AutoRedClaw: Automated Red-Teaming for Claw-Style Agents
To address this, we introduce AutoRedClaw, an automated red-teaming framework that generates end-to-end attacks together with the environments needed to execute them.
ProofPaper ↗
Key points
- Agent systems are shifting from task-scoped tool-use agents to persistent, personalized assistants such as OpenClaw.
- AutoRedClaw combines a strict quality-checking protocol with execution-based validation to ensure the correctness, realism, and effectiveness of generated samples, and adopts task-driven environment construction to keep costs low.
- AutoRedClaw achieves an effective attack rate above 30% at less than $0.10 per successful sample.
- From its outputs, we construct RedClaw-Bench, a 330-case benchmark that yields substantially higher attack success rates (ASRs) than AgentDojo, AgentDyn, and DTap.
Sources (1)
- [1]AutoRedClaw: Automated Red-Teaming for Claw-Style AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 08:15 PM
To address this, we introduce AutoRedClaw, an automated red-teaming framework that generates end-to-end attacks together with the environments needed to execute them.
Agent systems are shifting from task-scoped tool-use agents to persistent, personalized assistants such as OpenClaw.
Extractive summary: sentences quoted from the sources.
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