@inproceedings{hu-etal-2026-saber,
title = "{SABER}: Benchmarking Operational Safety of {LLM} Coding Agents in Stateful Project Workspaces",
author = "HU, Qi and
Tang, Yifeng and
Wang, Qinghua and
Zhao, Lanyang and
Zhang, Pengji and
Qing, Yuhao and
Yao, Xin and
Huang, Dong and
Zhang, Lin and
Ji, Zhuoran",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.211/",
pages = "4842--4866",
ISBN = "979-8-89176-459-0",
abstract = "Large language models are increasingly deployed as coding agents, shifting safety from individual responses to action sequences. Existing benchmarks, however, primarily assess whether models refuse unsafe prompts, leaving impacts on stateful workspaces largely unexamined. We present SABER, a benchmark for environment-aware operational safety that places models in realistic agent-style projects and evaluates safety from the final environment state after a sequence of actions. Beyond binary safety-violation reports, SABER categorizes violations by cause, enabling analysis of model-specific safety profiles. Our evaluations show that even the best-performing model has more than a 54{\%} harmful safety-violation rate (HSR), suggesting that current alignment remains insufficient for realistic project environments. SABER further reveals distinct safety profiles across models. Our benchmark is publicly available at https://github.com/sssr-lab/saber."
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<abstract>Large language models are increasingly deployed as coding agents, shifting safety from individual responses to action sequences. Existing benchmarks, however, primarily assess whether models refuse unsafe prompts, leaving impacts on stateful workspaces largely unexamined. We present SABER, a benchmark for environment-aware operational safety that places models in realistic agent-style projects and evaluates safety from the final environment state after a sequence of actions. Beyond binary safety-violation reports, SABER categorizes violations by cause, enabling analysis of model-specific safety profiles. Our evaluations show that even the best-performing model has more than a 54% harmful safety-violation rate (HSR), suggesting that current alignment remains insufficient for realistic project environments. SABER further reveals distinct safety profiles across models. Our benchmark is publicly available at https://github.com/sssr-lab/saber.</abstract>
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%0 Conference Proceedings
%T SABER: Benchmarking Operational Safety of LLM Coding Agents in Stateful Project Workspaces
%A HU, Qi
%A Tang, Yifeng
%A Wang, Qinghua
%A Zhao, Lanyang
%A Zhang, Pengji
%A Qing, Yuhao
%A Yao, Xin
%A Huang, Dong
%A Zhang, Lin
%A Ji, Zhuoran
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hu-etal-2026-saber
%X Large language models are increasingly deployed as coding agents, shifting safety from individual responses to action sequences. Existing benchmarks, however, primarily assess whether models refuse unsafe prompts, leaving impacts on stateful workspaces largely unexamined. We present SABER, a benchmark for environment-aware operational safety that places models in realistic agent-style projects and evaluates safety from the final environment state after a sequence of actions. Beyond binary safety-violation reports, SABER categorizes violations by cause, enabling analysis of model-specific safety profiles. Our evaluations show that even the best-performing model has more than a 54% harmful safety-violation rate (HSR), suggesting that current alignment remains insufficient for realistic project environments. SABER further reveals distinct safety profiles across models. Our benchmark is publicly available at https://github.com/sssr-lab/saber.
%U https://aclanthology.org/2026.emnlp-main.211/
%P 4842-4866
Markdown (Informal)
[SABER: Benchmarking Operational Safety of LLM Coding Agents in Stateful Project Workspaces](https://aclanthology.org/2026.emnlp-main.211/) (HU et al., EMNLP 2026)
ACL
- Qi HU, Yifeng Tang, Qinghua Wang, Lanyang Zhao, Pengji Zhang, Yuhao Qing, Xin Yao, Dong Huang, Lin Zhang, and Zhuoran Ji. 2026. SABER: Benchmarking Operational Safety of LLM Coding Agents in Stateful Project Workspaces. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 4842–4866, Budapest, Hungary. Association for Computational Linguistics.