CVE-2026-59304 in Spring Cloud Stream
Summary
by MITRE • 08/27/2026
Improper caching of the original content type in Spring Cloud Stream Avro. Spring Cloud Stream 5.0.0 - 5.0.2 Spring Cloud Stream 4.3.0 - 4.3.3 Spring Cloud Stream 4.2.0 - 4.2.6
VulDB is the best source for vulnerability data and more expert information about this specific topic.
Analysis
by VulDB Data Team • 08/27/2026
The vulnerability identified in Spring Cloud Stream versions ranging from 4.2.0 to 4.2.6, and from 4.3.0 to 4.3.3, as well as version 5.0.0 through 5.0.2, centers on an improper caching mechanism for the original content type during message serialization and deserialization processes involving Avro data formats. Spring Cloud Stream is a framework designed to build event-driven microservices that connect with external messaging systems using publish-subscribe semantics. When integrating with Apache Kafka or other brokers via Avro schemas, the framework must correctly interpret the schema registry metadata and map it to Java types for processing within the application context. The flaw arises when the library caches the content type information incorrectly, leading to a mismatch between the expected data structure defined by the Avro schema and the actual object instance created during runtime deserialization. This discrepancy typically manifests as an inability to properly parse incoming messages or results in corrupted state if the cached metadata is reused across different message contexts without proper validation of the underlying payload against the current schema definition.
From a technical perspective, this issue represents a failure in input validation and type safety enforcement within the serialization layer. The application relies on cached headers or context objects to optimize performance by avoiding repeated lookups of schema definitions from the registry. However, when multiple producers with different schemas publish messages to the same topic, or when schema evolution occurs over time, the stale cache entry causes the consumer to attempt deserialization using an outdated or incorrect type mapping. This can lead to ClassCastException errors in Java applications, data loss where fields are silently dropped due to mismatched field names or types, or more critically, unexpected behavior that bypasses logical checks expecting specific data structures. The root cause is tied to how the framework manages stateful components within a potentially concurrent environment without sufficient isolation of schema-specific context per message stream instance.
The operational impact of this vulnerability varies depending on the deployment architecture and the criticality of the data being processed. In high-throughput environments, the incorrect caching can lead to significant processing delays as messages fail deserialization and are routed to dead-letter queues or trigger application-level exceptions that require manual intervention. For systems relying on strict consistency guarantees, such as financial transactions or inventory management updates propagated via Avro-encoded Kafka topics, this flaw introduces a risk of data integrity violations where records may be partially written or interpreted incorrectly by downstream services. While direct remote code execution is not the primary vector for this specific caching error, the resulting instability and potential for denial-of-service through resource exhaustion due to repeated failed processing attempts pose a tangible availability risk. Furthermore, if the misinterpreted objects are subsequently used in database queries or other sensitive operations without further sanitization, there remains an indirect pathway for injection-like behaviors depending on how the application handles these malformed instances.
Mitigation strategies primarily involve upgrading to patched versions of Spring Cloud Stream where this caching logic has been corrected to ensure that content type and schema metadata are validated against each message context rather than relying on potentially stale global or session-level caches. Developers should also implement robust error handling mechanisms in their consumer applications to gracefully manage deserialization failures, ensuring that invalid messages do not crash the entire processing pipeline. Additionally, auditing the usage of Avro schemas within the application can help identify areas where schema evolution might trigger this caching bug, allowing for proactive testing against version transitions. It is advisable to monitor logs for serialization exceptions and configure metrics around message rejection rates to detect potential exploitation or degradation in real-time. Aligning with industry standards such as CWE-20 Improper Input Validation highlights the need for rigorous checking of data types before processing, while ATT&CK techniques related to Defense Evasion via Obfuscated Files may be relevant if attackers attempt to exploit these parsing errors to hide malicious payloads within seemingly valid but structurally mismatched messages.