FKIE_CVE-2026-53937
Vulnerability from fkie_nvd - Published: 2026-09-09 00:17 - Updated: 2026-09-10 19:57
Severity
Summary
MCP Kotlin SDK is the Kotlin Multiplatform software development kit for the Model Context Protocol. In versions 0.7.0 through 0.12.0, `ReadBuffer.append` in `kotlin-sdk-core/src/commonMain/kotlin/io/modelcontextprotocol/kotlin/sdk/shared/ReadBuffer.kt` writes every chunk of bytes received from the stdio transport into a `kotlinx.io.Buffer` with no size cap. Frames are extracted from that buffer only when a `\n` (0x0a) byte is observed. A peer that streams bytes without ever sending a newline causes the internal buffer to grow indefinitely until the JVM (or the surrounding host process) is OOM-killed. The leak is amplified by `StdioServerTransport` and `StdioClientTransport`, which both queue raw chunks through a `kotlinx.coroutines.channels.Channel<ByteArray>(Channel.UNLIMITED)` and then call `readBuffer.append(chunk)` without backpressure or size guard. This is a remote-pre-auth denial of service whenever an SDK stdio server's stdin is fed by an untrusted or attacker-controlled producer (for example: a host program that exec's the MCP server as a subprocess and pipes through bytes received from a network peer, or a sidecar wrapper that proxies bytes from an HTTP endpoint to the stdio transport). Version 0.13.0 fixes the issue.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "io.modelcontextprotocol:kotlin-sdk",
"vendor": "modelcontextprotocol",
"versions": [
{
"status": "affected",
"version": "\u003c 0.13.0"
}
]
},
{
"product": "io.modelcontextprotocol:kotlin-sdk-client",
"vendor": "modelcontextprotocol",
"versions": [
{
"status": "affected",
"version": "\u003e= 0.7.0, \u003c 0.13.0"
}
]
},
{
"product": "io.modelcontextprotocol:kotlin-sdk-core",
"vendor": "modelcontextprotocol",
"versions": [
{
"status": "affected",
"version": "\u003c 0.13.0"
}
]
},
{
"product": "io.modelcontextprotocol:kotlin-sdk-server",
"vendor": "modelcontextprotocol",
"versions": [
{
"status": "affected",
"version": "\u003e= 0.7.0, \u003c 0.13.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "MCP Kotlin SDK is the Kotlin Multiplatform software development kit for the Model Context Protocol. In versions 0.7.0 through 0.12.0, `ReadBuffer.append` in `kotlin-sdk-core/src/commonMain/kotlin/io/modelcontextprotocol/kotlin/sdk/shared/ReadBuffer.kt` writes every chunk of bytes received from the stdio transport into a `kotlinx.io.Buffer` with no size cap. Frames are extracted from that buffer only when a `\\n` (0x0a) byte is observed. A peer that streams bytes without ever sending a newline causes the internal buffer to grow indefinitely until the JVM (or the surrounding host process) is OOM-killed. The leak is amplified by `StdioServerTransport` and `StdioClientTransport`, which both queue raw chunks through a `kotlinx.coroutines.channels.Channel\u003cByteArray\u003e(Channel.UNLIMITED)` and then call `readBuffer.append(chunk)` without backpressure or size guard. This is a remote-pre-auth denial of service whenever an SDK stdio server\u0027s stdin is fed by an untrusted or attacker-controlled producer (for example: a host program that exec\u0027s the MCP server as a subprocess and pipes through bytes received from a network peer, or a sidecar wrapper that proxies bytes from an HTTP endpoint to the stdio transport). Version 0.13.0 fixes the issue."
}
],
"id": "CVE-2026-53937",
"lastModified": "2026-09-10T19:57:48.533",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 6.2,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.5,
"impactScore": 3.6,
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
},
"published": "2026-09-09T00:17:31.407",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/modelcontextprotocol/kotlin-sdk/blob/6d5bac1/kotlin-sdk-core/src/commonMain/kotlin/io/modelcontextprotocol/kotlin/sdk/shared/ReadBuffer.kt"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/modelcontextprotocol/kotlin-sdk/commit/6e6f80512fb8fcc9f3c031cfd693ccbcf9c4aaab"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/modelcontextprotocol/kotlin-sdk/releases/tag/0.13.0"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/modelcontextprotocol/kotlin-sdk/security/advisories/GHSA-74gp-qhv5-v493"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Awaiting Analysis",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-400"
},
{
"lang": "en",
"value": "CWE-770"
}
],
"source": "security-advisories@github.com",
"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.
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.
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