NLTK 3.9.4 through 3.10.2 contains a path traversal vulnerability in CrubadanCorpusReader. loadlangngrams joins the corpus root with crubadancode, the column-0 value read from the corpus table.txt mapping file, and opens the result with the builtin open() rather than the pathsec-validated opener, so os.path.join discards the root when that value is absolute and the read escapes the corpus directory without the containment check nltk.pathsec applies when ENFORCE is set. An attacker who controls a corpus package can disclose file contents outside the corpus root through langfreq, limited to paths ending in -3grams.txt whose contents parse as token count lines.

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.