Why is my domain or IP blocked by Spamhaus and how do I resolve it?

Key findings

1. Identifying the listing: A Spamhaus block often appears as an SBLCSS listing, meaning your IP address exhibits suspect behavior, is misconfigured, or has poor sending practices. Checking the Spamhaus Blocklist Removal Center provides details on the specific listing.
2. Root causes: Listings are usually due to unsolicited mail, compromised accounts, or poor list hygiene. Spamhaus focuses on preventing email-based abuse.
3. Domain vs. IP: Your entire domain can be listed, which in turn causes associated IP addresses to be blocked, indicating a network-wide spam issue rather than an isolated incident.
4. Shared infrastructure: If you are using a shared IP, the actions of other senders can impact your deliverability. This necessitates careful troubleshooting for shared email infrastructure.

Key considerations

1. Thorough investigation: Don't just request delisting, identify the root cause of the block. This involves reviewing sending practices, list acquisition methods, and monitoring complaint rates.
2. Address the problem: Before requesting removal, ensure that the underlying issue causing the listing has been fixed. Spamhaus will only delist an IP or domain once they are confident the problem has been resolved.
3. Monitor reputation: Regularly check your IP and domain reputation on various blocklists to detect issues early. Our guide to email blocklists provides comprehensive details.
4. Proactive measures: Implement strict opt-in processes and regularly clean your email lists to prevent sending to spam traps or disengaged recipients.

Key opinions

1. Initial confusion: Marketers frequently express difficulty in pinpointing the specific reason for a blocklist listing, even after reviewing their own sending data.
2. Campaign structure concerns: Sending through many different subdomains under a single root domain can raise suspicions with blocklist providers like Spamhaus, potentially leading to CSS listings if spam traps are hit.
3. Reputation tracking: Many marketers rely on external reputation tools, such as the Talos Intelligence Reputation Center, to get a broader view of their sending health beyond just direct blocklist checks.
4. Complaint feedback loops: Even with systems in place to handle FBLs (Feedback Loop complaints), marketers sometimes underestimate the impact of even a few complaints leading to a block.

Key considerations

1. Granular monitoring: Implement robust blocklist monitoring for all sending IPs and domains, including subdomains, to catch problems early.
2. Recipient engagement: Focus on sending only to truly engaged recipients. High complaint rates, even if few, are a strong indicator of unwanted mail.
3. Review sending practices: Evaluate if your sending patterns, such as extensive use of varying subdomains for similar content, could be misinterpreted as suspicious by automated systems.
4. Proactive vetting: For email service providers (ESPs), a rigorous vetting process for new clients is crucial to prevent abusive senders from impacting your shared reputation.

Key opinions

1. Sophisticated detection: Spamhaus's routines are designed to detect patterns of poor reputation and non-permission mail, not just single spam complaints. This includes looking at various domains associated with an IP.
2. Customer responsibility: For ESPs or those sending on behalf of clients, the primary issue is often clients sending non-opt-in mail, and Spamhaus believes the provider is not doing enough to control them.
3. Spam trap hits: Sending to Spamhaus traps is a significant indicator of poor list quality and non-permission sending. You can't directly detect who reports to Spamhaus because users don't; spam traps do.
4. Domain age: The age of a domain can influence how Spamhaus perceives sending behavior, with newer domains potentially receiving less leeway.

Key considerations

1. Identify problematic senders: The key is to identify which customers or internal users are sending mail that Spamhaus flags as problematic, particularly non-opt-in or 'cold emails'.
2. Customer segregation: Segregating customers based on reputation, with those showing good behavior using clean IPs, can prevent widespread blocklisting. This is particularly relevant when warming a new IP range.
3. Proof of consent: Treat FBLs as direct complaints and require customers to provide proof of consent for recipients generating such feedback.
4. Eliminate non-permission mail: The fundamental solution is to eradicate sending to non-permission lists, as this is the root cause of many blocklist issues. Understanding what causes legitimate listings is key.

Key findings

1. SBL policy: According to Spamhaus's SBL policy, IPs are listed when they appear to be controlled by, used by, or made available for use by spammers and abusers in unsolicited bulk email or other Internet abuse.
2. CSS listings: CSS (Composite Blocking List) listings are often due to suspect behavior, misconfiguration, or poor sending reputation, encompassing a broad range of issues that signal abuse.
3. FBLs as indicators: Feedback Loop (FBL) complaints are a direct signal from ISPs about user dissatisfaction and are a strong indicator of non-permission mail, as highlighted in documentation on spam traps.
4. Reputation is dynamic: Security intelligence platforms like Talos Intelligence provide reputation data based on mail volumes and observed threats, indicating ongoing issues that may lead to blocklistings.

Key considerations

1. Adherence to policies: Understand and strictly adhere to the Spamhaus SBL policies, which clearly state the conditions for listing and delisting.
2. System hardening: Ensure your mail servers are not misconfigured or compromised, as this can lead to listings even without intentional spamming.
3. Monitor FBLs actively: Use FBL data to identify and remove problematic recipients or campaigns quickly. This is essential for preventing email backscatter and other issues.
4. Proof of consent: Maintain verifiable proof of consent for all recipients to demonstrate legitimate sending practices when engaging with blocklist operators.