Why is deserializing untrusted pickle or unsafe YAML dangerous in Python?

Explains code execution during object reconstruction and chooses data-only formats with explicit validation.

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pickle can reconstruct Python objects by importing and invoking callables, so a malicious payload can execute code during unpickling. I never unpickle data from users, queues, caches, files, or databases unless its origin and integrity are fully trusted. YAML loaders that construct arbitrary Python objects have the same class of risk. For untrusted input I use a data-only format such as JSON or a safe YAML loader, then validate the resulting shape.

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Explains code execution during object reconstruction and chooses data-only formats with explicit validation.

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Explains code execution during object reconstruction and chooses data-only formats with explicit validation.
pickle can reconstruct Python objects by importing and invoking callables, so a malicious payload can execute code during unpickling. I never unpickle data from users, queues, caches, files, or databases unless its origin and integrity are fully trusted. YAML loaders that construct arbitrary Python objects have the same class of risk. For untrusted input I use a data-only format such as JSON or a safe YAML loader, then validate the resulting shape.
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A file being internal or base64-encoded does not make it trusted. Attackers can reach caches, object storage, job payloads, model artifacts, or signed links through another vulnerability. An HMAC can prove a payload was produced by someone holding the key, but it does not make old vulnerable producer code safe or solve key compromise. Allowlisting classes in a custom unpickler is difficult to review and is not my default API boundary.

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Unpickling data because it came from an internal bucket
Calling base64 encryption
Using a generic YAML loader for submitted configuration

Why is deserializing untrusted pickle or unsafe YAML dangerous in Python?

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