mal-2026-14590
Vulnerability from ossf_malicious_packages
Published
2026-08-28 10:01
Modified
2026-08-28 10:01
Summary
Malicious code in yamlformatter-utils (PyPI)
Details
-= Per source details. Do not edit below this line.=-
Source: kam193 (ef2cbf7d7c360d11860ea9383a1ed84b491e75361f994359fd97284a5d25cdc2)
During import, the package collects sensitive information and exfiltrates it using DNS queries.
Category: MALICIOUS - The campaign has clearly malicious intent, like infostealers.
Campaign: 2026-08-ekx-report-utils
Reasons (based on the campaign):
-
targetted-attack
-
exfiltration-generic
-
exfiltration-credentials
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "yamlformatter-utils"
},
"versions": [
"1.0.0"
]
}
],
"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/2026-08-ekx-report-utils/yamlformatter-utils",
"import_time": "2026-08-28T10:38:59.567163801Z",
"modified_time": "2026-08-28T10:01:50.203858Z",
"sha256": "ef2cbf7d7c360d11860ea9383a1ed84b491e75361f994359fd97284a5d25cdc2",
"source": "kam193",
"versions": [
"1.0.0"
]
}
]
},
"details": "\n---\n_-= Per source details. Do not edit below this line.=-_\n\n## Source: kam193 (ef2cbf7d7c360d11860ea9383a1ed84b491e75361f994359fd97284a5d25cdc2)\nDuring import, the package collects sensitive information and exfiltrates it using DNS queries.\n\n\n---\n\nCategory: MALICIOUS - The campaign has clearly malicious intent, like infostealers.\n\n\nCampaign: 2026-08-ekx-report-utils\n\n\nReasons (based on the campaign):\n\n\n - targetted-attack\n\n\n - exfiltration-generic\n\n\n - exfiltration-credentials\n",
"id": "MAL-2026-14590",
"modified": "2026-08-28T10:01:50Z",
"published": "2026-08-28T10:01:36Z",
"references": [
{
"type": "WEB",
"url": "https://bad-packages.kam193.eu/pypi/package/yamlformatter-utils"
}
],
"schema_version": "1.7.4",
"summary": "Malicious code in yamlformatter-utils (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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