CVE-2025-8713
PostgreSQL optimizer statistics allow a user to read sampled data within a view that the user cannot access. Separately, statistics allow a user to read sampled data that a row security policy intended to hide. PostgreSQL maintains statistics for tables by sampling data available in columns; this data is consulted during the query planning process. Prior to this release, a user could craft a leaky operator that bypassed view access control lists (ACLs) and bypassed row security policies in partitioning or table inheritance hierarchies.
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Reachable statistics data notably included histograms and most-common-values lists. CVE-2017-7484 and CVE-2019-10130 intended to close this class of vulnerability, but this gap remained. Versions before PostgreSQL 17.6, 16.10, 15.14, 14.19, and 13.22 are affected.
CVSS
- Versión: 3.1
- Vector: CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:N
- Puntuación base: 3.1
Probabilidad de explotación (EPSS)
- Probabilidad de explotación en los próximos 30 días: 0.24%
- Percentil entre todas las CVEs puntuadas: 13
- Fecha de la puntuación: 6/10/2026
EPSS (Exploit Prediction Scoring System, de FIRST) estima la probabilidad de que una vulnerabilidad sea explotada en 30 días. Complementa a CVSS (impacto) y a CISA KEV (explotación confirmada).
Tecnologías afectadas (1)
⚠ Inferidas por IA a partir de la descripción — NVD aún no ha analizado esta CVE; no son CPE verificados.
CWE
- CWE-1230
Referencias
JSON original (NVD)
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"id": "CVE-2025-8713",
"role": "CISA Coordinator",
"options": [
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"exploitation": "none"
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{
"automatable": "no"
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{
"technicalImpact": "partial"
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"version": "2.0.3",
"timestamp": "2025-08-14T19:50:52.127054Z"
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"vectorString": "CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:N",
"integrityImpact": "NONE",
"userInteraction": "NONE",
"attackComplexity": "HIGH",
"availabilityImpact": "NONE",
"privilegesRequired": "LOW",
"confidentialityImpact": "LOW"
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"affected": [
{
"source": "f86ef6dc-4d3a-42ad-8f28-e6d5547a5007",
"affectedData": [
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"vendor": "n/a",
"product": "PostgreSQL",
"versions": [
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"status": "affected",
"version": "17",
"lessThan": "17.6",
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"published": "2025-08-14T13:15:37.543",
"references": [
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"value": "PostgreSQL optimizer statistics allow a user to read sampled data within a view that the user cannot access. Separately, statistics allow a user to read sampled data that a row security policy intended to hide. PostgreSQL maintains statistics for tables by sampling data available in columns; this data is consulted during the query planning process. Prior to this release, a user could craft a leaky operator that bypassed view access control lists (ACLs) and bypassed row security policies in partitioning or table inheritance hierarchies. Reachable statistics data notably included histograms and most-common-values lists. CVE-2017-7484 and CVE-2019-10130 intended to close this class of vulnerability, but this gap remained. Versions before PostgreSQL 17.6, 16.10, 15.14, 14.19, and 13.22 are affected."
},
{
"lang": "es",
"value": "Las estadísticas del optimizador de PostgreSQL permiten al usuario leer datos muestreados dentro de una vista a la que no puede acceder. Por otro lado, las estadísticas permiten al usuario leer datos muestreados que una política de seguridad de filas pretendía ocultar. PostgreSQL mantiene las estadísticas de las tablas mediante el muestreo de los datos disponibles en las columnas; estos datos se consultan durante el proceso de planificación de consultas. Antes de esta versión, un usuario podía manipular un operador con fugas que omitía las listas de control de acceso (ACL) de las vistas y las políticas de seguridad de filas en las jerarquías de particionamiento o herencia de tablas. Los datos estadísticos accesibles incluían, en particular, histogramas y listas de valores más comunes. Las vulnerabilidades CVE-2017-7484 y CVE-2019-10130 pretendían subsanar esta clase de vulnerabilidad, pero esta brecha persistía. Las versiones anteriores a PostgreSQL 17.6, 16.10, 15.14, 14.19 y 13.22 están afectadas."
}
],
"lastModified": "2026-06-17T10:07:29.480",
"sourceIdentifier": "f86ef6dc-4d3a-42ad-8f28-e6d5547a5007"
}