Skip to main content
VulnerabilityPublished Updated

CVE-2026-12261: Safety · NLTK downloader allows cross-package resource and model poisoning

Affects safetySource GCVE Database

0
Compare
Authoritative record:CVE-2026-12261· Medium 5.3

Not known to be exploited · CVSS 5.3 Medium : upgrade to 3.8.1_1 when convenient.

Data as of 30 Oct 2026, 07:30 UTC

CVSS 3.0 base score
5.3Medium
How severe the flaw is on the 0–10 CVSS scale.
Patch
Available – fixed in 3.8.1_1
CVSS:3.0/AV:N/AC:H/PR:N/UI:R/S:U/C:N/I:H/A:N

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 7 Aug 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 29 Oct 2026.

What to do

Upgrade safety to 3.8.1_1.

Affected software

ProductAffectedFixed in
Homebrewsafetypkg:brew/safetyOther threatsAffected
  • >=3.3.1 <3.8.1_1
Fixed in
  • 3.8.1_1

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

Description

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.

References

Community reviews

Loading reviews…