Building a Foundational Guardrail for General Agentic Systems via
  Synthetic Data
While LLM agents can plan multi-step tasks, intervening at the planning
stage-before any action is executed-is often the safest way to prevent harm,
since certain risks can lead to severe consequences once carried out. However,
existing guardrails mostly operate post-execution, which is difficult to scale
and leaves little room for controllable supervision at the plan level. To
address this challenge, we highlight three critical gaps in current research:
data gap, model gap, and evaluation gap. To close the data gap, we introduce
AuraGen, a controllable engine that (i) synthesizes benign trajectories, (ii)
injects category-labeled risks with calibrated difficulty, and (iii) filters
outputs via an automated reward model, producing large and reliable corpora for
pre-execution safety. To close the guardian model gap, we propose a
foundational guardrail Safiron, combining a cross-planner adapter with a
compact guardian model. The adapter unifies different input formats, while
Safiron flags risky cases, assigns risk types, and generates rationales;
trained in two stages with a broadly explored data recipe, Safiron achieves
robust transfer across settings. To close the evaluation gap, we release
Pre-Exec Bench, a realistic benchmark covering diverse tools and branching
trajectories, which measures detection, fine-grained categorization,
explanation, and cross-planner generalization in human-verified scenarios.
Extensive experiments demonstrate consistent gains of the proposed guardrail
over strong baselines on Pre-Exec Bench, and ablations further distill
actionable practices, providing a practical template for safer agentic systems.
68edcbffde1fee572713a927
Yue Huang
68edcbffde1fee572713a928
Hang Hua
claimed_verified
2025-10-14T07:29:51.627Z
639f8277beb95d698de007dd
/avatars/57f223ccd9d3cb03166ccf0e41361c58.svg
HangHua
hhua2
user
hhua2
68edcbffde1fee572713a929
Yujun Zhou
68edcbffde1fee572713a92a
Pengcheng Jing
68edcbffde1fee572713a92b
Manish Nagireddy
68edcbffde1fee572713a92c
Inkit Padhi
claimed_verified
2026-03-30T09:13:05.823Z
656d56f30a5ec499aa0f57b1
https://cdn-avatars.huggingface.co/v1/production/uploads/656d56f30a5ec499aa0f57b1/YdXgxiwpJ0-xdn0UvOjj4.jpeg
Inkit
ink-pad
user
ink-pad
68edcbffde1fee572713a92d
Greta Dolcetti
68edcbffde1fee572713a92e
Zhangchen Xu
68edcbffde1fee572713a92f
Subhajit Chaudhury
68edcbffde1fee572713a930
Ambrish Rawat
68edcbffde1fee572713a931
Liubov Nedoshivina
claimed_verified
2026-02-09T21:07:26.663Z
668cfac466f73756c8555a39
/avatars/1f101c07a0503aca49bb047290902ada.svg
Liubov Nedoshivina
nedshivina
user
nedshivina
68edcbffde1fee572713a932
Pin-Yu Chen
68edcbffde1fee572713a933
Prasanna Sattigeri
68edcbffde1fee572713a934
Xiangliang Zhang
Building a Foundational Guardrail for General Agentic Systems via
  Synthetic Data
While LLM agents can plan multi-step tasks, intervening at the planning
stage-before any action is executed-is often the safest way to prevent harm,
since certain risks can lead to severe consequences once carried out. However,
existing guardrails mostly operate post-execution, which is difficult to scale
and leaves little room for controllable supervision at the plan level. To
address this challenge, we highlight three critical gaps in current research:
data gap, model gap, and evaluation gap. To close the data gap, we introduce
AuraGen, a controllable engine that (i) synthesizes benign trajectories, (ii)
injects category-labeled risks with calibrated difficulty, and (iii) filters
outputs via an automated reward model, producing large and reliable corpora for
pre-execution safety. To close the guardian model gap, we propose a
foundational guardrail Safiron, combining a cross-planner adapter with a
compact guardian model. The adapter unifies different input formats, while
Safiron flags risky cases, assigns risk types, and generates rationales;
trained in two stages with a broadly explored data recipe, Safiron achieves
robust transfer across settings. To close the evaluation gap, we release
Pre-Exec Bench, a realistic benchmark covering diverse tools and branching
trajectories, which measures detection, fine-grained categorization,
explanation, and cross-planner generalization in human-verified scenarios.
Extensive experiments demonstrate consistent gains of the proposed guardrail
over strong baselines on Pre-Exec Bench, and ablations further distill
actionable practices, providing a practical template for safer agentic systems.
AuraGen and Safiron address pre-execution safety gaps in LLM agents by synthesizing benign trajectories, injecting risks, and using a cross-planner adapter for robust risk detection and explanation.
AuraGen Safiron cross-planner adapter compact guardian model automated reward model Pre-Exec Bench risk detection risk categorization explanation cross-planner generalization