mal-2026-17422
Vulnerability from ossf_malicious_packages
Published
2026-10-01 21:00
Modified
2026-10-01 21:00
Summary
Malicious code in shortneer (PyPI)
Details
-= Per source details. Do not edit below this line.=-
Source: kam193 (5e166ae1253c886885381cb5087f67a59a869cad6ece6f3ac40aefc6e463c27f)
When used, the package exfiltrates Chrome extension files (likely targeting cryptocurrency wallets) and sensitive Telegram files.
Category: MALICIOUS - The campaign has clearly malicious intent, like infostealers.
Campaign: 2026-09-sherpy
Reasons (based on the campaign):
-
infostealer
-
exfiltration-crypto
-
target:telegram
-
native-extension
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "shortneer"
},
"versions": [
"0.1.0"
]
}
],
"credits": [
{
"contact": [
"https://github.com/kam193",
"https://bad-packages.kam193.eu/"
],
"name": "Kamil Ma\u0144kowski (kam193)",
"type": "REPORTER"
}
],
"database_specific": {
"malicious-packages-origins": [
{
"id": "pypi/2026-09-sherpy/shortneer",
"import_time": "2026-10-01T21:42:15.070973594Z",
"modified_time": "2026-10-01T21:00:49.972775Z",
"sha256": "5e166ae1253c886885381cb5087f67a59a869cad6ece6f3ac40aefc6e463c27f",
"source": "kam193",
"versions": [
"0.1.0"
]
}
]
},
"details": "\n---\n_-= Per source details. Do not edit below this line.=-_\n\n## Source: kam193 (5e166ae1253c886885381cb5087f67a59a869cad6ece6f3ac40aefc6e463c27f)\nWhen used, the package exfiltrates Chrome extension files (likely targeting cryptocurrency wallets) and sensitive Telegram files.\n\n\n---\n\nCategory: MALICIOUS - The campaign has clearly malicious intent, like infostealers.\n\n\nCampaign: 2026-09-sherpy\n\n\nReasons (based on the campaign):\n\n\n - infostealer\n\n\n - exfiltration-crypto\n\n\n - target:telegram\n\n\n - native-extension\n",
"id": "MAL-2026-17422",
"modified": "2026-10-01T21:00:49Z",
"published": "2026-10-01T21:00:49Z",
"references": [
{
"type": "WEB",
"url": "https://bad-packages.kam193.eu/pypi/package/shortneer"
}
],
"schema_version": "1.7.4",
"summary": "Malicious code in shortneer (PyPI)"
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- 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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