Database errors can inadvertently expose sensitive information, even when Row Level Security (RLS) policies are in place. These errors might reveal tenant existence, schema details, identifiers, query structures, and even infrastructure states. For AI clients using database tools, it is recommended to keep raw SQLSTATE, constraint information, and traces in a restricted audit path, return stable public error classes, and authorize before distinguishing between 'missing' and 'exists elsewhere' data. Normalizing globally unique conflicts and rate-limiting repeated probes are also suggested practices to mitigate these data leakage risks. AI
IMPACT Mitigating data leakage from database errors is crucial for securing AI applications and user data.
RANK_REASON The item discusses best practices for handling database errors in the context of AI tools, focusing on preventing data leakage.
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