A new study explores skill-conditional trust in heterogeneous LLM agent swarms, revealing that traditional global trust scores may overlook the benefits of specialization. The research shows that conditional trust can improve task routing efficiency under specific conditions but also presents vulnerabilities to manipulation. This has implications for the design of trust systems in AI, highlighting the need for careful consideration of evidence sources. The findings suggest a balance between leveraging cross-skill data and safeguarding against potential attacks.