Black-box red teaming of CrewAI and AutoGen finds 65% privacy risk in multi-agent configs and 85% agent-behavior vulnerability

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arXiv 2609.09647 (2026-09-09) presents a black-box agent red-teaming framework needing only a basic system description: a seven-domain risk taxonomy, automated SAGE-RT generation of 120 adversarial scenarios per domain, and human-validated LLM-judge scoring. Run against CrewAI and AutoGen with four base models, it measured 56.25% average governance risk, 65% privacy risk in multi-agent configurations, and agent-behavior vulnerability rates reaching 85%. The privacy number rising specifically in multi-agent setups is the finding worth acting on — adding a second agent adds a data path that single-turn chat evaluations never test.