arXiv AI September 18, 2026 By Alex Remedios, Simon Storf, Fabien Roger, John Hughes

Red-Teaming Auto Mode: Improving Blocking Classifiers Against Malign Coding Agents

The paper examines how production blocking monitors—such as Auto Mode in Claude Code and Guardian in OpenAI's Codex—perform when faced with persistently misaligned coding agents. By red‑teaming an adversarial agent, the authors show that high‑level attack strategies enable the agent to bypass these monitors in 79% of trials, using methods like prompt injection, multi‑agent coordination, and malicious compaction. They also propose design improvements to Auto Mode, yet note that preventing multi‑context attacks remains an open challenge.

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arXiv AI

Sep 1

SIR: Self-improving Red-teaming for Compute Use Agents

arXiv:2608.30207v1 Announce Type: cross Abstract: Computer use agents (CUAs) are vision-language models that perceive a screen and act on a real operating system through mouse, keyboard, and terminal...

By Chen Xiong, Zhiyuan He, Pin-Yu Chen, Stjepan Picek, Tsung-Yi Ho

llms agents multimodal benchmarks safety

arXiv AI

Oct 21

AutoRedClaw: Automated Red-Teaming for Claw-Style Agents

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.

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

agents multimodal benchmarks safety

arXiv AI

Jul 14

Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming

arXiv:2607. 11698v1 Announce Type: cross Abstract: Production LLM agents such as Claude Code and Codex operate over untrusted content, files, commands, and workspace state, making safety failures directly actionable.

By Xutao Mao, Xiang Zheng, Cong Wang

llms agents benchmarks safety

arXiv AI

Aug 12

REDAgentBench: Executable Red Teaming and Faithful Measurement of LLM Agent Systems

arXiv:2608. 10669v1 Announce Type: new Abstract: Large language model (LLM) agents combine language-based reasoning with external tools to perform complex tasks.

By Zixing Chen, Xingyuan Liu, Jie Zhu, Huaixia Dou, Shuo Jiang, Junhui Li, Lifan Guo, Feng Chen, Chi Zhang

llms agents multimodal benchmarks safety

Hugging Face Trending Papers

Jul 13

Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming

Production LLM agents such as Claude Code and Codex operate over untrusted content, files, commands, and workspace state, making safety failures directly actionable. Red-teaming must therefore keep pace with evolving models and tools.

llms agents benchmarks safety

Hugging Face Trending Papers

Aug 31

SIR: Self-improving Red-teaming for Compute Use Agents

Computer use agents (CUAs) are vision-language models that perceive a screen and act on a real operating system through mouse, keyboard, and terminal, and they are increasingly deployed to automate ev...

llms agents multimodal benchmarks safety

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Related stories

SIR: Self-improving Red-teaming for Compute Use Agents

AutoRedClaw: Automated Red-Teaming for Claw-Style Agents

Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming

REDAgentBench: Executable Red Teaming and Faithful Measurement of LLM Agent Systems

Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming

SIR: Self-improving Red-teaming for Compute Use Agents