Nltk/Nltk

In nltk version 3.9.4, the `nltk.downloader.Downloader._download_package()` function writes downloaded package bytes to disk and may extract them before enforcing SHA-256 or MD5 checksum validation. This allows an attacker to tamper with the package response body for `info.url` through a compromised mirror, malicious proxy, or other source-substitution condition, leading to the installation of attacker-controlled package bytes. The vulnerability can result in malicious corpus or model content being trusted by downstream users or applications.

Published: 2026-08-03T07:09:35.262Z
Updated: 2026-08-03T14:59:01.815Z

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.

Published: 2026-08-09T22:45:52.799Z
Updated: 2026-08-12T17:48:54.679Z

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.

Published: 2026-08-07T06:25:10.976Z
Updated: 2026-08-07T15:27:19.025Z

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.

Published: 2026-07-04T00:58:58.930Z
Updated: 2026-07-06T18:33:11.411Z

Published: 2026-06-30T00:14:35.370Z
Updated: 2026-08-18T08:33:26.727Z