GHSA-J456-XH4H-CPF2

GHSA-J456-XH4H-CPF2 is a high-severity path traversal vulnerability in nltk (pip), affecting versions <= 3.10.3. It is fixed in 3.10.4.

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Summary

NLTK: WekaClassifier passes an unvalidated caller-controlled model path to the weka JVM, bypassing nltk.pathsec containment

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] (nltk/classify/weka.py:256) and WekaClassifier._classify_many builds [..., "-l", self._model, "-T", <staged>, ...] (weka.py:131-133); self._model is stored unvalidated in __init__ (weka.py:108-110). Only the internally-generated train.arff/test.arff are staged via make_staging_dir; the model path is passed as-is. nltk.internals.java() (internals.py:388-431) validates the classpath (_verify_jar_sandbox), the JVM launcher options (_validate_java_options, the CVE-2026-12841 fix), the main-class token cmd[0], and rejects @argfile tokens in any position, but it explicitly permits program arguments starting with - and never inspects the interior of a program-arg path value. So -d <path> / -l <path> flow unchecked to subprocess.Popen(final_cmd, shell=False) and the weka JVM performs an unbounded native open/write on the caller-chosen path.

Sibling wrappers prove the intended pattern: CRFTagger.train/set_model_file call validate_tool_path (crf.py:134,300); MaltParser.generate_malt_command validates -i/-o via validate_tool_path (malt.py:340,344), its model via validate_model_resource (malt.py:326) and working_dir via validate_path (malt.py:206). weka.py already uses pathsec elsewhere (validate_path for WEKAHOME, pathsec_open for ARFF writes) but never for the model path.

Proof of Concept

import nltk.pathsec as ps; ps.ENFORCE = True
from nltk.classify.weka import WekaClassifier
# write-side: model written OUTSIDE any allowed NLTK data root (refused for CRFTagger, allowed here)
WekaClassifier.train("/tmp/outside_sandbox/evil.model",
                     [({"a": 1}, "pos"), ({"a": 0}, "neg")])
# read-side: -l opens an arbitrary path
clf = WekaClassifier(fmt, "/home/victim/secret.model")
clf.classify_many([{"a": 1}])

The argv construction, the vulnerable step, is confirmed without weka installed (see Bypass Evidence); a configured weka.jar is needed only to observe the JVM's actual file I/O.

Attack Chain

  1. Entry. Action: an app running with pathsec.ENFORCE=True lets an untrusted workflow choose the weka model path and calls WekaClassifier.train("../../../home/victim/x", featuresets) (write) or WekaClassifier(fmt, "/home/victim/secret.model").classify_many(...) (read). Guard: none, plain classmethod/constructor parameter (weka.py:230,108-110). Bypass proof: grep shows weka.py uses no validate_tool_path/validate_model_resource/validate_tool_dir (NONE).
  2. Command build. Action: the path is placed into cmd = [javaclass, "-d", model_filename, "-t", train_filename] (weka.py:256) / ["-l", self._model, ...]. Guard: only the internally-generated train.arff is staged via make_staging_dir; the model path is passed as-is. Bypass proof: runtime capture yielded ['weka.classifiers.bayes.NaiveBayes', '-d', '/tmp/outside_sandbox_weka.model', '-t', '<staged>/train.arff'], the raw out-of-root path is cmd[2].
  3. java() sink. Action: java(cmd, classpath=_weka_classpath, ...) → subprocess.Popen(final_cmd) (internals.py:431). Guard: java() validates classpath, JVM options, cmd[0], and rejects @argfile tokens. Bypass proof: none of these inspect the -d/-l program-arg value; internals.py explicitly documents that program args after the main class may start with -, and the path is neither cmd[0] nor an @-token, so every java() gate passes. Negative control: the same path through validate_tool_path(..., for_write=True, must_exist=False) raises PermissionError.
  4. Impact. Action: the weka JVM opens the path outside the allowed roots → arbitrary-location write (-d) / parse-existence oracle (-l). Guard: pathsec is never consulted (no validate_tool_path, no pathsec_open). Bypass proof: the directly-analogous sibling CRFTagger.train calls validate_tool_path(model_file, must_exist=False) (crf.py:300); weka does not, and commit ee6d6138 hardened weka's other sinks (scratch files, WEKAHOME jar) while leaving the model path unvalidated.

