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arXiv AI By Hao Li, Tongyan Hu, Weidi Luo, Shanghao Shi, Xiaogeng Liu, Aiden Suh, Yulong Cao, Ning Zhang, William Yeoh, Yevgeniy Vorobeychik, Chaowei Xiao

AutoRedClaw: Automated Red-Teaming for Claw-Style Agents

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AutoRedClaw is an automated red‑teaming framework designed for claw‑style agents such as OpenClaw, which maintain persistent configuration, memory, and reusable skills. It generates end‑to‑end attacks and the necessary environments, using a strict quality‑checking protocol and execution‑based validation to ensure realistic, effective samples. The framework produces a 330‑case benchmark, RedClaw‑Bench, achieving over 30% effective attack rate at less than $0.10 per successful sample and demonstrating higher attack success rates than existing benchmarks across multiple agent systems.

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