GHSA-5MG7-485Q-XM76
Vulnerability from github – Published: 2026-03-25 14:25 – Updated: 2026-03-27 00:32
VLAI
Summary
Two LiteLLM versions published containing credential harvesting malware
Details
After an API Token exposure from an exploited trivy dependency, two new releases of litellm were uploaded to PyPI containing automatically activated malware, harvesting sensitive credentials and files, and exfiltrating to a remote API.
Anyone who has installed and run the project should assume any credentials available to litellm environment may have been exposed, and revoke/rotate thema ccordingly.
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "litellm"
},
"ranges": [
{
"events": [
{
"introduced": "1.82.7"
},
{
"last_affected": "1.82.8"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [],
"database_specific": {
"cwe_ids": [
"CWE-506"
],
"github_reviewed": true,
"github_reviewed_at": "2026-03-25T14:25:42Z",
"nvd_published_at": null,
"severity": "CRITICAL"
},
"details": "After an API Token exposure from an exploited trivy dependency, two new releases of `litellm` were uploaded to PyPI containing automatically activated malware, harvesting sensitive credentials and files, and exfiltrating to a remote API.\n\nAnyone who has installed and run the project should assume any credentials available to litellm environment may have been exposed, and revoke/rotate thema ccordingly.",
"id": "GHSA-5mg7-485q-xm76",
"modified": "2026-03-27T00:32:11Z",
"published": "2026-03-25T14:25:42Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/BerriAI/litellm/issues/24518"
},
{
"type": "WEB",
"url": "https://docs.litellm.ai/blog/security-update-march-2026"
},
{
"type": "WEB",
"url": "https://futuresearch.ai/blog/litellm-pypi-supply-chain-attack"
},
{
"type": "PACKAGE",
"url": "https://github.com/BerriAI/litellm"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/litellm/PYSEC-2026-2.yaml"
},
{
"type": "WEB",
"url": "https://inspector.pypi.io/project/litellm/1.82.7/packages/79/5f/b6998d42c6ccd32d36e12661f2734602e72a576d52a51f4245aef0b20b4d/litellm-1.82.7-py3-none-any.whl/litellm/proxy/proxy_server.py#line.130"
},
{
"type": "WEB",
"url": "https://inspector.pypi.io/project/litellm/1.82.8/packages/f6/2c/731b614e6cee0bca1e010a36fd381fba69ee836fe3cb6753ba23ef2b9601/litellm-1.82.8.tar.gz/litellm-1.82.8/litellm_init.pth#line.1"
},
{
"type": "WEB",
"url": "https://www.wiz.io/blog/teampcp-attack-kics-github-action"
}
],
"schema_version": "1.4.0",
"severity": [],
"summary": "Two LiteLLM versions published containing credential harvesting malware"
}
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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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- Confirmed: The vulnerability has been validated from an analyst's perspective.
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- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
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- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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