GSD-2021-37381
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
Southsoft GMIS 5.0 is vulnerable to CSRF attacks. Attackers can access other users' private information such as photos through CSRF. For example: any student's photo information can be accessed through /gmis/(S([1]))/student/grgl/PotoImageShow/?bh=[2]. Among them, the code in [1] is a random string generated according to the user's login related information. It can protect the user's identity, but it can not effectively prevent unauthorized access. The code in [2] is the student number of any student. The attacker can carry out CSRF attack on the system by modifying [2] without modifying [1].
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-37381",
"description": "Southsoft GMIS 5.0 is vulnerable to CSRF attacks. Attackers can access other users\u0027 private information such as photos through CSRF. For example: any student\u0027s photo information can be accessed through /gmis/(S([1]))/student/grgl/PotoImageShow/?bh=[2]. Among them, the code in [1] is a random string generated according to the user\u0027s login related information. It can protect the user\u0027s identity, but it can not effectively prevent unauthorized access. The code in [2] is the student number of any student. The attacker can carry out CSRF attack on the system by modifying [2] without modifying [1].",
"id": "GSD-2021-37381"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
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],
"details": "Southsoft GMIS 5.0 is vulnerable to CSRF attacks. Attackers can access other users\u0027 private information such as photos through CSRF. For example: any student\u0027s photo information can be accessed through /gmis/(S([1]))/student/grgl/PotoImageShow/?bh=[2]. Among them, the code in [1] is a random string generated according to the user\u0027s login related information. It can protect the user\u0027s identity, but it can not effectively prevent unauthorized access. The code in [2] is the student number of any student. The attacker can carry out CSRF attack on the system by modifying [2] without modifying [1].",
"id": "GSD-2021-37381",
"modified": "2023-12-13T01:23:09.462172Z",
"schema_version": "1.4.0"
}
},
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"affects": {
"vendor": {
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"value": "Southsoft GMIS 5.0 is vulnerable to CSRF attacks. Attackers can access other users\u0027 private information such as photos through CSRF. For example: any student\u0027s photo information can be accessed through /gmis/(S([1]))/student/grgl/PotoImageShow/?bh=[2]. Among them, the code in [1] is a random string generated according to the user\u0027s login related information. It can protect the user\u0027s identity, but it can not effectively prevent unauthorized access. The code in [2] is the student number of any student. The attacker can carry out CSRF attack on the system by modifying [2] without modifying [1]."
}
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"name": "https://github.com/caiteli/poc_information/blob/main/southsoft_GMIS.txt",
"refsource": "MISC",
"url": "https://github.com/caiteli/poc_information/blob/main/southsoft_GMIS.txt"
},
{
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"description": {
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"value": "Southsoft GMIS 5.0 is vulnerable to CSRF attacks. Attackers can access other users\u0027 private information such as photos through CSRF. For example: any student\u0027s photo information can be accessed through /gmis/(S([1]))/student/grgl/PotoImageShow/?bh=[2]. Among them, the code in [1] is a random string generated according to the user\u0027s login related information. It can protect the user\u0027s identity, but it can not effectively prevent unauthorized access. The code in [2] is the student number of any student. The attacker can carry out CSRF attack on the system by modifying [2] without modifying [1]."
}
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"impact": {
"baseMetricV2": {
"acInsufInfo": false,
"cvssV2": {
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"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 6.8,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:M/Au:N/C:P/I:P/A:P",
"version": "2.0"
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"exploitabilityScore": 8.6,
"impactScore": 6.4,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
"userInteractionRequired": true
},
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"attackVector": "NETWORK",
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"baseScore": 8.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.1"
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"exploitabilityScore": 2.8,
"impactScore": 5.9
}
},
"lastModifiedDate": "2021-08-13T18:42Z",
"publishedDate": "2021-08-06T13:15Z"
}
}
}
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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.
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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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