GSD-2022-27221
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
A vulnerability has been identified in SINEMA Remote Connect Server (All versions < V3.1). An attacker in machine-in-the-middle could obtain plaintext secret values by observing length differences during a series of guesses in which a string in an HTTP request URL potentially matches an unknown string in an HTTP response body, aka a "BREACH" attack.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-27221",
"description": "A vulnerability has been identified in SINEMA Remote Connect Server (All versions \u003c V3.1). An attacker in machine-in-the-middle could obtain plaintext secret values by observing length differences during a series of guesses in which a string in an HTTP request URL potentially matches an unknown string in an HTTP response body, aka a \"BREACH\" attack.",
"id": "GSD-2022-27221"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2022-27221"
],
"details": "A vulnerability has been identified in SINEMA Remote Connect Server (All versions \u003c V3.1). An attacker in machine-in-the-middle could obtain plaintext secret values by observing length differences during a series of guesses in which a string in an HTTP request URL potentially matches an unknown string in an HTTP response body, aka a \"BREACH\" attack.",
"id": "GSD-2022-27221",
"modified": "2023-12-13T01:19:41.389844Z",
"schema_version": "1.4.0"
}
},
"namespaces": {
"cve.org": {
"CVE_data_meta": {
"ASSIGNER": "productcert@siemens.com",
"ID": "CVE-2022-27221",
"STATE": "PUBLIC"
},
"affects": {
"vendor": {
"vendor_data": [
{
"product": {
"product_data": [
{
"product_name": "SINEMA Remote Connect Server",
"version": {
"version_data": [
{
"version_value": "All versions \u003c V3.1"
}
]
}
}
]
},
"vendor_name": "Siemens"
}
]
}
},
"data_format": "MITRE",
"data_type": "CVE",
"data_version": "4.0",
"description": {
"description_data": [
{
"lang": "eng",
"value": "A vulnerability has been identified in SINEMA Remote Connect Server (All versions \u003c V3.1). An attacker in machine-in-the-middle could obtain plaintext secret values by observing length differences during a series of guesses in which a string in an HTTP request URL potentially matches an unknown string in an HTTP response body, aka a \"BREACH\" attack."
}
]
},
"problemtype": {
"problemtype_data": [
{
"description": [
{
"lang": "eng",
"value": "CWE-310: Cryptographic Issues"
}
]
}
]
},
"references": {
"reference_data": [
{
"name": "https://cert-portal.siemens.com/productcert/pdf/ssa-484086.pdf",
"refsource": "MISC",
"url": "https://cert-portal.siemens.com/productcert/pdf/ssa-484086.pdf"
}
]
}
},
"nvd.nist.gov": {
"configurations": {
"CVE_data_version": "4.0",
"nodes": [
{
"children": [],
"cpe_match": [
{
"cpe23Uri": "cpe:2.3:a:siemens:sinema_remote_connect_server:*:*:*:*:*:*:*:*",
"cpe_name": [],
"versionEndExcluding": "3.1",
"vulnerable": true
}
],
"operator": "OR"
}
]
},
"cve": {
"CVE_data_meta": {
"ASSIGNER": "productcert@siemens.com",
"ID": "CVE-2022-27221"
},
"data_format": "MITRE",
"data_type": "CVE",
"data_version": "4.0",
"description": {
"description_data": [
{
"lang": "en",
"value": "A vulnerability has been identified in SINEMA Remote Connect Server (All versions \u003c V3.1). An attacker in machine-in-the-middle could obtain plaintext secret values by observing length differences during a series of guesses in which a string in an HTTP request URL potentially matches an unknown string in an HTTP response body, aka a \"BREACH\" attack."
}
]
},
"problemtype": {
"problemtype_data": [
{
"description": [
{
"lang": "en",
"value": "CWE-203"
}
]
}
]
},
"references": {
"reference_data": [
{
"name": "https://cert-portal.siemens.com/productcert/pdf/ssa-484086.pdf",
"refsource": "MISC",
"tags": [
"Patch",
"Vendor Advisory"
],
"url": "https://cert-portal.siemens.com/productcert/pdf/ssa-484086.pdf"
}
]
}
},
"impact": {
"baseMetricV2": {
"acInsufInfo": false,
"cvssV2": {
"accessComplexity": "MEDIUM",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 4.3,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:M/Au:N/C:P/I:N/A:N",
"version": "2.0"
},
"exploitabilityScore": 8.6,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
"userInteractionRequired": false
},
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 5.9,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:N/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.2,
"impactScore": 3.6
}
},
"lastModifiedDate": "2023-07-18T13:53Z",
"publishedDate": "2022-06-14T10:15Z"
}
}
}
Loading…
Loading…
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.
Loading…
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.
Loading…
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.
Loading…