs internally represent safety is critical for diagnosing alignment vulnerabilities, as it explains why jailbreaks succeed and informs the design of robust alignment strategies.

By Shei Pern Chua, Fangzhao Wu

llms fine-tuning safety

arXiv AI

Aug 10

Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Models

arXiv:2605. 00123v3 Announce Type: replace Abstract: Safety trained large language models (LLMs) can often be induced to answer harmful requests through jailbreak prompts.

By Shubham Kumar, Narendra Ahuja

llms benchmarks safety

arXiv AI

Jun 29

Robust Harmful Features Under Jailbreak Attacks: Mechanistic Evidence from Attention Head Specialization in Large Language Models

arXiv:2606. 28153v1 Announce Type: cross Abstract: Jailbreak attacks bypass LLM safety alignment, yet their mechanisms remain poorly understood.

By Yanchen Yin, Dongqi Han, Linghui Li

llms benchmarks safety

arXiv AI

Jul 1

The Geometry of Refusal: Linear Instability in Safety-Aligned LLMs

arXiv:2606. 22686v2 Announce Type: replace-cross Abstract: Modern Large Language Models (LLMs) rely on extensive safety alignment, yet the mechanistic basis of refusal remains opaque.

By Shivam Ratnakar, Kartikeya Vats

llms multimodal safety

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Related stories

Beyond Refusal Patterns: Safe-Role Internalization for Robust and Generalizable LLM Safety Alignment

BreakFun: Jailbreaking LLMs via Object Instantiation under Simulated Code Execution

HARC: Coupling Harmfulness and Refusal Directions for Robust Safety Alignment

Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Models

Robust Harmful Features Under Jailbreak Attacks: Mechanistic Evidence from Attention Head Specialization in Large Language Models

The Geometry of Refusal: Linear Instability in Safety-Aligned LLMs