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RabbitMQ Server CVE-2026-67240

| EUVDEUVD-2026-85636 LOW
Inefficient Regular Expression Complexity (ReDoS) (CWE-1333)
2026-09-23 GitHub_M
2.3
CVSS 4.0 · Vendor: GitHub_M
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Attack ChainAIDerived

Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain

  1. Access
    Authenticate as AMQP 1.0 user with stream read+write
  2. Delivery
    Attach receiver with crafted LIKE filter
  3. Exploit
    Publish messages with long property values
  4. Execution
    Trigger backtracking regex per delivery
  5. Persist
    Amplify CPU across sessions/messages
  6. Impact
    Degrade broker CPU availability

Vulnerability Assessment

Weakness
CWE
ASVS 5.0
V2 - Validation and Business Logic Regex complexity limits
MITRE ATT&CK

1 technique across 1 tactic: Impact

The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up.

Exploitation Conditions

Exploitation requires all of: (1) AMQP 1.0 protocol in use with stream queues configured; (2) SQL filter expressions accepted on stream consumers (per the description these are accepted unconditionally at rabbit_amqp_session.erl:3264 with no feature flag when stream queues are used); (3) an authenticated account (PR:L) holding BOTH read permission (to attach a receiver with a filter) and write permission (to publish messages with long property values) on the target stream queue. Limiting factors: the attacker must already be authenticated and authorized on the stream; impact is bounded CPU amplification (~100-200 ms per delivered message, scheduler-yielding, capped by OTP's 10M match_limit) rather than a node crash or hang; and deployments not using AMQP 1.0 stream queues with SQL filters are unaffected.

Impact

This is a low-priority issue whose signals are internally consistent and point away from urgency. The vendor CVSS 4.0 score is 2.3 (AV:N/AC:L/AT:P/PR:L/UI:N) with only VA:L - an availability-integrity-confidentiality profile of N/N/Low, i.e. CPU amplification, not a crash or data exposure. Crucially the description itself explains why the ceiling is low: OTP's default 10M match_limit caps each regex match at ~100-200 ms and the re NIF yields to the scheduler, so this is backtracking-driven CPU burn amplified across many messages/sessions rather than a single-request outage or unbounded hang. AT:P (Present) correctly reflects that exploitation is gated on specific preconditions - AMQP 1.0 with stream queues in use and SQL LIKE filters accepted. There is no EPSS score, no CISA KEV listing, and no public exploit code - no public exploit identified at time of analysis. There are no conflicting signals here: this is a genuinely low-severity, config-and-authentication-gated resource-consumption bug, not a high-CVSS-but-low-real-risk mismatch. The CWE-1333 (inefficient regular expression complexity / ReDoS) classification matches the pattern_to_regex overlapping-lazy-quantifier root cause. Priority should be routine patching to 4.2.7/4.3.1, not emergency response.

Exploit ScenarioDerived

An attacker who already holds a valid RabbitMQ account with read and write permissions on a stream queue attaches an AMQP 1.0 receiver carrying a crafted SQL LIKE filter (e.g. a ~2000-pair %_%_..._%X pattern) and publishes messages with long property values. Each delivered message forces ~100-200 ms of catastrophic-backtracking CPU during per-message filter evaluation, and the attacker multiplies this across thousands of messages and parallel sessions to degrade node CPU. No public exploit code is identified at time of analysis.

Monitor Track and reassess. Why: LOW severity (CVSS 0.1-3.9).
Patch available 0.3% exploitation probability (EPSS)

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AnalysisAIv1 – Sep 23, 2026

Authenticated CPU exhaustion in RabbitMQ Server prior to 4.2.7 and 4.3.1 lets an AMQP 1.0 consumer that holds both read and write permission on a stream queue drive substantial backtracking work in the broker's regex engine: a crafted LIKE filter built from roughly two thousand overlapping '%_' wildcard pairs, combined with long message property values, forces about 100-200 ms of CPU to be consumed per delivered message. The impact is bounded resource amplification rather than a node crash or hang, consistent with a low-severity denial-of-service rating (CVSS 4.0 base 2.3; independent assessment CVSS 3.1 AV:N/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L), and no public exploit code has been identified at time of analysis. Exposure is limited to deployments that actually use AMQP 1.0 with stream queues and SQL filter expressions; systems not using that combination are unaffected.

RemediationAI

Vendor-released patch: upgrade to RabbitMQ 4.2.7 or 4.3.1, depending on the release train in use (advisory GHSA-59c5-553c-57m2, release links https://github.com/rabbitmq/rabbitmq-server/releases/tag/v4.2.7 and https://github.com/rabbitmq/rabbitmq-server/releases/tag/v4.3.1); note that nodes will fail to start on Erlang versions below the minimum listed in the RabbitMQ/Erlang compatibility matrix, so plan the Erlang upgrade alongside it. Where immediate upgrade is not possible, focus on the two prerequisites the exploit requires: because the filter path is accepted unconditionally and cannot be switched off by configuration, restrict who can hold permissions on AMQP 1.0 stream queues so that only fully trusted accounts have read, write or configure rights, and audit existing permission sets to remove any account that holds both read and write on a stream queue without a clear need (trade-off: this may break legitimate filtering consumers). If stream queues are not required, block or avoid AMQP 1.0 stream queue consumption and stick to AMQP 0-9-1 (trade-off: loss of AMQP 1.0 features and any streaming semantics). Where stream queues must stay in use, keep consumer-supplied SQL filters to trusted producers only, cap message property sizes at the ingress boundary to deny the long-value half of the attack, and monitor broker CPU per connection so that a burst of ~100-200 ms of CPU per delivered message across thousands of messages and parallel sessions can be detected and throttled. No public exploit code has been identified at time of analysis, so these measures are precautionary hardening rather than emergency response.

