What tools can send an ARF report test?

Key findings

• Crucial for feedback loops: ARF reports are fundamental for effective feedback loop (FBL) processing, which informs senders about spam complaints from major mailbox providers. Understanding how these reports impact ESPs is vital for deliverability.
• Limited dedicated tools: There are very few purpose-built tools designed specifically to generate and send ARF report tests, leading many to seek alternative methods.
• Manual workarounds are common: Many users resort to manually copying, modifying, and re-injecting existing ARF emails into their processing mailboxes for testing purposes.
• Open-source projects exist: Some community-driven or open-source projects, often found on platforms like GitHub, offer basic ARF message generation capabilities. For instance, Abusix maintains an eXtended Abuse Reporting Format (XARF) project, which extends RFC 5965.

Key considerations

• Accuracy of manual tests: Relying on manual creation of ARF reports introduces the risk of errors or inconsistencies, potentially leading to inaccurate test results.
• Need for real-world scenarios: Effective testing requires simulating various real-world ARF report scenarios, which can be difficult without dedicated tools.
• Integration and scalability: Testing should ideally integrate seamlessly with existing email infrastructure to validate the entire feedback loop process, from reception to data processing. For broader insights into testing, explore popular email deliverability testing tools.
• Testing outside your network: Testing ARF reports externally (outside your own network) is preferred to fully replicate how mailbox providers send these reports.

Key opinions

• Manual testing is common: Many marketers manually copy and modify actual ARF emails to use for testing, re-injecting them into their processing mailboxes.
• Desire for dedicated tools: There's a clear expressed interest and need for a specific, automated tool that can send ARF report tests, indicating dissatisfaction with current manual workarounds.
• GitHub as a resource: Some suggest looking to GitHub for existing open-source projects that might generate ARF reports, such as the ARF message generator.
• Testing external networks: Marketers frequently express a desire to test ARF reports from outside their internal network, similar to how services like Mail-Tester operate for general email tests.

Key considerations

• Complexity of DIY: While DIY solutions are feasible, they can be cumbersome and less than ideal for consistent or extensive testing.
• Workflow optimization: There's an open question regarding the ideal workflow for generating and processing test ARF reports efficiently.
• Multiple FBL addresses: Setting up multiple feedback loop (FBL) addresses (e.g., through Validity's uFBL) can provide varied complaint reports for testing different processing paths. This can be critical for how ESPs use ARF data.
• Impact on spam rates: Accurate ARF report processing directly influences efforts to mitigate issues such as emails going to spam.

Key opinions

• Critical for reputation: Testing ARF reports is critical for ensuring that feedback loop mechanisms are functioning correctly, which directly impacts a sender's reputation and ability to stay off email blacklists (or blocklists).
• Preference for automation: Automated solutions are generally preferred for ARF testing due to their reliability, scalability, and ability to handle diverse test scenarios more efficiently than manual methods.
• Adherence to RFC 5965: A deep understanding of the ARF specification (RFC 5965) is essential for developing or utilizing any testing mechanism, ensuring that generated reports are valid and correctly interpreted.
• ARF data for deliverability: ARF data, alongside DMARC reports, is vital for diagnosing deliverability issues and refining sending practices to reach the inbox consistently. Learn more about email authentication basics.

Key considerations

• Accurate interpretation: The key is not just receiving ARF reports, but accurately parsing and interpreting the data to extract actionable insights for list hygiene and campaign optimization.
• Simulating complex scenarios: An ideal ARF testing solution should be capable of simulating various complaint types and report structures to thoroughly test a processing system's robustness.
• Feedback loop management: Proper registration and management of feedback loops with mailbox providers are prerequisites for receiving ARF reports, whether for testing or production. This aligns with broader sender requirements, such as Outlook's new sender requirements.
• Data security: Handling ARF reports, which contain original message content, requires careful consideration of data security and privacy.

Key findings

• RFC 5965 defines ARF: The Internet Engineering Task Force (IETF) formally defined the Abuse Reporting Format in RFC 5965. This specifies the structure and content for email feedback reports.
• Purpose is abuse identification: ARF reports are designed to help email senders identify and address the sources of email abuse, such as spam complaints or phishing attempts.
• Includes original message: A key component of ARF reports is the inclusion of the original message headers and often its content, which is crucial for diagnosing the reported issue.
• Extensible format: The ARF format allows for extensibility, as seen with projects like XARF (eXtended Abuse Reporting Format), which add more data fields.

Key considerations

• Adherence to standards: Any tool or method for sending ARF report tests must strictly adhere to the RFC 5965 standard to ensure interoperability and correct interpretation by processing systems. This is similar to the importance of following standards like RFC 5322 for email formatting.
• Accurate parsing: The ability of a system to accurately parse ARF reports and extract all relevant data points is crucial for effective feedback loop management.
• Security implications: Handling ARF reports, which contain potentially sensitive email content, necessitates robust security measures in any processing or testing environment.
• Distinction from DMARC: While both provide feedback, ARF reports (complaints) differ from DMARC aggregate and forensic reports in their purpose and content. Understanding DMARC tags and meanings helps differentiate report types.