Vulnerability-Lookup

CVE-2026-15643 (GCVE-0-2026-15643)

Vulnerability from cvelistv5 – Published: 2026-07-14 20:03 – Updated: 2026-07-15 14:22

Title

AWS HealthLake MCP Server SSRF via Pagination URL

Summary

AWS HealthLake MCP Server (awslabs.healthlake-mcp-server) is a Model Context Protocol server that enables AI assistants to interact with AWS HealthLake FHIR datastores. A server-side request forgery in the pagination handling component in AWS awslabs.healthlake-mcp-server before 0.0.14 on all platforms might allow a remote authenticated user to exfiltrate AWS temporary security credentials to an arbitrary endpoint via a crafted next_token parameter. The server does not validate that pagination URLs point back to the expected HealthLake endpoint, allowing an actor to redirect subsequent requests to an actor-controlled server. Its recommended to upgrade to version 0.0.14 or later.

Severity

9.2 (Critical)


                        
                          CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:A/VC:H/VI:H/VA:N/SC:H/SI:N/SA:N

7.3 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:N

SSVC

Exploitation: none Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-07-15 14:21 UTC

CWE

CWE-918 - Server-Side request forgery (SSRF)

Assigner

AMZN mitre root

References

2 references

URL	Tags
https://pypi.org/project/awslabs.healthlake-mcp-s…	release-notes
https://aws.amazon.com/security/security-bulletin…	vendor-advisory

Impacted products

1 product

Vendor	Product	Version	CPE status
AWS	awslabs.healthlake-mcp-server	Affected: 0 , < 0.0.14 (custom) cpe:2.3:a:aws:awslabs.healthlake-mcp-server::::::::

Show details on NVD website

JSON

To clipboard

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}

FKIE_CVE-2026-15643

Vulnerability from fkie_nvd - Published: 2026-07-14 21:16 - Updated: 2026-07-15 16:17

Severity

7.3 (High) - CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:N

Summary

References

	URL	Tags
	ff89ba41-3aa1-4d27-914a-91399e9639e5	https://aws.amazon.com/security/security-bulletins/2026-054-aws/
	ff89ba41-3aa1-4d27-914a-91399e9639e5	https://pypi.org/project/awslabs.healthlake-mcp-server/0.0.14/

Impacted products

	Vendor	Product	Version

JSON

To clipboard

{
  "affected": [
    {
      "affectedData": [
        {
          "defaultStatus": "unaffected",
          "product": "awslabs.healthlake-mcp-server",
          "vendor": "AWS",
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      ],
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    }
  ],
  "id": "CVE-2026-15643",
  "lastModified": "2026-07-15T16:17:47.050",
  "metrics": {
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        "cvssData": {
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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.

Detection rules are retrieved from Rulezet.

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.

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.