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CVE-2026-85226UNKNOWNMITRENVD대응게시일: 2026. 09. 03.수정일: 2026. 09. 03.CNA: 5a6e4751-2f3f-4070-9419-94fb35b644e8Undergoing Analysis

MISP contains an authorization flaw in the OnDemand correlation engine where correlations were calculated solely from matching attribute val

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위협 신호 · CVSS · EPSS · KEV

정기 패치· 높은 악용 신호 없음
CVSS
unknown

이론적 심각도 점수

EPSS
0.2%상위 86.2%

30일 내 악용 확률 예측

KEV
미등재

실측 악용 기록 없음

권장 대응 기한60일 이내CISA SSVC 기준

계획된 패치 주기 내 조치(60일 이내)

외부 노출· KEV 미등재 · 자동화 어려움 · 부분 영향 · 외부 노출

CVSS 벡터 · 메트릭

버전별 점수
CVSS 4.05.3MEDIUM· 악용성숙도 NOT_DEFINED

CVSS 벡터 정보 없음

약점 (CWE)

  • CWE-862

    권한 검사 누락 — 접근 권한 확인 없이 기능/자원에 접근 허용.

상세 설명

MISP contains an authorization flaw in the OnDemand correlation engine where correlations were calculated solely from matching attribute values without applying the distribution, sharing group, organization, or other access-control restrictions associated with the correlated attributes and events. As a result, an authenticated user could receive correlation results referring to attributes or events that the user was not authorized to access.

The vulnerable correlation collection path did not take the requesting user into account. The patch changes the correlation collector to accept the current user and filters the resulting attribute identifiers through MISP's existing fetchAttributesSimple() authorization logic, which evaluates event-, attribute-, object-, distribution-, and sharing-group-level restrictions against the live data.

The issue also affected paths relying on previously stored correlation data. Because the OnDemand engine does not maintain the stored correlation table, its denormalized access-control information could be stale. The patch therefore validates correlated attribute identifiers against the current ACLs before returning them and additionally applies normal event visibility conditions when retrieving related events.

An authenticated low-privileged user could exploit this issue by querying or creating attributes that correlate with restricted MISP content, potentially learning information about otherwise inaccessible events or attributes.

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