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VulnerabilityPublished

CVE-2026-115878: stanfordnlp stanza · Code Injection

Affects stanfordnlp stanzaSource CVE Database V5

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Not known to be exploited · CVSS 7.8 High · fixed in 1.15.0 : patch stanfordnlp stanza soon.

Data as of 3 Nov 2026, 22:58 UTC

CVSS 3.1 base score
7.8High
How severe the flaw is on the 0–10 CVSS scale.

Thin evidence

One scorer or none
CVSS scores by source
CNA (GitHub_M)CVSS 3.17.8
Patch
Available – fixed in 1.15.0
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 3 Nov 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 3 Nov 2026.

What to do

Affected software

ProductAffectedFixed in
stanfordnlpstanzaAffected
  • =1.14.0
Fixed in–
PyPIstanzapkg:pypi/stanzaOther threatsAffected
  • =1.14.0
Fixed in–

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

AI-suggested

Upgrade stanza to version 1.15.0 or later, where this vulnerability is fixed. Avoid loading untrusted or attacker-controlled lemmatizer checkpoint files. Since this is a code injection via unsafe deserialization, do not use untrusted inputs for lemma_model_path or related pipeline construction parameters.

Description

Stanza is a Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages. From 1.14.0 until 1.15.0, loading an attacker-supplied lemmatizer checkpoint can execute arbitrary code because stanza/models/lemma/trainer.py stores pos_dict as gzip-compressed pickle bytes and Trainer.load passes the decompressed stream to unrestricted pickle.loads. The outer torch.load call uses weights_only=True, but the nested bytes are treated as a primitive and are not inspected by that allowlist. The path is reachable during pipeline construction through a caller-influenced lemma_model_path, before inference begins. This issue is fixed in version 1.15.0.

References

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