arXiv (All)
open access
Safety Kernel: Mechanistic Alignment for Cross-Lingual Defense in LLMs
The safety capabilities of large language models (LLMs) are highly uneven across different languages, often allowing cross-lingual attacks to bypass safety guardrails. Existing alignment methods predominantly rely on external alignment signals and provide limited insight into underlying neuron-level
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Safety Reconstructed: Generative Modeling via Masked Diffusion Builds Strong Safety Guardrails#1118707
- ReFrame: Evidence-Guided Test-Time Safety Alignment in Multimodal Large Language Models#662273
- ReFrame: Evidence-Guided Test-Time Safety Alignment in Multimodal Large Language Models#661223
- ReFrame: Evidence-Guided Test-Time Safety Alignment in Multimodal Large Language Models#480106
- BabelSteering: Multilingual Safety Alignment via English Steering Vectors#625155
Record · ID 1389560
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive