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NLTK: JVM argument injection bypass via per-call options in the NLTK Stanford wrappers (incomplete fix of CVE-2026-12841)

Critical severity GitHub Reviewed Published Aug 11, 2026 in nltk/nltk • Updated Sep 1, 2026

Package

pip nltk (pip)

Affected versions

<= 3.10.2

Patched versions

3.10.3

Description

Vulnerability

The fix for CVE-2026-12841 (CWE-88, JVM argument injection) added _validate_java_options() to block dangerous JVM flags such as -agentlib, -agentpath, -javaagent, -Xrunjdwp, and @argfile references. However, the validation is only applied when setting global options via config_java(). The java() function's per-call options parameter -- added by PR #3683 (CVE-2026-12615 fix) -- passes options directly to subprocess.Popen without calling _validate_java_options().

All four Stanford Java wrapper classes accept user-supplied java_options and route them through the unvalidated per-call path, bypassing the CVE-2026-12841 fix entirely.

Root Cause

In nltk/internals.py, the java() function (line 128) accepts an options keyword argument. When options is not None, it is converted to a list and prepended to the JVM command (lines 211-217) without any validation:

# nltk/internals.py, lines 211-217 (HEAD)
if options is None:
    java_options = _java_options       # validated by config_java()
else:
    if isinstance(options, str):
        options = options.split()
    java_options = list(options)       # NO validation
cmd = [_java_bin] + java_options + cmd

Compare with config_java() (line 92) which does validate:

# nltk/internals.py, lines 122-123
_validate_java_options(options)
_java_options[:] = options

The four affected wrapper classes store user-supplied java_options without validation and pass them through the unvalidated per-call path:

  1. GenericStanfordParser (nltk/parse/stanford.py): constructor parameter at line 39, stored at line 78, passed at lines 247 and 256
  2. StanfordTagger (nltk/tag/stanford.py): constructor parameter at line 51, stored at line 79, passed at line 118
  3. StanfordTokenizer (nltk/tokenize/stanford.py): constructor parameter at line 43, stored at line 66, passed at line 109
  4. StanfordSegmenter (nltk/tokenize/stanford_segmenter.py): constructor parameter at line 68, stored at line 117, passed at line 337

Proof of Concept

from nltk.internals import config_java, java, _validate_java_options

# 1. The global config_java() path correctly blocks dangerous flags:
try:
    config_java(options=["-agentpath:/tmp/evil.so"])
except ValueError as e:
    print(f"config_java blocked: {e}")   # blocked as expected

# 2. The per-call options path does NOT block them:
# (Would execute if Java were installed)
# java(["SomeClass"], classpath=".", options=["-agentpath:/tmp/evil.so"])
# This passes "-agentpath:/tmp/evil.so" directly to subprocess.Popen

# 3. Stanford wrapper classes pass through without validation:
# from nltk.parse.stanford import StanfordParser
# parser = StanfordParser(java_options="-agentpath:/tmp/evil.so")
# parser.parse(...)  # dangerous flag reaches JVM

# Verify the gap directly:
dangerous_opts = ["-agentpath:/tmp/evil.so"]
try:
    _validate_java_options(dangerous_opts)
    print("Would have been caught")
except ValueError:
    print("Correctly rejected by _validate_java_options()")

# But java() itself never calls _validate_java_options():
import inspect
source = inspect.getsource(java)
assert "_validate_java_options" not in source, "java() does not validate options"
print("Confirmed: java() does not call _validate_java_options()")

Impact

An attacker who controls the java_options parameter to any NLTK Stanford wrapper class can inject arbitrary JVM flags, including:

  • -agentpath:/path/to/malicious.so -- loads a native agent, achieving arbitrary code execution
  • -javaagent:/path/to/malicious.jar -- loads a Java agent for bytecode manipulation
  • -agentlib:jdwp=transport=dt_socket,server=y,address=*:5005 -- enables remote debugging, allowing remote code execution
  • @/path/to/argfile -- expands an argument file, which can smuggle any of the above

This is exploitable in scenarios where NLTK is deployed as a service and java_options is derived from user input, configuration files, or environment variables. The PR #3647 commit message explicitly states the fix was intended to cover "StanfordSegmenter, and GenericStanfordParser" but the implementation only validates in config_java().

Suggested Fix

Add _validate_java_options() to the java() function's per-call options handling:

# nltk/internals.py, in the java() function
if options is None:
    java_options = _java_options
else:
    if isinstance(options, str):
        options = options.split()
    java_options = list(options)
    _validate_java_options(java_options)   # ADD THIS LINE
cmd = [_java_bin] + java_options + cmd

This single-line addition closes the bypass for all four Stanford wrapper classes and any future callers of java(options=...).

AI tooling

AI assistance was used for the code audit and for drafting this report. The finding were manually verified against the project's source at the location cited above before reporting it, and the severity and impact assessment are the reporters.

References

@alvations alvations published to nltk/nltk Aug 11, 2026
Published to the GitHub Advisory Database Sep 1, 2026
Reviewed Sep 1, 2026
Last updated Sep 1, 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.
(36th percentile)

Weaknesses

Improper Neutralization of Argument Delimiters in a Command ('Argument Injection')

The product constructs a string for a command to be executed by a separate component in another control sphere, but it does not properly delimit the intended arguments, options, or switches within that command string. Learn more on MITRE.

CVE ID

CVE-2026-79675

GHSA ID

GHSA-m4rf-3fr8-xwx3

Source code

Credits

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