arXiv AI June 11, 2026 By Yitong Zhang, Shiteng Lu, Jia Li

Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code

arXiv:2606. 11817v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 11

When Grammar Guides the Attack: Uncovering Control-Plane Vulnerabilities in LLMs with Structured Output

arXiv:2503. 24191v4 Announce Type: replace-cross Abstract: Content Warning: This paper may contain unsafe or harmful content generated by LLMs that may be offensive to readers.

By Shuoming Zhang, Jiacheng Zhao, Hanyuan Dong, Ruiyuan Xu, Zhicheng Li, Yangyu Zhang, Shuaijiang Li, Yuan Wen, Chunwei Xia, Zheng Wang, Xiaobing Feng, Huimin Cui

llms multimodal benchmarks safety

arXiv AI

Jul 2

Beyond the Prompt: Jailbreaking Function-Calling LLMs via Simulated Moderation Traces

arXiv:2607. 00481v1 Announce Type: cross Abstract: Jailbreak attacks remain a critical threat to the safe deployment of large language models (LLMs).

By Junlong Liu, Haobo Wang, Weiqi Luo, Xiaojun Jia

llms benchmarks safety

arXiv Machine Learning

Jun 30

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks

arXiv:2606. 28962v1 Announce Type: cross Abstract: Model quantization is essential for the efficient deployment of Large Language Models (LLMs), but introduces a critical vulnerability: Quantization-Conditioned Backdoor (QCB) attacks.

By Aoying Zheng, Anqi Du, Zizhuang Deng, Yuxuan Chen

llms efficiency safety

arXiv AI

Jul 7

Context Misleads LLMs: The Role of Context Filtering in Maintaining Safe Alignment of LLMs

arXiv:2508. 10031v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) have shown significant advancements in performance, various jailbreak attacks have posed growing safety and ethical risks.

By Jinhwa Kim, Ian G. Harris

llms fine-tuning benchmarks safety

arXiv AI

Jul 3

Safety Targeted Embedding Exploit via Refinement

arXiv:2607. 01859v1 Announce Type: new Abstract: Safety training for large language models (LLMs) is conducted predominantly in English, leaving uncertain how well safety mechanisms generalize to low-resource languages and mixed-language code-switching.

By Joshua Adrian Cahyono

llms rag safety

arXiv Machine Learning

Jun 30

Sparse Autoencoders are Capable LLM Jailbreak Mitigators

arXiv:2602. 12418v2 Announce Type: replace-cross Abstract: Jailbreak attacks remain a persistent threat to large language model safety.

By Yannick Assogba, Jacopo Cortellazzi, Javier Abad, Pau Rodriguez, Xavier Suau, Arno Blaas

llms diffusion safety

Related stories

When Grammar Guides the Attack: Uncovering Control-Plane Vulnerabilities in LLMs with Structured Output

Beyond the Prompt: Jailbreaking Function-Calling LLMs via Simulated Moderation Traces

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks

Context Misleads LLMs: The Role of Context Filtering in Maintaining Safe Alignment of LLMs

Safety Targeted Embedding Exploit via Refinement

Sparse Autoencoders are Capable LLM Jailbreak Mitigators