FKIE_CVE-2026-55165
Vulnerability from fkie_nvd - Published: 2026-08-18 19:16 - Updated: 2026-09-08 21:02
Severity
Summary
Lemur manages TLS certificate creation. Prior to 1.9.2, the JWT verifier in lemur/auth/service.py:130-137 used fetch_token_header to read header_data["alg"] from an unverified token and passed that attacker-controlled value to decode_with_multiple_secrets. PyJWT 2.x rejects alg=none with the configured key, so the flaw is a defense-in-depth gap rather than a direct authentication bypass in the shipped configuration. The unpinned algorithm can become exploitable after an asymmetric-signing migration through algorithm confusion, and it weakens algorithm-based anomaly detection because the token chooses the recorded value. A separate disclosure of LEMUR_TOKEN_SECRET would also permit forged HS256 tokens, although that disclosure is an independent prerequisite. The fix introduces the server-controlled LEMUR_TOKEN_ALGORITHMS allowlist and defaults it to HS256. This issue is fixed in version 1.9.2.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "lemur",
"vendor": "Netflix",
"versions": [
{
"status": "affected",
"version": "\u003c 1.9.2"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Lemur manages TLS certificate creation. Prior to 1.9.2, the JWT verifier in lemur/auth/service.py:130-137 used fetch_token_header to read header_data[\"alg\"] from an unverified token and passed that attacker-controlled value to decode_with_multiple_secrets. PyJWT 2.x rejects alg=none with the configured key, so the flaw is a defense-in-depth gap rather than a direct authentication bypass in the shipped configuration. The unpinned algorithm can become exploitable after an asymmetric-signing migration through algorithm confusion, and it weakens algorithm-based anomaly detection because the token chooses the recorded value. A separate disclosure of LEMUR_TOKEN_SECRET would also permit forged HS256 tokens, although that disclosure is an independent prerequisite. The fix introduces the server-controlled LEMUR_TOKEN_ALGORITHMS allowlist and defaults it to HS256. This issue is fixed in version 1.9.2."
}
],
"id": "CVE-2026-55165",
"lastModified": "2026-09-08T21:02:26.047",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 4.8,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.2,
"impactScore": 2.5,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-55165",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-08-18T19:35:36.998493Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-08-18T19:16:58.647",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/Netflix/lemur/commit/89898e665beed044cf7bd5927dfaa484e55bf242"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/Netflix/lemur/releases/tag/v1.9.2"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/Netflix/lemur/security/advisories/GHSA-r9gp-7f88-9r54"
},
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"url": "https://github.com/Netflix/lemur/security/advisories/GHSA-r9gp-7f88-9r54"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Deferred",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-347"
}
],
"source": "security-advisories@github.com",
"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
- 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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