Affected: nltk

NLTK through 3.10.3 contains a path traversal vulnerability in WekaClassifier that allows attackers to bypass nltk.pathsec containment by supplying unvalidated model paths. Attackers controlling the model_filename or model argument can make the weka JVM write files via -d or probe files via -l outside allowed data roots.

Affected: PyPI: nltk

## Summary `WekaClassifier` in NLTK passes a caller-controlled model file path to the weka Java VM without any `nltk.pathsec` validation. `WekaClassifier.train` puts the caller's `model_filename` verbatim into the weka argv `["-d", model_filename, ...]` (weka *writes* the trained model there), and `WekaClassifier._classify_many` puts `self._model` into `["-l", self._model, ...]` (weka *reads* it). Under `nltk.pathsec.ENFORCE=True` (the default on current HEAD), NLTK's stated invariant — established by advisory CVE-2026-81726 / GHSA-8mgp-746c-j5xp — is that caller-controlled model-artifact paths must be bounded to the allowed NLTK data roots before any native tool opens them. `WekaClassifier` is the one `java()`-based tool wrapper that skips this entirely, giving an untrusted workflow arbitrary-location file **write** (`-d`) and an arbitrary file **read/parse oracle** (`-l`), defeating pathsec containment.

This is a standalone, unfixed variant of the same class as CVE-2026-81726, on a code path the CVE-2026-81726 fix never touched. That advisory enumerates 6 functions (`TransitionParser.train`, `TransitionParser.parse`, `AveragedPerceptron.save`, `AveragedPerceptron.load`, `PerceptronTagger.save_to_json`, `save_maxent_params`) — `WekaClassifier` is not among them, and weka.py contains no `validate_tool_path`/`validate_model_resource` at all.

## Root Cause `WekaClassifier.train(cls, model_filename, ...)` builds `cmd = [javaclass, "-d", model_filename, "-t", train_filename]`…