FKIE_CVE-2026-102824
Vulnerability from fkie_nvd - Published: 2026-09-29 19:17 - Updated: 2026-09-30 21:17
Severity
Summary
Russh is a Rust SSH client and server library. Prior to 0.63.0, the hybrid ML-KEM 768 and X25519 implementation in russh/src/kex/hybrid_mlkem.rs accepts an all-zero 32-byte peer X25519 public key in both server_dh and compute_shared_secret, forcing the X25519 contribution to the combined shared secret to zero. A malicious SSH peer can therefore make the combined secret depend only on ML-KEM, defeating the hybrid exchange's intended fallback protection if ML-KEM is later weakened. This issue is fixed in version 0.63.0.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "russh",
"vendor": "Eugeny",
"versions": [
{
"status": "affected",
"version": "\u003c 0.63.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Russh is a Rust SSH client and server library. Prior to 0.63.0, the hybrid ML-KEM 768 and X25519 implementation in russh/src/kex/hybrid_mlkem.rs accepts an all-zero 32-byte peer X25519 public key in both server_dh and compute_shared_secret, forcing the X25519 contribution to the combined shared secret to zero. A malicious SSH peer can therefore make the combined secret depend only on ML-KEM, defeating the hybrid exchange\u0027s intended fallback protection if ML-KEM is later weakened. This issue is fixed in version 0.63.0."
}
],
"id": "CVE-2026-102824",
"lastModified": "2026-09-30T21:17:05.920",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 4.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 1.4,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-102824",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-09-30T20:24:54.936526Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-09-29T19:17:24.543",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/Eugeny/russh/commit/8da8967f196472576b1565d518a0ed60fce60f0c"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/Eugeny/russh/releases/tag/v0.63.0"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/Eugeny/russh/security/advisories/GHSA-w3jg-pjxf-73p4"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Deferred",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-327"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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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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