CVE-2026-95660 (GCVE-0-2026-95660)
Vulnerability from cvelistv5 – Published: 2026-09-22 18:00 – Updated: 2026-09-22 19:05
VLAI
EPSS
VEX
Title
Moonshot AI Kimi Code MCP Configuration Loader config-loader.ts os command injection
Summary
A security flaw has been discovered in Moonshot AI Kimi Code up to 0.31.0. The affected element is an unknown function of the file agent-core-v2/src/agent/mcp/config-loader.ts of the component MCP Configuration Loader. The manipulation results in os command injection. The attack may be launched remotely. The exploit has been released to the public and may be used for attacks. Upgrading to version 0.31.1 is sufficient to fix this issue. It is recommended to upgrade the affected component. Beyond the trust prompt, the fix resolves fd/stty binaries to absolute paths specifically "so untrusted workspaces cannot plant bare-name executables before confirmation," fixing a secondary $PATH path-planting vector alongside the primary untrusted-.mcp.json auto-spawn.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-22 19:05 UTC
Assigner
References
7 references
| URL | Tags |
|---|---|
| https://vuldb.com/vuln/408415 | vdb-entry |
| https://vuldb.com/vuln/408415/cti | signaturepermissions-required |
| https://vuldb.com/cve/CVE-2026-95660 | third-party-advisory |
| https://vuldb.com/submit/897071 | third-party-advisory |
| https://docs.google.com/document/d/1GPDw0UzUkgfXR… | exploit |
| https://github.com/MoonshotAI/kimi-code/releases/… | patch |
| https://docs.google.com/document/d/1GPDw0UzUkgfXR… | exploit |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Moonshot AI | Kimi Code |
Affected:
0.1
Affected: 0.2 Affected: 0.3 Affected: 0.4 Affected: 0.5 Affected: 0.6 Affected: 0.7 Affected: 0.8 Affected: 0.9 Affected: 0.10 Affected: 0.11 Affected: 0.12 Affected: 0.13 Affected: 0.14 Affected: 0.15 Affected: 0.16 Affected: 0.17 Affected: 0.18 Affected: 0.19 Affected: 0.20 Affected: 0.21 Affected: 0.22 Affected: 0.23 Affected: 0.24 Affected: 0.25 Affected: 0.26 Affected: 0.27 Affected: 0.28 Affected: 0.29 Affected: 0.30 Affected: 0.31.0 Unaffected: 0.31.1 cpe:2.3:a:moonshot_ai:kimi_code:*:*:*:*:*:*:*:* |
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"version": "0.27"
},
{
"status": "affected",
"version": "0.28"
},
{
"status": "affected",
"version": "0.29"
},
{
"status": "affected",
"version": "0.30"
},
{
"status": "affected",
"version": "0.31.0"
},
{
"status": "unaffected",
"version": "0.31.1"
}
]
}
],
"credits": [
{
"lang": "en",
"type": "reporter",
"value": "38.5 (VulDB User)"
},
{
"lang": "en",
"type": "coordinator",
"value": "VulDB CNA Team"
}
],
"descriptions": [
{
"lang": "en",
"value": "A security flaw has been discovered in Moonshot AI Kimi Code up to 0.31.0. The affected element is an unknown function of the file agent-core-v2/src/agent/mcp/config-loader.ts of the component MCP Configuration Loader. The manipulation results in os command injection. The attack may be launched remotely. The exploit has been released to the public and may be used for attacks. Upgrading to version 0.31.1 is sufficient to fix this issue. It is recommended to upgrade the affected component. Beyond the trust prompt, the fix resolves fd/stty binaries to absolute paths specifically \"so untrusted workspaces cannot plant bare-name executables before confirmation,\" fixing a secondary $PATH path-planting vector alongside the primary untrusted-.mcp.json auto-spawn."
}
],
"metrics": [
{
"cvssV4_0": {
"baseScore": 5.3,
"baseSeverity": "MEDIUM",
"vectorString": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:P",
"version": "4.0"
}
},
{
"cvssV3_1": {
"baseScore": 6.3,
"baseSeverity": "MEDIUM",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:L/I:L/A:L/E:P/RL:O/RC:C",
"version": "3.1"
}
},
{
"cvssV3_0": {
"baseScore": 6.3,
"baseSeverity": "MEDIUM",
"vectorString": "CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:L/I:L/A:L/E:P/RL:O/RC:C",
"version": "3.0"
}
},
{
"cvssV2_0": {
"baseScore": 7.5,
"vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P/E:POC/RL:OF/RC:C",
"version": "2.0"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-78",
"description": "OS Command Injection",
"lang": "en",
"type": "CWE"
}
]
},
{
"descriptions": [
{
"cweId": "CWE-77",
"description": "Command Injection",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2026-09-22T18:00:11.243Z",
"orgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"shortName": "VulDB"
},
"references": [
{
"name": "VDB-408415 | Moonshot AI Kimi Code MCP Configuration Loader config-loader.ts os command injection",
"tags": [
"vdb-entry"
],
"url": "https://vuldb.com/vuln/408415"
},
{
"name": "VDB-408415 | CTI Indicators (IOB, IOC, TTP, IOA)",
"tags": [
"signature",
"permissions-required"
],
"url": "https://vuldb.com/vuln/408415/cti"
},
{
"name": "CVE-2026-95660 | CVE Analysis and Report",
"tags": [
"third-party-advisory"
],
"url": "https://vuldb.com/cve/CVE-2026-95660"
},
{
"name": "Submit #897071 | Moonshot AI Kimi Code 0.27.0 Code Injection",
"tags": [
"third-party-advisory"
],
"url": "https://vuldb.com/submit/897071"
},
{
"tags": [
"exploit"
],
"url": "https://docs.google.com/document/d/1GPDw0UzUkgfXRkWEDqCIanKDq3Tpvllyf08O-0eLyoQ/edit?usp=sharing"
},
{
"tags": [
"patch"
],
"url": "https://github.com/MoonshotAI/kimi-code/releases/tag/%40moonshot-ai%2Fkimi-code%400.31.1"
}
],
"timeline": [
{
"lang": "en",
"time": "2026-09-22T00:00:00.000Z",
"value": "Advisory disclosed"
},
{
"lang": "en",
"time": "2026-09-22T02:00:00.000Z",
"value": "VulDB entry created"
},
{
"lang": "en",
"time": "2026-09-22T14:47:46.000Z",
"value": "VulDB entry last update"
}
],
"title": "Moonshot AI Kimi Code MCP Configuration Loader config-loader.ts os command injection",
"x_generator": [
"VulDB PVTS v202609"
]
}
},
"cveMetadata": {
"assignerOrgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"assignerShortName": "VulDB",
"cveId": "CVE-2026-95660",
"datePublished": "2026-09-22T18:00:11.243Z",
"dateReserved": "2026-09-22T12:42:33.085Z",
"dateUpdated": "2026-09-22T19:05:35.649Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
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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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