GHSA-7p94-766c-hgjp CVE-2025-14009 PYSEC-2026-96

NLTK has a Zip Slip Vulnerability

High PyPI

Severity: 10.0 / 10; High · CVSS 3.0
Exploit chance (EPSS): 1.04%; Chance of exploitation in the next 30 days. Higher than 63.92% of scored vulnerabilities.
Known exploited: No; Not in CISA's catalog
Weakness: CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix: 3.9.3; Upgrade nltk to this version or later

Description

A critical vulnerability exists in the NLTK downloader component of nltk/nltk, affecting all versions. The _unzip_iter function in nltk/downloader.py uses zipfile.extractall() without performing path validation or security checks. This allows attackers to craft malicious zip packages that, when downloaded and extracted by NLTK, can execute arbitrary code. The vulnerability arises because NLTK assumes all downloaded packages are trusted and extracts them without validation. If a malicious package contains Python files, such as init.py, these files are executed automatically upon import, leading to remote code execution. This issue can result in full system compromise, including file system access, network access, and potential persistence mechanisms.

Affected versions

PyPI

Package	Vulnerable	Fixed in
nltk	`< 3.9.3`	`3.9.3`

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 → `3.9.3`

pip

pip install --upgrade "nltk>=3.9.3"

poetry

poetry add "nltk>=3.9.3"

uv

uv add "nltk>=3.9.3"

Severity breakdown

CVSS 3.0 10.0

CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H

Attack vector: Network
Attack complexity: Low
Privileges required: None
User interaction: None
Scope: Changed
Confidentiality: High
Integrity: High
Availability: High

Weakness

CWE-94: Improper Control of Generation of Code ('Code Injection')

The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.

How to prevent it

Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
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. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
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.
Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].

References

Advisories

Packages

github.com/nltk/nltk

Timeline

Feb 18, 2026 Published
Sep 10, 2026 Last updated by the source
Oct 31, 2026 Latest EPSS score

Advisory	Severity	Why it's related
GHSA-j456-xh4h-cpf2 NLTK: WekaClassifier passes an unvalidated caller-controlled model path to the weka JVM, bypassing nltk.pathsec containment	High	Also affects nltk
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

Is this in your code?

MergeHarbor checks every repository's dependencies against this database, ranks what matters by exploitability and reachability, routes it to the owning team and opens the fix pull request.

Check your repos with MergeHarbor