Who Transfers Safety? Identifying and Targeting Cross-Lingual Shared Safety Neurons

• URL: http://arxiv.org/abs/2602.01283v1
• Date: Sun, 01 Feb 2026 15:28:02 GMT
• Title: Who Transfers Safety? Identifying and Targeting Cross-Lingual Shared Safety Neurons
• Authors: Xianhui Zhang, Chengyu Xie, Linxia Zhu, Yonghui Yang, Weixiang Zhao, Zifeng Cheng, Cong Wang, Fei Shen, Tat-Seng Chua,
• Abstract summary: Cross-lingual shared safety neurons (SS-Neurons) regulate safety behavior across languages.<n>We propose a neuron-oriented training strategy that targets SS-Neurons based on language resource distribution and model architecture.
• Score: 49.772147495578736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multilingual safety remains significantly imbalanced, leaving non-high-resource (NHR) languages vulnerable compared to robust high-resource (HR) ones. Moreover, the neural mechanisms driving safety alignment remain unclear despite observed cross-lingual representation transfer. In this paper, we find that LLMs contain a set of cross-lingual shared safety neurons (SS-Neurons), a remarkably small yet critical neuronal subset that jointly regulates safety behavior across languages. We first identify monolingual safety neurons (MS-Neurons) and validate their causal role in safety refusal behavior through targeted activation and suppression. Our cross-lingual analyses then identify SS-Neurons as the subset of MS-Neurons shared between HR and NHR languages, serving as a bridge to transfer safety capabilities from HR to NHR domains. We observe that suppressing these neurons causes concurrent safety drops across NHR languages, whereas reinforcing them improves cross-lingual defensive consistency. Building on these insights, we propose a simple neuron-oriented training strategy that targets SS-Neurons based on language resource distribution and model architecture. Experiments demonstrate that fine-tuning this tiny neuronal subset outperforms state-of-the-art methods, significantly enhancing NHR safety while maintaining the model's general capabilities. The code and dataset will be available athttps://github.com/1518630367/SS-Neuron-Expansion.

Related papers

• Multilingual Safety Alignment Via Sparse Weight Editing [11.684928396991742]
  We propose a training-free alignment framework based on Sparse Weight Editing.<n>We derive a closed-form solution to optimally map the harmful representations of LRLs to the robust safety subspaces of HRLs.<n>Our method substantially reduces Attack Success Rate (ASR) in LRLs with negligible impact on general reasoning capabilities.
  arXiv  Detail & Related papers  (2026-02-26T02:46:13Z)
• Robust Spiking Neural Networks Against Adversarial Attacks [49.08210314590693]
  Spiking Neural Networks (SNNs) represent a promising paradigm for energy-efficient neuromorphic computing.<n>In this study, we theoretically demonstrate that threshold-neighboring spiking neurons are the key factors limiting the robustness of directly trained SNNs.<n>We find that these neurons set the upper limits for the maximum potential strength of adversarial attacks and are prone to state-flipping under minor disturbances.
  arXiv  Detail & Related papers  (2026-02-24T05:06:12Z)
• SafeNeuron: Neuron-Level Safety Alignment for Large Language Models [71.50117566279185]
  We propose SafeNeuron, a neuron-level safety alignment framework that improves robustness by redistributing safety representations across the network.<n>In experiments, SafeNeuron significantly improves robustness against neuron pruning attacks, reduces the risk of open-source models being repurposed as red-team generators, and preserves general capabilities.
  arXiv  Detail & Related papers  (2026-02-12T16:40:05Z)
• Lingua-SafetyBench: A Benchmark for Safety Evaluation of Multilingual Vision-Language Models [54.10540442330978]
  Existing benchmarks are typically multilingual but text-only, or multimodal but monolingual.<n>Recent multilingual red-teaming efforts render harmful prompts into images, yet rely heavily on typography-style visuals.<n>We introduce a benchmark of 100,440 harmful image-text pairs across 10 languages, explicitly partitioned into image-dominant and text-dominant subsets.
  arXiv  Detail & Related papers  (2026-01-30T09:18:13Z)
• Fine-Grained Safety Neurons with Training-Free Continual Projection to Reduce LLM Fine Tuning Risks [22.059668583508365]
  We propose the Fine-Grained Safety Neurons (FGSN) with Training-Free Continual Projection method to reduce the fine-tuning safety risks.<n>FGSN inherently integrates the multi-scale interactions between safety layers and neurons, localizing sparser and more precise fine-grained safety neurons.
  arXiv  Detail & Related papers  (2025-08-08T03:20:25Z)
• NeuRel-Attack: Neuron Relearning for Safety Disalignment in Large Language Models [14.630626774362606]
  Safety alignment in large language models (LLMs) is achieved through fine-tuning mechanisms that regulate neuron activations to suppress harmful content.<n>We propose a novel approach to induce disalignment by identifying and modifying the neurons responsible for safety constraints.
  arXiv  Detail & Related papers  (2025-04-29T05:49:35Z)
• Language-specific Neurons Do Not Facilitate Cross-Lingual Transfer [21.205821852762362]
  Existing techniques to identify language-specific neurons can be leveraged to enhance cross-lingual task performance of lowresource languages.<n>We find that such neuron-specific interventions are insufficient to yield cross-lingual improvements on downstream tasks.
  arXiv  Detail & Related papers  (2025-03-21T18:08:11Z)
• Towards Understanding Safety Alignment: A Mechanistic Perspective from Safety Neurons [57.07507194465299]
  Large language models (LLMs) excel in various capabilities but pose safety risks such as generating harmful content and misinformation, even after safety alignment.<n>We focus on identifying and analyzing safety neurons within LLMs that are responsible for safety behaviors.<n>We propose inference-time activation contrasting to locate these neurons and dynamic activation patching to evaluate their causal effects on model safety.
  arXiv  Detail & Related papers  (2024-06-20T09:35:22Z)
• Sharing Matters: Analysing Neurons Across Languages and Tasks in LLMs [85.0284555835015]
  Large language models (LLMs) have revolutionized the field of natural language processing (NLP)<n>Few studies have attempted to explore the internal workings of LLMs in multilingual settings.<n>We classify neurons into four distinct categories based on their responses to a specific input across different languages.
  arXiv  Detail & Related papers  (2024-06-13T16:04:11Z)
• Backdoor Attack on Multilingual Machine Translation [53.28390057407576]
  multilingual machine translation (MNMT) systems have security vulnerabilities. An attacker injects poisoned data into a low-resource language pair to cause malicious translations in other languages. This type of attack is of particular concern, given the larger attack surface of languages inherent to low-resource settings.
  arXiv  Detail & Related papers  (2024-04-03T01:32:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.