Http4s (http4s-blaze-server) is a minimal, idiomatic Scala interface for HTTP services. Http4s before versions 0.21.17, 0.22.0-M2, and 1.0.0-M14 have a vulnerability which can lead to a denial-of-service. Blaze-core, a library underlying http4s-blaze-server, accepts connections unboundedly on its selector pool. This has the net effect of amplifying degradation in services that are unable to handle their current request load, since incoming connections are still accepted and added to an unbounded queue. Each connection allocates a socket handle, which drains a scarce OS resource. This can also confound higher level circuit breakers which work based on detecting failed connections. http4s provides a general MaxActiveRequests middleware mechanism for limiting open connections, but it is enforced inside the Blaze accept loop, after the connection is accepted and the socket opened. Thus, the limit only prevents the number of connections which can be simultaneously processed, not the number of connections which can be held open. In 0.21.17, 0.22.0-M2, and 1.0.0-M14, a new maxConnections property, with a default value of 1024, has been added to the BlazeServerBuilder. Setting the value to a negative number restores unbounded behavior, but is strongly disrecommended. The NIO2 backend does not respect maxConnections. Its use is now deprecated in http4s-0.21, and the option is removed altogether starting in http4s-0.22. There are several possible workarounds described in the refrenced GitHub Advisory GHSA-xhv5-w9c5-2r2w.
The product does not properly control the allocation and maintenance of a limited resource, thereby enabling an actor to influence the amount of resources consumed, eventually leading to the exhaustion of available resources.
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Http4s | Typelevel | * | 0.21.17 (excluding) |
Limited resources include memory, file system storage, database connection pool entries, and CPU. If an attacker can trigger the allocation of these limited resources, but the number or size of the resources is not controlled, then the attacker could cause a denial of service that consumes all available resources. This would prevent valid users from accessing the product, and it could potentially have an impact on the surrounding environment. For example, a memory exhaustion attack against an application could slow down the application as well as its host operating system. There are at least three distinct scenarios which can commonly lead to resource exhaustion:
Resource exhaustion problems are often result due to an incorrect implementation of the following situations:
Mitigation of resource exhaustion attacks requires that the target system either:
The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question.
The second solution is simply difficult to effectively institute – and even when properly done, it does not provide a full solution. It simply makes the attack require more resources on the part of the attacker.