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libp2p DoS vulnerability from lack of resource management

High severity GitHub Reviewed Published Dec 7, 2022 in libp2p/go-libp2p • Updated Feb 14, 2023

Package

gomod github.com/libp2p/go-libp2p (Go)

Affected versions

< 0.18.0

Patched versions

0.18.0

Description

Impact

Versions older than v0.18.0 of go-libp2p are vulnerable to targeted resource exhaustion attacks. These attacks target libp2p’s connection, stream, peer, and memory management. An attacker can cause the allocation of large amounts of memory, ultimately leading to the process getting killed by the host’s operating system. While a connection manager tasked with keeping the number of connections within manageable limits has been part of go-libp2p, this component was designed to handle the regular churn of peers, not a targeted resource exhaustion attack.

In the original version of the attack, the malicious node would continue opening new streams on a stream multiplexer that doesn’t provide sufficient back pressure (yamux or mplex). It is easy to defend against this one attack, but there are countless variations of this attack:

  • Opening streams and causing a non-trivial memory allocation (e.g., for multistream or protobuf parsing)
  • Creating a lot of sybil nodes and opening new connections across nodes

Patches (What to do as a go-libp2p consumer:)

  1. Update your go-libp2p dependency to go-libp2p v0.18.0 or greater (current version as of publish date is v0.24.0.)

    • Note: It's recommend that you update to v0.21.0 onwards as you’ll get some useful functionality that will help in production environments like better metrics around resource usage, Grafana dashboards around resource usage, allow list support, and default autoscaling limits. Please see the v0.21.0 release notes for more info.)
  2. Determine appropriate limits for your application - go-libp2p sets up a resource manager with the default limits if none are provided. For default definitions please see limits_defaults.go. These limits are also set to automatically scale, this is done using the AutoScale method of the ScalingLimitConfig. We recommend you tune your limits as described here.

  3. Configure your node to be attack resilient. See how to respond to an attack and identify misbehaving peers here. Then setup automatic blocking with fail2ban using canonical libp2p log lines: guide on how to do so here.

Examples

Note: go-libp2p still implements the connection manager mentioned above. The connection manager is a component independent of the resource manager, which aims to keep the number of libp2p connections between a low and a high watermark. When modifying connection limits, it’s advantageous to keep the configuration of these components consistent, i.e., when setting a limit of N concurrent connections in the resource manager, the high watermark should be at most (and ideally slightly less) than N.

Workarounds

Although there are no workarounds within go-libp2p, some range of attacks can be mitigated using OS tools (like manually blocking malicious peers using iptables or ufw ) or making use of a load balancer in front of libp2p nodes.

However these require direct action & responsibility on your part and are no substitutes for upgrading go-libp2p. Therefore, we highly recommend upgrading your go-libp2p version for the way it enables tighter scoped limits and provides visibility into and easier reasoning about go-libp2p resource utilization.

References

Please see our DoS Mitigation page for more information on how to incorporate mitigation strategies, monitor your application, and respond to attacks: https://docs.libp2p.io/reference/dos-mitigation/.

Please see the related disclosure for rust-libp2p: GHSA-jvgw-gccv-q5p8 and js-libp2p: GHSA-f44q-634c-jvwv

For more information

If you have any questions or comments about this advisory email us at [email protected]

References

@p-shahi p-shahi published to libp2p/go-libp2p Dec 7, 2022
Published to the GitHub Advisory Database Dec 7, 2022
Reviewed Dec 7, 2022
Published by the National Vulnerability Database Dec 8, 2022
Last updated Feb 14, 2023

Severity

High

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 v3 base metrics

Attack vector
Network
Attack complexity
Low
Privileges required
None
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

EPSS score

0.199%
(58th percentile)

Weaknesses

CVE ID

CVE-2022-23492

GHSA ID

GHSA-j7qp-mfxf-8xjw

Source code

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