CVE-2026-53937 (GCVE-0-2026-53937)
Vulnerability from cvelistv5 – Published: 2026-09-08 23:21 – Updated: 2026-09-14 12:09
VLAI
EPSS
VEX
Title
MCP Kotlin SDK's unbounded line buffer in StdioServerTransport/StdioClientTransport leads to memory exhaustion (DoS)
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.
Severity
6.2 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-14 12:09 UTC
CWE
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/modelcontextprotocol/kotlin-sd… | x_refsource_CONFIRM |
| https://github.com/modelcontextprotocol/kotlin-sd… | x_refsource_MISC |
| https://github.com/modelcontextprotocol/kotlin-sd… | x_refsource_MISC |
| https://github.com/modelcontextprotocol/kotlin-sd… | x_refsource_MISC |
Impacted products
4 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| modelcontextprotocol | io.modelcontextprotocol:kotlin-sdk |
Affected:
< 0.13.0
|
guessed | |
| modelcontextprotocol | io.modelcontextprotocol:kotlin-sdk-client |
Affected:
>= 0.7.0, < 0.13.0
|
guessed | |
| modelcontextprotocol | io.modelcontextprotocol:kotlin-sdk-core |
Affected:
< 0.13.0
|
guessed | |
| modelcontextprotocol | io.modelcontextprotocol:kotlin-sdk-server |
Affected:
>= 0.7.0, < 0.13.0
|
guessed |
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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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