CVE-2026-73497 (GCVE-0-2026-73497)
Vulnerability from cvelistv5 – Published: 2026-09-14 19:50 – Updated: 2026-09-15 13:56
VLAI
EPSS
VEX
Title
MCP Atlassian is a Model Context Protocol (MCP): DNS-rebinding TOCTOU bypass of the SSRF fix (CVE-2026-27826): unauthenticated SSRF to cloud metadata
Summary
MCP Atlassian is a Model Context Protocol (MCP) server for Atlassian products (Confluence and Jira). From 0.17.0 until 0.22.0, validate_url_for_ssrf resolves the attacker-controlled X-Atlassian-Jira-Url and X-Atlassian-Confluence-Url header host once at middleware time, but the outbound request is built with the raw hostname and resolves it again at connection time with no IP pinning. An attacker-controlled DNS-rebinding name can return a public IP during validation and 169.254.169.254 or another internal IP during connection, enabling unauthenticated server-side requests to cloud metadata or internal services. The flaw spans src/mcp_atlassian/utils/urls.py, src/mcp_atlassian/servers/main.py, and src/mcp_atlassian/servers/dependencies.py; validate_url_for_ssrf returns only a verdict rather than a pinned IP, UserTokenMiddleware processes the attacker-controlled headers before fetcher creation, and the Jira and Confluence fetchers use the raw hostname. This issue is fixed in version 0.22.0.
Severity
6.5 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-15 13:25 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/sooperset/mcp-atlassian/securi… | x_refsource_CONFIRM |
| https://github.com/sooperset/mcp-atlassian/pull/1448 | x_refsource_MISC |
| https://github.com/sooperset/mcp-atlassian/commit… | x_refsource_MISC |
| https://github.com/sooperset/mcp-atlassian/releas… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| sooperset | mcp-atlassian |
Affected:
>= 0.17.0, < 0.22.0
|
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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