CVE-2025-11012 (GCVE-0-2025-11012)
Vulnerability from cvelistv5 – Published: 2025-09-26 11:32 – Updated: 2025-09-26 12:13 X_Open Source
VLAI
EPSS
VEX
Title
BehaviorTree Diagnostic Message script_parser.cpp ParseScript stack-based overflow
Summary
A vulnerability was determined in BehaviorTree up to 4.7.0. This affects the function ParseScript of the file /src/script_parser.cpp of the component Diagnostic Message Handler. Executing manipulation of the argument error_msgs_buffer can lead to stack-based buffer overflow. The attack can only be executed locally. The exploit has been publicly disclosed and may be utilized. This patch is called cb6c7514efa628adb8180b58b4c9ccdebbe096e3. A patch should be applied to remediate this issue.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-09-26 12:12 UTC
Assigner
References
7 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.325955 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.325955 | signaturepermissions-required |
| https://vuldb.com/?submit.654074 | third-party-advisory |
| https://github.com/BehaviorTree/BehaviorTree.CPP/… | issue-tracking |
| https://github.com/BehaviorTree/BehaviorTree.CPP/… | issue-tracking |
| https://github.com/user-attachments/files/2225133… | exploit |
| https://github.com/BehaviorTree/BehaviorTree.CPP/… | patch |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| n/a | BehaviorTree |
Affected:
4.0
Affected: 4.1 Affected: 4.2 Affected: 4.3 Affected: 4.4 Affected: 4.5 Affected: 4.6 Affected: 4.7.0 |
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"title": "BehaviorTree Diagnostic Message script_parser.cpp ParseScript stack-based overflow"
}
},
"cveMetadata": {
"assignerOrgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"assignerShortName": "VulDB",
"cveId": "CVE-2025-11012",
"datePublished": "2025-09-26T11:32:12.970Z",
"dateReserved": "2025-09-26T06:34:47.466Z",
"dateUpdated": "2025-09-26T12:13:06.743Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.1"
}
}
}
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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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