blaze is a Scala library for building asynchronous pipelines, with a focus on network IO. All servers running blaze-core before version 0.14.15 are affected by a vulnerability in which unbounded connection acceptance leads to file handle exhaustion. Blaze, accepts connections unconditionally on a dedicated thread 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. The vast majority of affected users are using it as part of http4s-blaze-server <= 0.21.16. http4s provides a mechanism for limiting open connections, but 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. The issue is fixed in version 0.14.15 for NIO1SocketServerGroup. A maxConnections parameter is added, with a default value of 512. Concurrent connections beyond this limit are rejected. To run unbounded, which is not recommended, set a negative number. The NIO2SocketServerGroup has no such setting and is now deprecated. There are several possible workarounds described in the refrenced GitHub Advisory GHSA-xmw9-q7x9-j5qc.
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 |
|---|---|---|---|
| Blaze | Typelevel | * | 0.14.15 (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.