XARF, short for the eXtended Abuse Reporting Format, is a standardized set of schemas developed by Abusix and a community around for describing abusive behaviour or abusive content.
It has been adopted as a quasi-standard by several governments, Enterprises, and a large ISPs and Hosting Companies and other involved organizations.
Designed to be shared via email, which is still the standard for abuse reporting, it can be shared by other mechanisms like HTTP as well. Structured in a fashion that at least has to contain the bare minimum set of information to act upon the origin of abusive behaviour within a network makes XARF light and slim. Simplicity is the most significant benefit compared with STIX/TAXII or IODEF or other formats that serve a completely different use case.
Attacks on network infrastructure, trademark, copyright infringements, dangerous content like phishing, hosted malware or illegal content like child exploitation, all have an origin or a source.
Informing the owner or maintainer of the source to stop the attacks or take down the content in question is the only way to mitigate these issues and therefore is an essential part of the internet infrastructure. Unfortunately, the status quo for reporting abuse is a very unstructured and cluttered environment, which is the primary reason for the lack of efficiency in operationalizing abuse report metadata today.
XARF is a standard developed to improve the ability of recipients of abuse reports to operationalize the data. Unlike previous methods of sharing network abuse data, XARF is simple, extensible and structured and, therefore, easily automated. XARF aims to improve security measures in a few ways:
- Extend the capabilities of current network abuse report sharing.
- Add the flexibility to adapt to new use cases as they occur quickly.
- Easy to generate.
- Easy to read (machine- as well as human-readable).
- Provide the basis for a unified and holistic approach to abuse handling.
XARF is an open, community-driven effort that provides free specifications to aid in the automated expression of information about network abuse observed.