brew-sgr-cve-2018-18482
Vulnerability from osv_homebrew
Published
2026-08-13 17:34
Modified
2026-09-10 01:12
Summary
libpg_query memory leak
Details
An issue was discovered in libpg_query 10-1.0.2. There is a memory leak in pg_query_raw_parse in pg_query_parse.c, which might lead to a denial of service.
Severity
References
{
"affected": [
{
"ecosystem_specific": {
"fix": "bump",
"range_state": "fixed",
"resource": "pglast",
"resource_purl": "pkg:pypi/pglast@7.7",
"upstream_fixed_in": "0.28"
},
"package": {
"ecosystem": "Homebrew",
"name": "sgr",
"purl": "pkg:brew/sgr"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.3.12_15"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"database_specific": {
"confidence": "high",
"source": "matched",
"strategy": "registry",
"upstream_evidence": [
{
"ecosystem": "PyPI",
"key": "pkg:pypi/pglast@7.7",
"name": "pglast",
"resource": "pglast",
"strategy": "registry",
"subject_version": "7.7"
}
]
},
"details": "An issue was discovered in libpg_query 10-1.0.2. There is a memory leak in pg_query_raw_parse in pg_query_parse.c, which might lead to a denial of service.",
"id": "BREW-sgr-CVE-2018-18482",
"modified": "2026-09-10T01:12:04Z",
"published": "2026-08-13T17:34:36Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2018-18482"
},
{
"type": "WEB",
"url": "https://github.com/lfittl/libpg_query/issues/49"
},
{
"type": "PACKAGE",
"url": "https://github.com/lelit/pglast"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/pg-query/PYSEC-2018-154.yaml"
}
],
"schema_version": "1.7.3",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "libpg_query memory leak",
"upstream": [
"GHSA-vm3q-58wm-2r2x",
"CVE-2018-18482",
"PYSEC-2018-154",
"PYSEC-2026-895"
]
}
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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
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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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