CveMateVulnerability intelligence

Nltk/Nltk vulnerabilities

10 vulnerabilities

Known vulnerabilities in Nltk/Nltk, newest first, with CVSS severity, EPSS, KEV listings, ransomware use and the affected versions.

CVE-2021-3828debiannltk+75y ago27-09-2021 12:25

nltk is vulnerable to Inefficient Regular Expression Complexity CVE-2021-3842debiannltk+64y ago04-01-2022 14:50

nltk is vulnerable to Inefficient Regular Expression Complexity CVE-2025-14009chainguardapache-beam-python-3.11-sdk+188mo ago18-02-2026 17:45

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.CVE-2026-0847debiannltk+67mo ago04-03-2026 18:25

A vulnerability in NLTK versions up to and including 3.9.2 allows arbitrary file read via path traversal in multiple CorpusReader classes, including WordListCorpusReader, TaggedCorpusReader, and BracketParseCorpusReader. These classes fail to properly sanitize or validate file paths, enabling attackers to traverse directories and access sensitive files on the server. This issue is particularly critical in scenarios where user-controlled file inputs are processed, such as in machine learning APIs, chatbots, or NLP pipelines. Exploitation of this vulnerability can lead to unauthorized access to sensitive files, including system files, SSH private keys, and API tokens, and may potentially escalate to remote code execution when combined with other vulnerabilities.CVE-2026-0848debiannltk+47mo ago05-03-2026 20:48

NLTK versions <=3.9.2 are vulnerable to arbitrary code execution due to improper input validation in the StanfordSegmenter module. The module dynamically loads external Java .jar files without verification or sandboxing. An attacker can supply or replace the JAR file, enabling the execution of arbitrary Java bytecode at import time. This vulnerability can be exploited through methods such as model poisoning, MITM attacks, or dependency poisoning, leading to remote code execution. The issue arises from the direct execution of the JAR file via subprocess with unvalidated classpath input, allowing malicious classes to execute when loaded by the JVM.CVE-2026-0846debiannltk+47mo ago09-03-2026 19:19

A vulnerability in the `filestring()` function of the `nltk.util` module in nltk version 3.9.2 allows arbitrary file read due to improper validation of input paths. The function directly opens files specified by user input without sanitization, enabling attackers to access sensitive system files by providing absolute paths or traversal paths. This vulnerability can be exploited locally or remotely, particularly in scenarios where the function is used in web APIs or other interfaces that accept user-supplied input.CVE-2026-12372debiannltk+52mo ago09-08-2026 22:45

A Server-Side Request Forgery (SSRF) vulnerability exists in nltk/nltk versions 3.9.4 and the current develop branch. The `nltk.pathsec.validate_network_url()` function, intended to prevent SSRF by rejecting internal network addresses, fails to reject IPs in the RFC 6598 shared address space (`100.64.0.0/10`). This occurs because Python's `ipaddress` module does not classify such addresses as `is_private` or `is_global`, and the current guard only checks `is_private` and a few explicit categories. An attacker who can influence a URL passed to NLTK's network-loading helpers can exploit this vulnerability to make a strict-mode application send requests to shared-address-space hosts, potentially exposing non-public infrastructure reachable from the application host. The impact is limited to SSRF-style confidentiality exposure, with no code execution claimed.CVE-2026-12199debiannltk+24mo ago17-06-2026 07:13

A vulnerability in `nltk.app.wordnet_app` up to version 3.9.3 allows unauthenticated remote shutdown of the local WordNet Browser HTTP server when started in its default mode. The server listens on all interfaces and processes a specific unauthenticated GET request (`/SHUTDOWN%20THE%20SERVER`) to terminate the process immediately via `os._exit(0)`. This results in a denial of service, impacting service availability. The issue arises due to insufficient authentication and protection mechanisms for critical server functions.CVE-2026-12252debiannltk+43mo ago04-07-2026 00:58

In nltk/nltk versions 3.9.3 and earlier, five Stanford interface classes (StanfordPOSTagger, StanfordNERTagger, StanfordParser, StanfordDependencyParser, and StanfordNeuralDependencyParser) are vulnerable to untrusted JAR code execution. These classes accept user-controllable JAR paths and execute them via the `java()` function, which invokes `subprocess.Popen()` without integrity verification. This vulnerability is identical to CVE-2026-0848, which was fixed for StanfordSegmenter by adding SHA256 verification. However, the fix was not applied to these additional classes, leaving them susceptible to arbitrary code execution when loading untrusted JAR files.CVE-2026-12261debiannltk+42mo ago07-08-2026 06:25

A vulnerability in `nltk.downloader` in nltk/nltk versions <= 3.9.4 allows for cross-package resource and model poisoning. The downloader extracts package archives into shared namespaces such as `corpora/` and `taggers/` instead of package-isolated roots, and validates package integrity only after the archive has been written and extracted. This design flaw enables one package to overwrite another package's trusted resources within the same namespace, making the changes immediately active through ordinary NLTK APIs. This issue persists across fresh interpreter restarts and can affect downstream workflows, including machine learning pipelines and reproducibility-sensitive environments.