Vulnerability GHSA-v5jw-96jm-7h2c

Summary

Stanza 1.12.0 attempts to safely load PyTorch checkpoint files using torch.load(..., weights_only=True), but automatically falls back to the fully unsafe torch.load(..., weights_only=False) when the safe load raises pickle.UnpicklingError. Because the UnpicklingError condition is fully attacker-controllable, any .pt file that contains a single unsupported pickle global will trigger it.

An attacker who can place a malicious pretrain or model file on disk (via supply-chain compromise, a poisoned model repository, or a shared model cache) can achieve arbitrary code execution on any machine that loads a Stanza NLP pipeline.

Code execution occurs inside the Stanza pretrain-loading API, not merely by calling torch.load directly.

Details

The vulnerable code is in pretrain.py#L59-L67 (Stanza 1.12.0):

try:
    data = torch.load(self.filename, lambda storage, loc: storage, weights_only=True)
except UnpicklingError:
    data = torch.load(self.filename, lambda storage, loc: storage, weights_only=False)

When weights_only=True is passed, PyTorch's deserializer raises pickle.UnpicklingError for any object whose class or callable is not on the safe-globals allowlist. This is the intended safety mechanism. However, Stanza catches that exception and immediately reloads the same attacker-controlled file with weights_only=False, which invokes Python's full pickle deserializer and executes any __reduce__ method in the file without restriction.

The fallback is triggered reliably and intentionally: an attacker embeds one unsupported pickle global (e.g., builtins.open) anywhere in an otherwise structurally valid Stanza pretrain state dict. The safe load rejects it; the unsafe reload runs it.

The same try/except pattern exists in at least five additional loaders in Stanza 1.12.0:

Additionally, stanza/models/lemma_classifier/base_model.py:127 calls torch.load(filename, lambda storage, loc: storage) with no weights_only argument at all, which defaults to False on any PyTorch < 2.6.

The call chain from the public API to the vulnerable fallback is:

stanza.models.common.foundation_cache.load_pretrain(path)
  → FoundationCache.load_pretrain(path)
    → stanza.models.common.pretrain.Pretrain(filename)
      → Pretrain.emb  (property access triggers load)
        → Pretrain.load()
          → torch.load(..., weights_only=True)   # raises UnpicklingError
          → torch.load(..., weights_only=False)  # executes arbitrary pickle

Impact

Vulnerability class: CWE-502 : Deserialization of Untrusted Data

Who is impacted: Any user, researcher, CI/CD pipeline, or production NLP service that loads a Stanza model pretrain file from a source that is not under the victim's exclusive cryptographic control. Concretely:

• Developers who run stanza.Pipeline(lang) after downloading models from HuggingFace or GitHub
• CI pipelines that automatically refresh Stanza models during builds
• Research environments that share pretrain files over shared network storage or model repositories

Attack prerequisites: The attacker must be able to place a malicious .pt pretrain file at a path that Stanza will load. Realistic delivery vectors include:

• Compromise of a HuggingFace model repository hosting Stanza pretrain weights
• Poisoning of a shared model cache directory (NFS, S3, artifact store)
• A malicious pretrain file distributed via a third-party fine-tuning hub or research repo

What an attacker achieves: Arbitrary code execution with the full privileges of the process running stanza.Pipeline(), typically a developer workstation, a Jupyter notebook server, or a GPU training node. This allows credential theft (HuggingFace tokens, cloud IAM keys from environment variables), persistent backdoors, data exfiltration, and lateral movement in multi-tenant training infrastructure.

Recommended fix:

Remove the unsafe fallback entirely. If weights_only=True raises UnpicklingError, fail closed:

try:
    data = torch.load(self.filename, lambda storage, loc: storage, weights_only=True)
except UnpicklingError as e:
    raise RuntimeError(
        f"Refusing to load legacy pretrain file {self.filename!r} with unsafe "
        "deserialization. Regenerate the file using a trusted Stanza migration tool."
    ) from e

If legacy NumPy-containing pretrain files must be supported, use PyTorch's add_safe_globals() API to allowlist the specific NumPy dtypes required, rather than disabling all safety checks. Apply the same fix to all six affected loaders listed above.