A Server-Side Request Forgery (SSRF) vulnerability exists in nltk/nltk versions 3.9.4 and the current develop branch. The nltk.pathsec.validatenetworkurl() 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 isprivate or isglobal, and the current guard only checks isprivate 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.

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.

A critical vulnerability exists in the NLTK downloader component of nltk/nltk, affecting all versions. The unzipiter 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.

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.