CVE-2026-3259 (GCVE-0-2026-3259)
Vulnerability from cvelistv5 – Published: 2026-04-23 08:35 – Updated: 2026-04-30 15:21
VLAI
EPSS
VEX
Title
Sensitive Data Disclosure in BigQuery via Materialized View Error Messages
Summary
A Generation of Error Message Containing Sensitive Information vulnerability in the Materialized View Refresh mechanism in Google BigQuery on Google Cloud Platform allows an authenticated user to potentially disclose sensitive data using a crafted materialized view that triggers a runtime error during the refresh process.
This vulnerability was patched on 29 January 2026, and no customer action is needed.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-04-23 12:31 UTC
CWE
- CWE-209 - Generation of error message containing sensitive information
Assigner
References
1 reference
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Google Cloud | BigQuery |
Affected:
0 , < 01/29/2026
(date)
|
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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