BarrierSteer: LLM Safety via Learning Barrier Steering
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916037562204160 |
|---|---|
| author | Tran, Thanh Q. Verma, Arun Wong, Kiwan Low, Bryan Kian Hsiang Rus, Daniela Xiao, Wei |
| author_facet | Tran, Thanh Q. Verma, Arun Wong, Kiwan Low, Bryan Kian Hsiang Rus, Daniela Xiao, Wei |
| contents | Despite the strong performance of large language models (LLMs) across diverse tasks, their susceptibility to adversarial attacks and unsafe content generation remains a significant obstacle to deployment, particularly in high-stakes settings. Addressing this challenge requires safety mechanisms that are both practically effective and theoretically grounded. In this paper, we introduce BarrierSteer, a novel inference-time framework that improves response safety by embedding learned nonlinear safety constraints directly into the model's latent representation space. BarrierSteer treats hidden-state safety classifiers as Control Barrier Functions (CBFs), enabling constraint-guided steering of unsafe latent trajectories during generation. By composing multiple safety constraints through efficient constraint merging without modifying the underlying LLM parameters, BarrierSteer preserves model utility. We provide theoretical results showing that applying CBFs in the latent space yields a principled, modular, and computationally efficient approach for steering with respect to learned safety constraints, with guarantees conditional on the learned barriers capturing the intended safety property. Our extensive experimental results across multiple model families and datasets demonstrate that BarrierSteer substantially reduces adversarial attack success rates and unsafe generations, outperforming the existing method. The code is available in our \href{https://github.com/thanhquangtran/BarrierSteer}{GitHub repository}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20102 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BarrierSteer: LLM Safety via Learning Barrier Steering Tran, Thanh Q. Verma, Arun Wong, Kiwan Low, Bryan Kian Hsiang Rus, Daniela Xiao, Wei Machine Learning Artificial Intelligence Despite the strong performance of large language models (LLMs) across diverse tasks, their susceptibility to adversarial attacks and unsafe content generation remains a significant obstacle to deployment, particularly in high-stakes settings. Addressing this challenge requires safety mechanisms that are both practically effective and theoretically grounded. In this paper, we introduce BarrierSteer, a novel inference-time framework that improves response safety by embedding learned nonlinear safety constraints directly into the model's latent representation space. BarrierSteer treats hidden-state safety classifiers as Control Barrier Functions (CBFs), enabling constraint-guided steering of unsafe latent trajectories during generation. By composing multiple safety constraints through efficient constraint merging without modifying the underlying LLM parameters, BarrierSteer preserves model utility. We provide theoretical results showing that applying CBFs in the latent space yields a principled, modular, and computationally efficient approach for steering with respect to learned safety constraints, with guarantees conditional on the learned barriers capturing the intended safety property. Our extensive experimental results across multiple model families and datasets demonstrate that BarrierSteer substantially reduces adversarial attack success rates and unsafe generations, outperforming the existing method. The code is available in our \href{https://github.com/thanhquangtran/BarrierSteer}{GitHub repository}. |
| title | BarrierSteer: LLM Safety via Learning Barrier Steering |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2602.20102 |