@inproceedings{zheng-etal-2026-stepguard,
title = "{S}tep{G}uard: Learning Step-Level Guardrails with Scalable Supervision and Safety{--}Utility Balancing",
author = "Zheng, Zhijie and
Li, Yu and
Qian, Chen and
Fu, Yuqian and
Fu, Yanwei and
Sheng, Lu and
Shao, Jing and
Liu, Dongrui",
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.1073/",
pages = "24644--24667",
ISBN = "979-8-89176-459-0",
abstract = "LLM-based agents can interact with external environments through tool invocation, but this capability also introduces security risks such as file modification, information leakage, and unauthorized actions. Existing guardrails often evaluate completed trajectories, leaving pre-execution monitoring of step-level actions underexplored. We propose StepGuard, a step-level guard model that can audit completed agent trajectories and check tool actions before they are executed. To train StepGuard, we introduce StepGen, an automatic data engine that generates safe and unsafe trajectories with the same context but different actions at the risky step. To further reduce over-defense and under-defense, we propose Balance-GRPO, which dynamically balances learning between safe and unsafe actions based on their observed accuracy. Experiments show that StepGuard achieves the highest average accuracy among open-weight guard models, with performance comparable to GPT-5.4. When used to guard agents on AgentDojo and AgentDyn, StepGuard reduces mean ASR by 77.3{\%} relative to the no-guard setting, while mean utility drops by only 2.8 points."
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<abstract>LLM-based agents can interact with external environments through tool invocation, but this capability also introduces security risks such as file modification, information leakage, and unauthorized actions. Existing guardrails often evaluate completed trajectories, leaving pre-execution monitoring of step-level actions underexplored. We propose StepGuard, a step-level guard model that can audit completed agent trajectories and check tool actions before they are executed. To train StepGuard, we introduce StepGen, an automatic data engine that generates safe and unsafe trajectories with the same context but different actions at the risky step. To further reduce over-defense and under-defense, we propose Balance-GRPO, which dynamically balances learning between safe and unsafe actions based on their observed accuracy. Experiments show that StepGuard achieves the highest average accuracy among open-weight guard models, with performance comparable to GPT-5.4. When used to guard agents on AgentDojo and AgentDyn, StepGuard reduces mean ASR by 77.3% relative to the no-guard setting, while mean utility drops by only 2.8 points.</abstract>
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%0 Conference Proceedings
%T StepGuard: Learning Step-Level Guardrails with Scalable Supervision and Safety–Utility Balancing
%A Zheng, Zhijie
%A Li, Yu
%A Qian, Chen
%A Fu, Yuqian
%A Fu, Yanwei
%A Sheng, Lu
%A Shao, Jing
%A Liu, Dongrui
%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 zheng-etal-2026-stepguard
%X LLM-based agents can interact with external environments through tool invocation, but this capability also introduces security risks such as file modification, information leakage, and unauthorized actions. Existing guardrails often evaluate completed trajectories, leaving pre-execution monitoring of step-level actions underexplored. We propose StepGuard, a step-level guard model that can audit completed agent trajectories and check tool actions before they are executed. To train StepGuard, we introduce StepGen, an automatic data engine that generates safe and unsafe trajectories with the same context but different actions at the risky step. To further reduce over-defense and under-defense, we propose Balance-GRPO, which dynamically balances learning between safe and unsafe actions based on their observed accuracy. Experiments show that StepGuard achieves the highest average accuracy among open-weight guard models, with performance comparable to GPT-5.4. When used to guard agents on AgentDojo and AgentDyn, StepGuard reduces mean ASR by 77.3% relative to the no-guard setting, while mean utility drops by only 2.8 points.
%U https://aclanthology.org/2026.emnlp-main.1073/
%P 24644-24667
Markdown (Informal)
[StepGuard: Learning Step-Level Guardrails with Scalable Supervision and Safety–Utility Balancing](https://aclanthology.org/2026.emnlp-main.1073/) (Zheng et al., EMNLP 2026)
ACL
- Zhijie Zheng, Yu Li, Chen Qian, Yuqian Fu, Yanwei Fu, Lu Sheng, Jing Shao, and Dongrui Liu. 2026. StepGuard: Learning Step-Level Guardrails with Scalable Supervision and Safety–Utility Balancing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 24644–24667, Budapest, Hungary. Association for Computational Linguistics.