FKIE_CVE-2026-103877
Vulnerability from fkie_nvd - Published: 2026-10-02 10:17 - Updated: 2026-10-02 10:17
Severity
Summary
Deserialization of Untrusted Data vulnerability in Apache Directory LDAP API.
A rogue/compromised LDAP server (or pre-TLS MITM) can answer a client's loadSchema() subschema search with a schema object that contains a serialized Java class, allowing some potential RCE.
This issue affects Apache Directory LDAP API: from 2.1.0 before 2.1.9.
Users are recommended to upgrade to version 2.1.9, which fixes the issue.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"collectionURL": "https://repo.maven.apache.org/maven2",
"defaultStatus": "unaffected",
"packageName": "org.apache.directory.api:api-ldap-schema-data",
"packageURL": "pkg:maven/org.apache.directory.api/api-ldap-schema-data",
"product": "Apache Directory LDAP API",
"vendor": "Apache Software Foundation",
"versions": [
{
"lessThan": "2.1.9",
"status": "affected",
"version": "2.1.0",
"versionType": "semver"
}
]
}
],
"source": "security@apache.org"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Deserialization of Untrusted Data vulnerability in Apache Directory LDAP API.\n\n\n\nA rogue/compromised LDAP server (or pre-TLS MITM) can answer a client\u0027s loadSchema() subschema search with a schema object that contains a serialized Java class, allowing some potential RCE.\u00a0\n\n\n\nThis issue affects Apache Directory LDAP API: from 2.1.0 before 2.1.9.\n\n\n\nUsers are recommended to upgrade to version 2.1.9, which fixes the issue."
}
],
"id": "CVE-2026-103877",
"lastModified": "2026-10-02T10:17:06.437",
"metrics": {},
"published": "2026-10-02T10:17:06.437",
"references": [
{
"source": "security@apache.org",
"url": "https://lists.apache.org/thread.html/sys8l881blqfgoc3o724jl32bmjl8pvw"
}
],
"sourceIdentifier": "security@apache.org",
"vulnStatus": "Received",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-502"
}
],
"source": "security@apache.org",
"type": "Primary"
}
]
}
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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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