GHSA-m4rf-3fr8-xwx3 CVE-2026-79675 PYSEC-2026-3749
NLTK: JVM argument injection bypass via per-call options in the NLTK Stanford wrappers (incomplete fix of CVE-2026-12841)
- Severity
- 0.0 / 10
- Critical · CVSS 4.0
- Exploit chance (EPSS)
- 0.78%
- Chance of exploitation in the next 30 days. Higher than 55.80% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-88 Improper Neutralization of Argument Delimiters in a Command ('Argument Injection')
- Fix
3.10.3- Upgrade nltk to this version or later
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:
GenericStanfordParser(nltk/parse/stanford.py): constructor parameter at line 39, stored at line 78, passed at lines 247 and 256StanfordTagger(nltk/tag/stanford.py): constructor parameter at line 51, stored at line 79, passed at line 118StanfordTokenizer(nltk/tokenize/stanford.py): constructor parameter at line 43, stored at line 66, passed at line 109StanfordSegmenter(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.
Affected versions
PyPI
| Package | Vulnerable | Fixed in |
|---|---|---|
| nltk | < 3.10.3 |
3.10.3 |
How to fix
Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.
nltk PyPI → 3.10.3
- pip
pip install --upgrade "nltk>=3.10.3"- poetry
poetry add "nltk>=3.10.3"- uv
uv add "nltk>=3.10.3"
Severity breakdown
CVSS 4.0 0.0
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
- Attack vector
- Network
- Attack complexity
- Low
- Attack requirements
- None
- Privileges required
- None
- User interaction
- None
- Confidentiality (vulnerable system)
- High
- Integrity (vulnerable system)
- High
- Availability (vulnerable system)
- High
- Confidentiality (subsequent systems)
- None
- Integrity (subsequent systems)
- None
- Availability (subsequent systems)
- None
CVSS 3.1 9.8
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- None
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- High
Weakness
CWE-88: 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.
How to prevent it
- Implementation: Where possible, avoid building a single string that contains the command and its arguments. Some languages or frameworks have functions that support specifying independent arguments, e.g. as an array, which is used to automatically perform the appropriate quoting or escaping while building the command. For example, in PHP, escapeshellarg() can be used to escape a single argument to system(), or exec() can be called with an array of arguments. In C, code can often be refactored from using system() - which accepts a single string - to using exec(), which requires separate function arguments for each parameter.
- Architecture and Design: Understand all the potential areas where untrusted inputs can enter your product: parameters or arguments, cookies, anything read from the network, environment variables, request headers as well as content, URL components, e-mail, files, databases, and any external systems that provide data to the application. Perform input validation at well-defined interfaces.
- Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
- Implementation: Directly convert your input type into the expected data type, such as using a conversion function that translates a string into a number. After converting to the expected data type, ensure that the input's values fall within the expected range of allowable values and that multi-field consistencies are maintained.
- Implementation: Inputs should be decoded and canonicalized to the application's current internal representation before being validated (CWE-180, CWE-181). Make sure that your application does not inadvertently decode the same input twice (CWE-174). Such errors could be used to bypass allowlist schemes by introducing dangerous inputs after they have been checked. Use libraries such as the OWASP ESAPI Canonicalization control. Consider performing repeated canonicalization until your input does not change any more. This will avoid double-decoding and similar scenarios, but it might inadvertently modify inputs that are allowed to contain properly-encoded dangerous content.
- Implementation: When exchanging data between components, ensure that both components are using the same character encoding. Ensure that the proper encoding is applied at each interface. Explicitly set the encoding you are using whenever the protocol allows you to do so.
- Implementation: When your application combines data from multiple sources, perform the validation after the sources have been combined. The individual data elements may pass the validation step but violate the intended restrictions after they have been combined.
- Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
References
Advisories
Packages
Other links
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
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| GHSA-ff5c-cp5c-9wjf NLTK: Uncontrolled resource consumption in RecursiveDescentParser via ambiguous or left-recursive grammars | Medium | Also affects nltk |
| GHSA-f794-5jv7-7672 NLTK: Downloader.download follows hardlinks and overwrites outside-root files | High | Also affects nltk |
| GHSA-8mgp-746c-j5xp NLTK: Model-artifact APIs bypass pathsec and touch files outside allowed roots | High | Also affects nltk |
| GHSA-8mpw-7fpc-4gqj NLTK: Pl196xCorpusReader has quadratic ReDoS on malformed TEI blocks | Low | Also affects nltk |
| GHSA-cw6x-m8jw-qmrh NLTK: Uncontrolled recursion in nltk.featstruct.FeatStructReader causes unhandled RecursionError (DoS) via deeply nested feature-structure input | Medium | Also affects nltk |
| GHSA-vp2x-qp44-57v7 NLTK: Quadratic CPU Exhaustion in `XMLCorpusView._read_xml_fragment()` | Low | Also affects nltk |
| GHSA-ww6m-cw3f-q94g NLTK: Quadratic-time DoS in PorterStemmer via long runs of 'y' | High | Also affects nltk |
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