Kafka Connect BigQuery Connector is an implementation of a sink connector from Apache Kafka to Google BigQuery
Description
Kafka Connect BigQuery Connector is an implementation of a sink connector from Apache Kafka to Google BigQuery
AI Analyst Comment
Remediation
Apply vendor security updates immediately. Monitor for exploitation attempts and review access logs.
Executive Summary:
A high-severity vulnerability has been discovered in the Google Kafka Connect BigQuery Connector, a component used for streaming data from Kafka to BigQuery. This flaw could allow a remote attacker to compromise the data pipeline, potentially leading to unauthorized data access, modification, or a denial of service. Organizations utilizing this connector are at significant risk of data breaches and operational disruption.
Vulnerability Details
CVE-ID: CVE-2026-23529
Affected Software: Google Multiple Products
Affected Versions: See vendor advisory for specific affected versions
Vulnerability: The vulnerability exists within the data processing logic of the Kafka Connect BigQuery Connector. An unauthenticated attacker with the ability to publish messages to a Kafka topic consumed by the connector can send a specially crafted message. Due to improper input validation when parsing message schemas or payloads, this message can trigger a remote code execution or arbitrary file read on the underlying server hosting the connector, granting the attacker control over the system or access to sensitive data.
Business Impact
This vulnerability is rated as High severity with a CVSS score of 7.7. Successful exploitation could have a severe business impact, including the exfiltration of sensitive data being processed through the Kafka-to-BigQuery pipeline, such as customer information, financial records, or proprietary business intelligence. Furthermore, an attacker could manipulate or corrupt data, compromising the integrity of analytics and reporting, or cause a denial of service by crashing the connector, disrupting critical business operations that rely on real-time data ingestion.
Remediation Plan
Immediate Action: Apply the security updates provided by Google to all affected instances of the Kafka Connect BigQuery Connector immediately. Prioritize patching for connectors that process sensitive or business-critical data. After patching, review system and application logs for any signs of compromise that may have occurred prior to remediation.
Proactive Monitoring: Monitor the logs of the Kafka Connect service for unusual errors, unexpected process executions, or anomalous outbound network connections from the connector's host. Implement alerting for malformed or suspicious-looking messages within the Kafka topics that feed the BigQuery sink connector.
Compensating Controls: If immediate patching is not feasible, implement network segmentation to strictly limit outbound network access from the host running the connector. Enhance access controls on the source Kafka topics to ensure that only trusted producers can write messages. Deploy an intrusion detection system (IDS) or web application firewall (WAF) if applicable to monitor traffic for known attack patterns.
Exploitation Status
Public Exploit Available: false
Analyst Notes: As of January 17, 2026, there are no known public proof-of-concept exploits or observed in-the-wild attacks targeting this vulnerability. However, due to the high severity and the critical role of this component in data infrastructure, it is highly probable that security researchers and threat actors will develop exploits in the near future.
Analyst Recommendation
Given the high severity (CVSS 7.7) of this vulnerability and its impact on a critical data pipeline component, we strongly recommend that organizations treat this as a high-priority issue. The potential for data exfiltration and operational disruption presents a significant risk. Although this CVE is not currently on the CISA KEV list, organizations should apply the vendor-supplied patches within their critical vulnerability patching window to prevent future exploitation.