CVE Intelligence
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CVE-2026-1727: Agentspace: sensitive information exposure via predictable Cloud Storage bucket names

The Agentspace service used predictable Google Cloud Storage bucket names for error logs and temporary staging during data imports from GCS and Cloud SQL. This predictability allowed an attacker to create those buckets before a customer's first use, a pattern the record calls bucket squatting. The record states that versions after December 12, 2025 include the update and that no user action is required.

Published Oct 13, 2026 · Updated Oct 13, 2026 · Ler em português

Overview

The record describes a weakness in the Agentspace service in which predictable Google Cloud Storage bucket names were used for error logs and temporary staging during data imports from GCS and Cloud SQL. Because the names could be anticipated, a third party could create the buckets before the customer did, which the record calls bucket squatting. The cited reference is the Gemini Enterprise release notes for February 6, 2026, which the record links to this CVE.

Impact

  • The weakness is classified as CWE-200, Exposure of Sensitive Information.
  • The CNA assigned a CVSS 4.0 base score of 9.1 with network attack vector, low attack complexity, no privileges required and no user interaction. The vector indicates high confidentiality and integrity impact on the vulnerable system.
  • The record does not specify which data types were logged or staged in the affected buckets.

Affected products and versions

  • The record does not list affected products or version ranges in structured form, so the affected Agentspace versions are unknown from the sources consulted.
  • The record states that versions after December 12, 2025 have been updated.
  • Whether earlier versions were affected is inferred from that date but is not explicitly stated.

Exploitation status

  • The record shows this CVE is not listed in the CISA KEV catalog.
  • No confirmed exploitation in the wild appears in the sources consulted.
  • The record gives an EPSS probability of 0.00293 and a percentile of 0.2033.

Remediation and mitigation

  • The record states that no user action is required for versions updated after December 12, 2025.
  • Confirm through the vendor release notes that your environment runs a current release.
  • Review which Cloud Storage buckets the service uses for imports and confirm that your organization owns and controls them.
  • Review whether logs or staged files in those buckets contain data that needs additional protection or retention controls.

Detection guidance

  • Inventory the Cloud Storage buckets associated with the service and confirm each one belongs to your organization.
  • Monitor for bucket creation events with names that match the service's import or logging workflows and that you did not create.
  • Review access logs for buckets used by the service for unexpected access patterns.

Limitations of the available information

  • The record has no title, no affected version list, no fixed version identifiers and no patch identifiers.
  • The only vendor reference is the release notes page, so the exact technical scope of the change is not described.
  • The record gives a CNA CVSS score and no separate NVD score.
  • The EPSS date in the record (2026-10-13) is later than the publication date (2026-02-06), so the EPSS values should be verified before use.

Frequently asked questions

What is the core weakness in CVE-2026-1727?

The Agentspace service used predictable Google Cloud Storage bucket names for error logs and temporary staging during data imports, which allowed a third party to create those buckets first.

Does this CVE require action from users?

The record states that versions after December 12, 2025 include the update and that no user action is required. Confirming your version against the vendor release notes is still advisable.

Is this CVE known to be actively exploited?

The record shows it is not listed in the CISA KEV catalog, and no confirmed exploitation in the wild appears in the sources consulted.

This analysis was drafted with AI assistance from verified source data and automatically checked against it.