FKIE_CVE-2026-84439
Vulnerability from fkie_nvd - Published: 2026-09-16 10:16 - Updated: 2026-09-17 20:18
Severity
Summary
When audit logging is enabled (zookeeper.audit.enable=true), an unauthenticated attacker can inject arbitrary fields into Apache ZooKeeper's audit log by sending a digest authentication request with tab characters (\t) embedded in the username. Because the audit log uses tab-separated key=value format, the injected tabs are parsed as legitimate field separators, allowing the attacker to spoof audit results (e.g., injecting result=success), forge operation types, and corrupt forensic evidence.
A log injection vulnerability in Apache ZooKeeper allows a client that can call setACL to inject forged key-value fields into zookeeper_audit.log. When audit logging is enabled, the server serializes attacker-controlled digest ACL ids into the acl= audit field without escaping tab characters. Because audit events are emitted as tab-separated key=value records, a crafted ACL id can make one successful setAcl event appear to contain forged fields such as operation=delete and znode=/forged. This undermines the integrity of downstream audit parsing, alerting, and incident response.
This issue affects Apache ZooKeeper: from 3.9.0 through 3.9.5, from 3.8.0 through 3.8.6.
Users are recommended to upgrade to version 3.9.6 or 3.8.7, which fixes the issue.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"collectionURL": "https://repo.maven.apache.org/maven2",
"defaultStatus": "unaffected",
"packageName": "org.apache.zookeeper:zookeeper",
"packageURL": "pkg:maven/org.apache.zookeeper/zookeeper",
"product": "Apache ZooKeeper",
"vendor": "Apache Software Foundation",
"versions": [
{
"lessThanOrEqual": "3.9.5",
"status": "affected",
"version": "3.9.0",
"versionType": "maven"
},
{
"lessThanOrEqual": "3.8.6",
"status": "affected",
"version": "3.8.0",
"versionType": "maven"
}
]
}
],
"source": "security@apache.org"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "When audit logging is enabled (zookeeper.audit.enable=true), an unauthenticated attacker can inject arbitrary fields into Apache ZooKeeper\u0027s audit log by sending a digest authentication request with tab characters (\\t) embedded in the username. Because the audit log uses tab-separated\u00a0key=value\u00a0format, the injected tabs are parsed as legitimate field separators, allowing the attacker to spoof audit results (e.g., injecting\u00a0result=success), forge operation types, and corrupt forensic evidence.\n\nA log injection vulnerability in Apache ZooKeeper allows a client that can call\u00a0setACL\u00a0to inject forged key-value fields into\u00a0zookeeper_audit.log. When audit logging is enabled, the server serializes attacker-controlled digest ACL ids into the\u00a0acl=\u00a0audit field without escaping tab characters. Because audit events are emitted as tab-separated\u00a0key=value\u00a0records, a crafted ACL id can make one successful\u00a0setAcl\u00a0event appear to contain forged fields such as\u00a0operation=delete\u00a0and\u00a0znode=/forged. This undermines the integrity of downstream audit parsing, alerting, and incident response.\n\nThis issue affects Apache ZooKeeper: from 3.9.0 through 3.9.5, from 3.8.0 through 3.8.6.\n\nUsers are recommended to upgrade to version 3.9.6 or 3.8.7, which fixes the issue."
}
],
"id": "CVE-2026-84439",
"lastModified": "2026-09-17T20:18:48.187",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 5.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 1.4,
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-84439",
"options": [
{
"exploitation": "none"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-09-17T19:14:36.442526Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-09-16T10:16:53.433",
"references": [
{
"source": "security@apache.org",
"url": "https://lists.apache.org/thread/b7qnjvqjh393l0j07tmnb5ggg40sx3m6"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "http://www.openwall.com/lists/oss-security/2026/09/15/6"
}
],
"sourceIdentifier": "security@apache.org",
"vulnStatus": "Undergoing Analysis",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-117"
}
],
"source": "security@apache.org",
"type": "Secondary"
}
]
}
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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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- 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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