Solo Dev Used Uncensored Local LLM to Stress-Test His macOS Security App

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A solo developer used a locally run uncensored large language model, Qwen 27B, to conduct adversarial QA testing on his macOS security application called RoamSwitch. Because commercial cloud-based LLMs reject prompts involving hostile scripts or malware patterns, running the model locally allowed unrestricted automated attacks including ARP injection, backdoor listeners, and quarantine evasion payloads against an isolated macOS virtual machine. The testing uncovered real architectural flaws that standard unit tests had missed, including a bug where a second process using the same binary name bypassed port anomaly detection entirely. A fix was shipped in version 1.6.2, scoping trust to a specific executable path and port combination rather than binary name alone. The exercise highlighted how adversarial AI-driven QA can expose edge cases that developer-written test scripts tend to overlook due to inherent bias.

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