CVE-2026-61634 (GCVE-0-2026-61634)
Vulnerability from cvelistv5 – Published: 2026-08-18 16:32 – Updated: 2026-08-18 17:27
VLAI
EPSS
VEX
Title
RabbitMQ Java client accepts broker frames larger than the negotiated AMQP frame_max
Summary
The RabbitMQ Java client library allows Java and JVM-based applications to connect to and interact with RabbitMQ nodes. Prior to 5.33.0, the AMQP connection tuning path records the negotiated AMQP frame_max value, but src/main/java/com/rabbitmq/client/impl/SocketFrameHandler.java and NettyFrameHandlerFactory continue to validate broker-controlled frame payload lengths against maxInboundMessageBodySize because the negotiated limit is not applied consistently through setMaxInboundFramePayloadSize. A malicious or compromised broker can send a method frame larger than the negotiated frame_max during or after connection establishment, causing the client to allocate and decode a protocol-invalid frame instead of rejecting it with MalformedFrameException. The protocol violation can disrupt the affected connection and cause client-side denial of service. This issue is fixed in version 5.33.0.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-18 17:26 UTC
CWE
- CWE-20 - Improper Input Validation
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://github.com/rabbitmq/rabbitmq-java-client/… | x_refsource_CONFIRM |
| https://github.com/rabbitmq/rabbitmq-java-client/… | x_refsource_MISC |
| https://github.com/rabbitmq/rabbitmq-java-client/… | x_refsource_MISC |
| https://github.com/rabbitmq/rabbitmq-java-client/… | x_refsource_MISC |
| https://github.com/rabbitmq/rabbitmq-java-client/… | x_refsource_MISC |
| https://github.com/rabbitmq/rabbitmq-java-client/… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| rabbitmq | rabbitmq-java-client |
Affected:
< 5.33.0
|
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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