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ReDoS via AMQP 1.0 SQL filter LIKE wildcard

Low
ansd published GHSA-59c5-553c-57m2 Jul 9, 2026

Software

rabbitmq-server

Affected versions

>= 4.2.0, < 4.2.7
>= 4.3.0, < 4.3.1

Patched versions

4.2.7
4.3.1

Description

Origin

This vulnerability was identified by Team RabbitMQ and/or other teams at Broadcom, not via a responsible disclosure from an external researcher.

Source references

deps/rabbit/src/rabbit_amqp_filter_sql.erl:377 · ReDoS

Impact

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.

Description

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.

Exploit scenario

Attacker with read+write on a stream queue publishes messages with property p = "aaaa...(long, no X)", then attaches a receiver with filter p LIKE '%%%...%X' (~4080 chars). Each delivery candidate runs the catastrophic regex for ~100-200 ms; thousands of messages times parallel sessions saturate CPU.

Preconditions

  • 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)

CVSS

CVSS v4.0: 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 (2.3, Low). Fixed in 4.2.7, 4.3.1.

Severity

Low

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements Present
Privileges Required Low
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity None
Availability Low
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
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

CVE ID

No known CVE

Weaknesses

Inefficient Regular Expression Complexity

The product uses a regular expression with an inefficient, possibly exponential worst-case computational complexity that consumes excessive CPU cycles. Learn more on MITRE.