CVE-2026-69098 (GCVE-0-2026-69098)
Vulnerability from cvelistv5 – Published: 2026-08-04 15:15 – Updated: 2026-08-05 14:54 X_Open Source
VLAI
EPSS
VEX
Title
kotaemon 0.12.0 Unauthenticated Remote Code Execution via Insecure Deserialization
Summary
kotaemon through 0.12.0 contains an insecure deserialization vulnerability in the check_connection endpoint that allows unauthenticated attackers to instantiate arbitrary Python classes by supplying crafted YAML/JSON input with a __type__ field. Attackers can exploit this to override the __type__ field with subprocess.check_output and arbitrary arguments, achieving remote code execution with application process privileges.
Severity
9.8 (Critical)
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-05 14:54 UTC
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/Cinnamon/kotaemon/issues/844 | technical-descriptionexploit |
| https://www.vulncheck.com/advisories/kotaemon-una… | third-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Cinnamon | kotaemon |
Affected:
0 , ≤ 0.12.0
(semver)
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Date Public
2026-06-29 00:00
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"exploit"
],
"url": "https://github.com/Cinnamon/kotaemon/issues/844"
},
{
"tags": [
"third-party-advisory"
],
"url": "https://www.vulncheck.com/advisories/kotaemon-unauthenticated-remote-code-execution-via-insecure-deserialization"
}
],
"tags": [
"x_open-source"
],
"title": "kotaemon 0.12.0 Unauthenticated Remote Code Execution via Insecure Deserialization",
"x_generator": {
"engine": "vulncheck-endgame"
}
}
},
"cveMetadata": {
"assignerOrgId": "83251b91-4cc7-4094-a5c7-464a1b83ea10",
"assignerShortName": "VulnCheck",
"cveId": "CVE-2026-69098",
"datePublished": "2026-08-04T15:15:06.018Z",
"dateReserved": "2026-08-03T10:44:14.336Z",
"dateUpdated": "2026-08-05T14:54:51.084Z",
"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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