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NLTK: Allowlisted pickle loaders still permit code execution in current source

Critical severity GitHub Reviewed Published Aug 11, 2026 in nltk/nltk • Updated Sep 8, 2026

Package

pip nltk (pip)

Affected versions

<= 3.10.2

Patched versions

3.10.3

Description

Summary

The current source tree still allows arbitrary code execution during supposedly safer allowlisted pickle loading. The allowlist trusts whole module namespaces instead of exact safe globals, so crafted pickles can invoke dangerous in-namespace callables through pickle REDUCE.

Details

  • Vulnerability type: Remote code execution via unsafe deserialization
  • Affected component: nltk.picklesec.allowlisted_pickle_load, nltk.tokenize.punkt.punkt_pickle_load, nltk.parse.transitionparser.TransitionParser.parse
  • Affected versions: Current source v3.10.0-rc2; published 3.9.4 was not the claim target for this bypass.
  • Patched versions: Not yet patched
  • Root cause: Module-prefix allowlists include dangerous callables such as nltk.tokenize.repp.ReppTokenizer._execute and numpy.f2py.crackfortran.myeval.

punkt_pickle_load() allowlists both nltk.tokenize.punkt and the whole nltk.tokenize namespace, which exposes ReppTokenizer._execute() and its subprocess.Popen(...) sink during unpickling. TransitionParser.parse() uses allowlisted_pickle_load(..., allowed_modules=("numpy", "scipy", "sklearn")), which permits numpy.f2py.crackfortran.myeval() and its attacker-controlled eval(...) path. I confirmed both gadgets create marker files before the caller returns or later aborts on type misuse.

PoC

Preconditions

  • The application loads an attacker-controlled tokenizer or model artifact through these public loaders.

Steps

  1. Create a pickle whose REDUCE callable is ReppTokenizer._execute and point its command to a harmless marker-file write.
  2. Pass that payload to punkt_pickle_load(BytesIO(payload)) and observe the marker file is created during unpickling.
  3. Create a second pickle whose REDUCE callable is numpy.f2py.crackfortran.myeval and load it through TransitionParser.parse().
  4. Observe the second marker file is created before TransitionParser.parse() later fails on the returned object type.

Minimal reproducible excerpt

{'punkt_marker': 'PUNKT_RCE', 'transitionparser_marker': 'TP_RCE'}

Impact

Any caller that trusts these current allowlisted loaders can still execute attacker-controlled commands while loading model or tokenizer artifacts. This defeats the protection mechanism that replaced unrestricted pickle loading and creates a dangerous false sense of safety.

Remediation

Replace broad module-prefix allowlists with exact (module, qualname) pairs for the few safe classes or functions genuinely required. Do not allow entire namespaces such as nltk.tokenize or numpy, and keep post-load type validation only as a secondary defense.

Resources

Fix + attack demonstration (verified)

  • tightened callers
    find_class now, before the allowlists:
  1. Rejects any dotted name → closes 4489 with zero legit impact.
  2. Denies dangerous modules (os, subprocess, sys, builtins, numpy.f2py, nltk.tokenize.repp, …) even under a broad allowed_modules — a defense-in-depth backstop so a future too-broad allowlist can't silently reopen RCE.
  3. builtins denied wholesale; safe primitives (int, str, …) must be named exactly via allowed_globals.

Callers tightened: punkt drops the broad nltk.tokenize (keeps nltk.tokenize.punkt + exact collections.defaultdict/builtins.int); transitionparser keeps numpy/scipy/sklearn (array unpickling needs their submodules) with the new guards blocking the gadgets.

Full pickle-sink audit

Every deserialization sink in the tree was reviewed: no raw pickle.load anywhere, and no joblib/numpy/torch/dill/yaml/marshal loaders. data.load + wordnet_app use RestrictedUnpickler (blocks all globals — safe); the remaining pickle_load sites (chartparser_app, tbl/demo) load user-selected or self-written files and keep their warning.

Attack demonstration (captured; fork clone)

=== EXPLOITS blocked ===
  4489 sklearn.os.system (dotted)      -> BLOCKED
  x99w numpy.f2py.crackfortran.myeval  -> BLOCKED
  x99w nltk.tokenize.repp._execute     -> BLOCKED
  backstop os.system (os allowlisted)  -> BLOCKED
  backstop builtins.eval (exact global)-> BLOCKED
=== LEGIT loads still work ===
  punkt round-trip via punkt_pickle_load -> OK
  builtins.int (safe primitive)          -> OK

Tests

test_pickle_allowlist_security.py — added 5 regressions (dotted traversal, both namespace gadgets, denied-module backstop, legit round-trip). Suite: 122 passed / 9 skipped (sklearn-dependent) across pickle/punkt/transition/tokenize. pre-commit (black/isort/ruff) clean.

References

@alvations alvations published to nltk/nltk Aug 11, 2026
Published to the GitHub Advisory Database Sep 8, 2026
Reviewed Sep 8, 2026
Last updated Sep 8, 2026

Severity

Critical

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality High
Integrity High
Availability High
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(69th percentile)

Weaknesses

Deserialization of Untrusted Data

The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid. Learn more on MITRE.

Protection Mechanism Failure

The product does not use or incorrectly uses a protection mechanism that provides sufficient defense against directed attacks against the product. Learn more on MITRE.

CVE ID

CVE-2026-79657

GHSA ID

GHSA-x99w-6fgc-pmfw

Source code

Credits

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