Bypass Evidence

  • Negative control (guard is real): validate_tool_path("/tmp/outside_sandbox_weka.model", for_write=True, must_exist=False) → PermissionError; validate_tool_path("../../../home/victim/.ssh/authorized_keys", ...) → PermissionError.
  • Sink reached unvalidated (guard absent): captured write-side argv ['weka.classifiers.bayes.NaiveBayes', '-d', '/tmp/outside_sandbox_weka.model', '-t', '<staged>/train.arff'] and read-side [..., '-l', '/home/victim/secret.model', '-T', '<staged>/test.arff', '-p', '0'].
  • Version: git show v3.10.3:nltk/classify/weka.py contains both the -d model-path build (line 235) and the -l read, and NO validate_tool_path.

Affected Versions

<= 3.10.3 (present on the latest release tag v3.10.3 and on HEAD c06cd264). No published fix has ever targeted the weka -d/-l model path; commits after v3.10.3 (ee6d6138, 92aeada6, a9752559) touched weka.py for other sinks but did not add model-path validation.

Fix status (2026-09-28)

Status: MERGED on develop (unreleased; v3.10.3 of 2026-08-12 predates the fix).

PR: #3863 "Bound the WekaClassifier model path to the pathsec data roots", merged 2026-09-06, merge commit bc2878725 on develop. Fix commit: 6a1760629.

Probe: nltk/test/unit/security_probes/ghsa_j456_xh4h_cpf2.py, added by PR #3905 "Add GHSA-j456 live probe; expand the GHSA-8mgp probe attack set" (commit a96715264, merged 2026-09-20, merge commit 84d987bf8); verdict on develop 2fa3f48fe: FIXED (WekaClassifier -l and -d refuse out-of-root model paths).

Release: none yet; no release tag contains 6a1760629.

Impact

Under pathsec.ENFORCE=True (default on HEAD), an untrusted workflow that chooses the weka model save/load path obtains:

  • Arbitrary-location file write via -d, overwrite or create a file at an attacker-chosen absolute or ..-traversal path outside the allowed NLTK data roots (content constrained to weka's serialized-model format; the target choice is the integrity impact). This corrupts/clobbers arbitrary chosen files.
  • Arbitrary file read/parse oracle via -l, weka loads an out-of-root path as a model, yielding an existence/parse differential (file content is not echoed back → confidentiality impact is limited).

This defeats the pathsec containment advisory CVE-2026-81726 promises, at the same privilege level / threat model that advisory was accepted under.

Input manipulates file paths to reach files outside the intended directory, such as configuration or credential files. Typical impact: unauthorized file read or write outside the intended directory.

GHSA-J456-XH4H-CPF2 has a CVSS score of 8.6 (High). The vector is network-reachable, no privileges required, and no user interaction. A CVSS score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether this affects your application depends on whether the vulnerable code is present and reachable in your environment. A fixed version is available (3.10.4); upgrading removes the vulnerable code path.

Affected versions

nltk (<= 3.10.3)

Security releases

nltk → 3.10.4 (pip)

Kodem intelligence

Severity tells you how bad this could be in the worst case. It does not tell you whether you are exposed. Exploitability and impact are functions of runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A vulnerable package can sit in your dependency tree and never run.

Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter. Kodem's runtime-powered SCA identifies whether this CVE is reachable in your applications.

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Remediation advice

Before building the -d/-l/-T command, validate the model path with pathsec, mirroring CRFTagger/MaltParser:

validate_tool_path(model_filename, context="WekaClassifier.train", for_write=True, must_exist=False)
validate_tool_path(self._model, context="WekaClassifier._classify_many")

Reported by zx (Jace), GitHub: @manus-use

Frequently Asked Questions

  1. What is GHSA-J456-XH4H-CPF2? GHSA-J456-XH4H-CPF2 is a high-severity path traversal vulnerability in nltk (pip), affecting versions <= 3.10.3. It is fixed in 3.10.4. Input manipulates file paths to reach files outside the intended directory, such as configuration or credential files.
  2. How severe is GHSA-J456-XH4H-CPF2? GHSA-J456-XH4H-CPF2 has a CVSS score of 8.6 (High). This score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether it represents real risk in your environment depends on whether the vulnerable code is present and reachable.
  3. Which versions of nltk are affected by GHSA-J456-XH4H-CPF2? nltk (pip) versions <= 3.10.3 is affected.
  4. Is there a fix for GHSA-J456-XH4H-CPF2? Yes. GHSA-J456-XH4H-CPF2 is fixed in 3.10.4. Upgrade to this version or later.
  5. Is GHSA-J456-XH4H-CPF2 exploitable, and should I be worried? Whether GHSA-J456-XH4H-CPF2 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
  6. What actually determines whether GHSA-J456-XH4H-CPF2 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
  7. How do I fix GHSA-J456-XH4H-CPF2? Upgrade nltk to 3.10.4 or later.

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