Affected VersionsENISA

rabbitmq-server 4.3.0, < 4.3.1
rabbitmq-server 4.2.0, < 4.2.7

Affected Packages

euvd rabbitmq_server_4_2_0 fix: 4.2.7
euvd rabbitmq_server_4_3_0 fix: 4.3.1

Source: OSV (Open Source Vulnerabilities)

Affects your stack

Affects 2 packages in euvd

Checking this CVE against your own stacks and servers is part of the Basic plan and up.

Technical ContextAI · show details

The defect is a classic inefficient regular-expression complexity issue (CWE-1333, ReDoS) in RabbitMQ's translation of SQL LIKE patterns into anchored Erlang regular expressions. RabbitMQ's pattern_to_regex routine maps '%' to '.*?' and '_' to '.', then compiles '^...$' with only the [unicode] option, and invokes re:run with just [{capture, none}] and no explicit match_limit. Because lazy quantifiers of the form '.*?..*?.....*? .X$' overlap, the backtracking engine explores a combinatorial number of paths on non-matching input. The whole-expression guards are generous relative to the attack: ?MAX_EXPRESSION_LENGTH=4096 characters and ?MAX_TOKENS=200, but an entire LIKE string literal counts as a single token, so roughly 2000 '%_' pairs fit comfortably within those limits. These SQL filters are accepted unconditionally at rabbit_amqp_session.erl:3264 (no feature flag gates them when stream queues are used) and are evaluated per message at rabbit_stream_queue.erl:1439. The Erlang/OTP re NIF yields to the scheduler and OTP's default 10M match_limit caps an individual match at approximately 100-200 ms rather than seconds, which is why the outcome is sustained CPU amplification across thousands of messages and parallel sessions rather than a single long stall. The CPE string cpe:2.3:a:rabbitmq:rabbitmq-server:*:*:*:*:*:*:*:* reflects that all RabbitMQ Server builds below the fixed releases carry the flaw.

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Severity by source

Vendor (GitHub_M) PRIMARY 2.3 LOW
CVSS:4.0/AV:N/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N
vuln.today AI 3.1 LOW

Authenticated stream user (PR:L), remote (AV:N); AC:H reflects the AMQP 1.0 + stream-queue + SQL-filter precondition; only bounded CPU degradation so A:L, no C/I impact.

3.1 AV:N/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
4.0 AV:N/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N

Primary rating from Vendor (GitHub_M).

CVSS VectorVendor: GitHub_M

Attack Vector
Network
Attack Complexity
Low
Attack Requirements
Present
Privileges Required
Low
User Interaction
None
Vulnerable system: Confidentiality
None
Vulnerable system: Integrity
None
Vulnerable system: Availability
Low
Subsequent system: Confidentiality
None
Subsequent system: Integrity
None
Subsequent system: Availability
None

Lifecycle Timeline

2
Patch available
Sep 23, 2026 - 21:17 EUVD
CVE Published
Sep 23, 2026 - 20:19 cve.org
LOW 2.3

DescriptionCVE.org

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.

Threat Intelligence Overview

EPSS Score
0.3%
Top 84% exploitable
Prevalence
High
RabbitMQ is a widely-deployed open-source enterprise messaging broker
Data Quality
5 sources
Confidence: Medium

How this decision was reached

The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.

SignalValue
Severity LOW
CISA KEV No
Public POC No
EPSS 0.3%
Patch available Yes
Active threat No
Deciding rule
LOW severity (CVSS 0.1-3.9)
What every action level means
Emergency Immediate action required. Criteria: CRITICAL + in CISA KEV (actively exploited) without patch, OR CRITICAL + EPSS > 30% without patch, OR CRITICAL + active threat signals without patch.
Act Now Prioritize within 24-48h. Criteria: in KEV with patch available, OR CRITICAL + public POC, OR HIGH + EPSS > 10%, OR CRITICAL + active threat, OR any CRITICAL severity.
This Week Schedule remediation this week. Criteria: HIGH severity (CVSS 7.0-8.9). Patch within normal change window.
This Month Plan within 30 days. Criteria: MEDIUM severity (CVSS 4.0-6.9), lower exploitation probability. Include in regular patch cycle.
Monitor Track and reassess. Criteria: LOW severity (CVSS 0.1-3.9), minimal exploitation risk. No immediate action needed.

SSVC AssessmentENISA

SSVC Decision· derived
Defer
Exploitation
Poc
System Exposure· derived
Open
Automatable
No
Human Impact· derived
Low
Technical Impact
Partial

Priority Score

12
Low Medium High Critical
KEV: 0
EPSS: 0
CVSS: +12
PoC: 0
Weaponized: 0
Threat intel: 0
Gov advisory: 0

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