Vulnerability-Lookup

CVE-2026-87911 (GCVE-0-2026-87911)

Vulnerability from cvelistv5 – Published: 2026-09-09 19:35 – Updated: 2026-09-10 13:53

Title

Read-only enforcement bypass enabling operating system command execution in the SQL validation component of Amazon awslabs postgres-mcp-server

Summary

An OS command injection weakness in the read-only enforcement of the SQL validation component in Amazon awslabs postgres-mcp-server before 1.1.7 might allow an unauthenticated actor to execute operating system commands on the host of a self-managed PostgreSQL server by placing a crafted COPY ... TO PROGRAM statement into content that is processed when an authenticated user interacts with the MCP server in its default read-only mode. To remediate this issue, users should upgrade to version 1.1.7 or later.

Severity

9.6 (Critical)


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

9 (Critical)


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

SSVC

Exploitation: none Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-09-10 13:53 UTC

CWE

CWE-78 - Improper neutralization of special elements used in an OS command ('OS command injection')
CWE-184 - Incomplete list of disallowed inputs

Assigner

AMZN mitre root

References

3 references

URL	Tags
https://pypi.org/project/awslabs.postgres-mcp-ser…	patch
https://aws.amazon.com/security/security-bulletin…	vendor-advisory
https://github.com/awslabs/mcp/security/advisorie…	third-party-advisory

Impacted products

1 product

Vendor	Product	Version	CPE status
AWS	AWS Labs postgres MCP Server	Affected: 0 , < 1.1.7 (custom) cpe:2.3:a:aws:aws_labs_postgres_mcp_server::::::::

Credits

Corsen AI

Show details on NVD website

JSON

To clipboard

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FKIE_CVE-2026-87911

Vulnerability from fkie_nvd - Published: 2026-09-09 20:21 - Updated: 2026-09-10 15:53

Severity

9.6 (Critical) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H

Summary

References

	URL	Tags
	ff89ba41-3aa1-4d27-914a-91399e9639e5	https://aws.amazon.com/security/security-bulletins/2026-104-aws/
	ff89ba41-3aa1-4d27-914a-91399e9639e5	https://github.com/awslabs/mcp/security/advisories/GHSA-fph8-pg5w-78fv
	ff89ba41-3aa1-4d27-914a-91399e9639e5	https://pypi.org/project/awslabs.postgres-mcp-server/1.1.7/

Impacted products

	Vendor	Product	Version

JSON

To clipboard

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          "providerUrgency": "NOT_DEFINED",
          "subAvailabilityImpact": "HIGH",
          "subConfidentialityImpact": "HIGH",
          "subIntegrityImpact": "HIGH",
          "userInteraction": "PASSIVE",
          "valueDensity": "NOT_DEFINED",
          "vectorString": "CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:P/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
          "version": "4.0",
          "vulnAvailabilityImpact": "HIGH",
          "vulnConfidentialityImpact": "HIGH",
          "vulnIntegrityImpact": "HIGH",
          "vulnerabilityResponseEffort": "NOT_DEFINED"
        },
        "source": "ff89ba41-3aa1-4d27-914a-91399e9639e5",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2026-87911",
          "options": [
            {
              "exploitation": "none"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "total"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2026-09-10T13:53:17.650302Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2026-09-09T20:21:02.017",
  "references": [
    {
      "source": "ff89ba41-3aa1-4d27-914a-91399e9639e5",
      "url": "https://aws.amazon.com/security/security-bulletins/2026-104-aws/"
    },
    {
      "source": "ff89ba41-3aa1-4d27-914a-91399e9639e5",
      "url": "https://github.com/awslabs/mcp/security/advisories/GHSA-fph8-pg5w-78fv"
    },
    {
      "source": "ff89ba41-3aa1-4d27-914a-91399e9639e5",
      "url": "https://pypi.org/project/awslabs.postgres-mcp-server/1.1.7/"
    }
  ],
  "sourceIdentifier": "ff89ba41-3aa1-4d27-914a-91399e9639e5",
  "vulnStatus": "Deferred",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-78"
        },
        {
          "lang": "en",
          "value": "CWE-184"
        }
      ],
      "source": "ff89ba41-3aa1-4d27-914a-91399e9639e5",
      "type": "Secondary"
    }
  ]
}

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.