CVE-2026-7866 (GCVE-0-2026-7866)
Vulnerability from cvelistv5 – Published: 2026-09-22 17:50 – Updated: 2026-09-22 19:05
VLAI
EPSS
VEX
Title
Stack-based Buffer Overflow vulnerability in RTI Connext Professional (Core Libraries) allows Overflow Buffers.
Summary
Stack-based Buffer Overflow vulnerability in RTI Connext Professional (Core Libraries) allows Overflow Buffers. This issue affects Connext Professional: from 7.4.0 before 7.7.0.1, from 7.0.0 before 7.3.1.6, from 6.1.0 before 6.1.*, from 6.0.0 before 6.0.*, from 5.3.0 before 5.3.*, from 5.2.0 before 5.2.*, from 4.3x before 5.1.*.
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-22 19:05 UTC
CWE
- CWE-121 - Stack-based Buffer Overflow
Assigner
References
1 reference
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| RTI | Connext Professional |
Affected:
7.4.0 , < 7.7.0.1
(custom)
Affected: 7.0.0 , < 7.3.1.6 (custom) Affected: 6.1.0 , < 6.1.* (custom) Affected: 6.0.0 , < 6.0.* (custom) Affected: 5.3.0 , < 5.3.* (custom) Affected: 5.2.0 , < 5.2.* (custom) Affected: 4.3x , < 5.1.* (custom) cpe:2.3:a:rti:connext_professional:*:*:*:*:*:*:*:* cpe:2.3:a:rti:connext_professional:*:*:*:*:*:*:*:* cpe:2.3:a:rti:connext_professional:*:*:*:*:*:*:*:* cpe:2.3:a:rti:connext_professional:*:*:*:*:*:*:*:* cpe:2.3:a:rti:connext_professional:*:*:*:*:*:*:*:* cpe:2.3:a:rti:connext_professional:*:*:*:*:*:*:*:* cpe:2.3:a:rti:connext_professional:*:*:*:*:*:*:*:* |
Date Public
2026-09-15 17:46
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"title": "Stack-based Buffer Overflow vulnerability in RTI Connext Professional (Core Libraries) allows Overflow Buffers.",
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"engine": "RTI Lubna 1.18.7"
}
}
},
"cveMetadata": {
"assignerOrgId": "3f572a00-62e2-4423-959a-7ea25eff1638",
"assignerShortName": "RTI",
"cveId": "CVE-2026-7866",
"datePublished": "2026-09-22T17:50:13.857Z",
"dateReserved": "2026-05-05T13:54:48.225Z",
"dateUpdated": "2026-09-22T19:05:51.079Z",
"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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