FKIE_CVE-2018-18585
Vulnerability from fkie_nvd - Published: 2018-10-23 02:29 - Updated: 2026-06-17 01:47
Severity
Summary
chmd_read_headers in mspack/chmd.c in libmspack before 0.8alpha accepts a filename that has '\0' as its first or second character (such as the "/\0" name).
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| kyzer | libmspack | 0.3 | |
| kyzer | libmspack | 0.4 | |
| kyzer | libmspack | 0.5 | |
| kyzer | libmspack | 0.6 | |
| kyzer | libmspack | 0.7 | |
| debian | debian_linux | 8.0 | |
| redhat | enterprise_linux_desktop | 7.0 | |
| redhat | enterprise_linux_server | 7.0 | |
| redhat | enterprise_linux_workstation | 7.0 | |
| canonical | ubuntu_linux | 12.04 | |
| canonical | ubuntu_linux | 14.04 | |
| canonical | ubuntu_linux | 16.04 | |
| canonical | ubuntu_linux | 18.04 | |
| canonical | ubuntu_linux | 18.10 | |
| suse | linux_enterprise_server | 11 | |
| suse | linux_enterprise_server | 12 | |
| suse | linux_enterprise_server | 12 | |
| suse | linux_enterprise_server | 12 | |
| starwindsoftware | starwind_virtual_san | - |
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"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "chmd_read_headers in mspack/chmd.c in libmspack before 0.8alpha accepts a filename that has \u0027\\0\u0027 as its first or second character (such as the \"/\\0\" name)."
},
{
"lang": "es",
"value": "chmd_read_headers en mspack/chmd.c en libmspack en versiones anteriores a la 0.8alpha acepta un nombre de archivo que tiene \"\\0\" como su primer o segundo car\u00e1cter (como el nombre \"/\\0\")."
}
],
"id": "CVE-2018-18585",
"lastModified": "2026-06-17T01:47:33.020",
"metrics": {
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"baseSeverity": "MEDIUM",
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"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 4.3,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:M/Au:N/C:N/I:N/A:P",
"version": "2.0"
},
"exploitabilityScore": 8.6,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
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"published": "2018-10-23T02:29:00.513",
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"tags": [
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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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