CVE-2026-67240 (GCVE-0-2026-67240)
Vulnerability from cvelistv5 – Published: 2026-09-23 20:19 – Updated: 2026-09-24 14:24
VLAI
EPSS
VEX
Title
RabbitMQ: ReDoS via AMQP 1.0 SQL filter LIKE wildcard
Summary
RabbitMQ is a messaging and streaming broker. Prior to versions 4.2.7 and 4.3.1, pattern_to_regex maps % -> .*? and _ -> ., then compiles ^...$ with only [unicode]; re:run is called with only [{capture, none}] - no explicit match_limit. A pattern like %_%_..._%X becomes ^.*?..*?.....*?.X$ with overlapping lazy quantifiers. The whole-expression cap is ?MAX_EXPRESSION_LENGTH=4096 chars / ?MAX_TOKENS=200; a LIKE string literal is one token, so ~2000 %_ pairs fit. SQL filters are accepted unconditionally at rabbit_amqp_session.erl:3264 (no feature flag). Evaluated per-message at rabbit_stream_queue.erl:1439. OTP's default 10M match_limit caps each match at ~100-200 ms (not seconds), and the re NIF yields to the scheduler. An authenticated AMQP 1.0 consumer with read+write on a stream queue can cause ~100-200 ms of CPU per delivered message via a crafted LIKE filter, multiplied across thousands of messages and parallel sessions - a substantial backtracking-driven CPU amplification. Preconditions include AMQP 1.0 with stream queues in use Attacker can attach a receiver with a filter (read permission) and publish messages with long property values (write permission). This issue is fixed in versions 4.2.7 and 4.3.1.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-24 14:24 UTC
CWE
- CWE-1333 - Inefficient Regular Expression Complexity
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/rabbitmq/rabbitmq-server/secur… | x_refsource_CONFIRM |
| https://github.com/rabbitmq/rabbitmq-server/relea… | x_refsource_MISC |
| https://github.com/rabbitmq/rabbitmq-server/relea… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| rabbitmq | rabbitmq-server |
Affected:
>= 4.2.0, < 4.2.7
Affected: >= 4.3.0, < 4.3.1 |
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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