GSD-2021-3129
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
Ignition before 2.5.2, as used in Laravel and other products, allows unauthenticated remote attackers to execute arbitrary code because of insecure usage of file_get_contents() and file_put_contents(). This is exploitable on sites using debug mode with Laravel before 8.4.2.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-3129",
"description": "Ignition before 2.5.2, as used in Laravel and other products, allows unauthenticated remote attackers to execute arbitrary code because of insecure usage of file_get_contents() and file_put_contents(). This is exploitable on sites using debug mode with Laravel before 8.4.2.",
"id": "GSD-2021-3129",
"references": [
"https://packetstormsecurity.com/files/cve/CVE-2021-3129"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2021-3129"
],
"details": "Ignition before 2.5.2, as used in Laravel and other products, allows unauthenticated remote attackers to execute arbitrary code because of insecure usage of file_get_contents() and file_put_contents(). This is exploitable on sites using debug mode with Laravel before 8.4.2.",
"id": "GSD-2021-3129",
"modified": "2023-12-13T01:23:35.030369Z",
"schema_version": "1.4.0"
}
},
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"affects": {
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}
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"name": "https://www.ambionics.io/blog/laravel-debug-rce",
"refsource": "MISC",
"url": "https://www.ambionics.io/blog/laravel-debug-rce"
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"name": "https://github.com/facade/ignition/pull/334",
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"url": "https://github.com/facade/ignition/pull/334"
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{
"name": "http://packetstormsecurity.com/files/162094/Ignition-2.5.1-Remote-Code-Execution.html",
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"url": "http://packetstormsecurity.com/files/162094/Ignition-2.5.1-Remote-Code-Execution.html"
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"url": "http://packetstormsecurity.com/files/165999/Ignition-Remote-Code-Execution.html"
}
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}
},
"gitlab.com": {
"advisories": [
{
"affected_range": "\u003c2.5.2",
"affected_versions": "All versions before 2.5.2",
"cvss_v2": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
"cvss_v3": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"cwe_ids": [
"CWE-1035",
"CWE-937"
],
"date": "2022-02-22",
"description": "Ignition, as used in Laravel and other products, allows unauthenticated remote attackers to execute arbitrary code because of insecure usage of `file_get_contents()` and `file_put_contents()`. This is exploitable on sites using debug mode with Laravel ",
"fixed_versions": [
"2.5.2"
],
"identifier": "CVE-2021-3129",
"identifiers": [
"CVE-2021-3129"
],
"not_impacted": "All versions starting from 2.5.2",
"package_slug": "packagist/facade/ignition",
"pubdate": "2021-01-12",
"solution": "Upgrade to version 2.5.2 or above.",
"title": "Code Injection",
"urls": [
"https://nvd.nist.gov/vuln/detail/CVE-2021-3129"
],
"uuid": "bf43803e-9ef3-4682-910e-125d95546465"
}
]
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"description": {
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{
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{
"name": "http://packetstormsecurity.com/files/162094/Ignition-2.5.1-Remote-Code-Execution.html",
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"url": "http://packetstormsecurity.com/files/165999/Ignition-Remote-Code-Execution.html"
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},
"impact": {
"baseMetricV2": {
"acInsufInfo": false,
"cvssV2": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 7.5,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 6.4,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "HIGH",
"userInteractionRequired": false
},
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 9.8,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 5.9
}
},
"lastModifiedDate": "2022-02-22T10:15Z",
"publishedDate": "2021-01-12T15:15Z"
}
}
}
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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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