We propose a distributed trust information management framework for crowdsourced IoT services. The crowdsourced IoT service environment consists of distributed entities that store and manage trust information. Traditional trust management frameworks often assume the trustworthiness of these entities. However, they may tamper with trust data, making the system vulnerable to internal attacks. The rise of AI tools, such as ChatGPT, has further lowered the barrier for adversaries, enabling even low-skilled actors to manipulate trust information with alarming sophistication. AI-assisted tampering could severely compromise trust assessments and mislead IoT service users. To counter this emerging threat, we propose a novel AI-based tampering detection approach, complemented by a heuristic-based approach to identify tampered trust information. We conduct a series of experiments to evaluate the effectiveness of the proposed approaches. The results demonstrate more than 95% accuracy, confirming the effectiveness of our approach.