CVE-2022-25878 (GCVE-0-2022-25878)
Vulnerability from cvelistv5 – Published: 2022-05-27 20:00 – Updated: 2024-09-17 02:36
VLAI
EPSS
VEX
Title
Prototype Pollution
Summary
The package protobufjs before 6.11.3 are vulnerable to Prototype Pollution which can allow an attacker to add/modify properties of the Object.prototype. This vulnerability can occur in multiple ways: 1. by providing untrusted user input to util.setProperty or to ReflectionObject.setParsedOption functions 2. by parsing/loading .proto files
Severity
CWE
- Prototype Pollution
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://snyk.io/vuln/SNYK-JS-PROTOBUFJS-2441248 | x_refsource_MISC |
| https://snyk.io/vuln/SNYK-JAVA-ORGWEBJARSNPM-2841507 | x_refsource_MISC |
| https://github.com/protobufjs/protobuf.js/blob/d1… | x_refsource_MISC |
| https://github.com/protobufjs/protobuf.js/commit/… | x_refsource_MISC |
| https://github.com/protobufjs/protobuf.js/pull/1731 | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| n/a | protobufjs |
Affected:
unspecified , < 6.11.3
(custom)
|
Date Public
2022-05-27 00:00
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"redhat_vex": {
"aggregate_severity": "Moderate",
"current_release_date": "2025-11-21T15:31:45+00:00",
"cve": "CVE-2022-25878",
"id": "CVE-2022-25878",
"initial_release_date": "2022-05-27T00:00:00+00:00",
"product_status:known_affected": "19",
"product_status:known_not_affected": "2",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "protobufjs: Prototype Pollution via util.setProperty or ReflectionObject.setParsedOption methods",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2022/cve-2022-25878.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "important",
"current_release_date": "2025-02-16T07:34:23Z",
"cve": "CVE-2022-25878",
"id": "CVE-2022-25878",
"initial_release_date": "2023-07-07T02:20:58Z",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2022-25878",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2022-25878.json",
"version": "5"
}
}
}
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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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