Who audits the auditors? Do LLM auditors go easy on collusion by their own model family?

LLMs are increasingly used to check whether AI agents are colluding, and the auditor can come from the same model family as the agents it audits. Prior work predicts opposite effects: LLM judges favour their own family, but monitors are best at catching sabotage by the lineage they were tuned on. We tested which holds for prompted auditors of multi-agent collusion. In 96 episodes of the Colosseum task-allocation environment, two of four agents were either instructed to form a coalition or not, with or without a private channel. Three open-weight model families (Gemma 4, GPT-OSS-120B, Nemotron 3 Super) played the agents and audited every episode under four views of the log: messages only, actions only, both, and a neutral summary. That is 3,456 audits, analysed under a pre-registered plan. Every auditor was competent on the full log (AUROC 0.81–0.89). We found no significant same-family effect (−0.029 AUROC, 95% CI −0.063 to +0.004), which rules out a familiarity advantage of the size reported for trained monitors; the estimates lean towards leniency. The larger problem was what auditors read. With actions only, all three were at chance. With messages, suspicion followed how much the coalition talked, not what it gained, and counting the single word "coalition" came close to the auditors' accuracy. Summarising the log lowered detection. We release the code, logs and frozen prompts: https://github.com/faheemgurkani/multi-agent-collusion-auditor-family-evidence