A dataset-processing vulnerability is a weakness in code that loads, parses, validates, or transforms data before it is used by an application or model. It can appear in CSV, JSON, image, archive, or machine-learning dataset handlers, especially when the code trusts file contents, performs unsafe deserialization, follows attacker-controlled paths, or enables code execution through “trusted” loading options.
These flaws matter because attackers can hide malicious payloads inside seemingly ordinary data files. A successful exploit may cause code execution, secret leakage, data corruption, or a pivot from a sandbox into wider systems. Defenders reduce the risk by treating datasets as untrusted input, disabling execution features by default, using strict parsers and allowlists, isolating loaders, and applying least privilege and egress controls. In practice, the dataset pipeline should be secured like any other attack surface, not treated as a passive utility.