ASTRA: Autonomous Spatial-Temporal Red-teaming for AI Software
  Assistants
AI coding assistants like GitHub Copilot are rapidly transforming software
development, but their safety remains deeply uncertain-especially in
high-stakes domains like cybersecurity. Current red-teaming tools often rely on
fixed benchmarks or unrealistic prompts, missing many real-world
vulnerabilities. We present ASTRA, an automated agent system designed to
systematically uncover safety flaws in AI-driven code generation and security
guidance systems. ASTRA works in three stages: (1) it builds structured
domain-specific knowledge graphs that model complex software tasks and known
weaknesses; (2) it performs online vulnerability exploration of each target
model by adaptively probing both its input space, i.e., the spatial
exploration, and its reasoning processes, i.e., the temporal exploration,
guided by the knowledge graphs; and (3) it generates high-quality
violation-inducing cases to improve model alignment. Unlike prior methods,
ASTRA focuses on realistic inputs-requests that developers might actually
ask-and uses both offline abstraction guided domain modeling and online domain
knowledge graph adaptation to surface corner-case vulnerabilities. Across two
major evaluation domains, ASTRA finds 11-66% more issues than existing
techniques and produces test cases that lead to 17% more effective alignment
training, showing its practical value for building safer AI systems.
6893fdd6741a16f544fbce13
Xiangzhe Xu
claimed_verified
2025-08-07T10:41:51.786Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64416825bd0c972652986a9e/1JgP2oCeEwyj_EC_D2gBn.png
Alex Xu
user
Alex-xu
6893fdd6741a16f544fbce14
Guangyu Shen
6893fdd6741a16f544fbce15
Zian Su
claimed_verified
2025-08-25T08:28:45.286Z
633652d438e33ddc2657fcc0
https://cdn-avatars.huggingface.co/v1/production/uploads/633652d438e33ddc2657fcc0/n7Bvc4FIOsLJPZjnuAW_7.png
Zian Su
user
ziansu
6893fdd6741a16f544fbce16
Siyuan Cheng
6893fdd6741a16f544fbce17
Hanxi Guo
6893fdd6741a16f544fbce18
Lu Yan
claimed_verified
2025-08-14T13:40:11.377Z
6487971dde9f68b81b03d67f
/avatars/e79a419c5d8350f7fba9c72342f9504e.svg
Lu Y
user
who-am-i
6893fdd6741a16f544fbce19
Xuan Chen
6893fdd6741a16f544fbce1a
Jiasheng Jiang
6893fdd6741a16f544fbce1b
Xiaolong Jin
6893fdd6741a16f544fbce1c
Chengpeng Wang
6893fdd6741a16f544fbce1d
Zhuo Zhang
6893fdd6741a16f544fbce1e
Xiangyu Zhang
ASTRA: Autonomous Spatial-Temporal Red-teaming for AI Software
  Assistants
AI coding assistants like GitHub Copilot are rapidly transforming software
development, but their safety remains deeply uncertain-especially in
high-stakes domains like cybersecurity. Current red-teaming tools often rely on
fixed benchmarks or unrealistic prompts, missing many real-world
vulnerabilities. We present ASTRA, an automated agent system designed to
systematically uncover safety flaws in AI-driven code generation and security
guidance systems. ASTRA works in three stages: (1) it builds structured
domain-specific knowledge graphs that model complex software tasks and known
weaknesses; (2) it performs online vulnerability exploration of each target
model by adaptively probing both its input space, i.e., the spatial
exploration, and its reasoning processes, i.e., the temporal exploration,
guided by the knowledge graphs; and (3) it generates high-quality
violation-inducing cases to improve model alignment. Unlike prior methods,
ASTRA focuses on realistic inputs-requests that developers might actually
ask-and uses both offline abstraction guided domain modeling and online domain
knowledge graph adaptation to surface corner-case vulnerabilities. Across two
major evaluation domains, ASTRA finds 11-66% more issues than existing
techniques and produces test cases that lead to 17% more effective alignment
training, showing its practical value for building safer AI systems.
ASTRA, an automated agent system, uncovers safety flaws in AI-driven code generation and security guidance by building knowledge graphs, exploring vulnerabilities, and generating violation-inducing cases, outperforming existing methods in real-world scenarios.
knowledge graphs vulnerability exploration input space reasoning processes violation-inducing cases domain modeling domain knowledge graph adaptation