Support for SURBL HASHBL
Idea shared by Scott Hendrickson - 3/5/2026 at 6:12 PM
Under Consideration
Hi Folks!  

Apparently in 2022, SURBL launched a new HASHBL that tracks more than just URIs within malicious emails and sounds like it would make a great addition to any spam filter.  According to SURBL, in their HASHBL they track the following...
  • Phone number listings
  • Abused shortened links
  • Abused sender email addresses and reply-to's
  • Abused public cloud providers
  • Full URI listings
  • Crypto hash listings
We get a pretty fair number of scam emails containing bogus phone numbers and shortened URLs that get past the current blocklists.  I don't know about you, but I think the first two items alone would be worth giving SmarterMail the ability to check this new list.  So if you also think this would be a good thing to add to SmarterMail's anti-spam protection, please give it a thumbs up.  

Thanks!  
Scott Hendrickson
SOS4Net, Inc.
Centennial, CO. U.S.A.
I don't understand how this will work, seems like more than just a simple text string converted into a DNS query, and therefore too complex for SmarterMail's rudimentary filtering capabilities.    I suggest you implement it in a customizable spam filter like Declude or rSpamD, then report results.   It is also the quickest path to getting what you want.
Hi Douglas:  

I don't have all the details.  However from what I've read, it looks like you're supposed to convert the phone number, email address, etc. to an MD5 hash, add the hash to the beginning of the appropriate SURBL list URL (similar to sending an IP) and send it to them.  They're supposed to send back the same type of response as their other lists.  

Scott
Scott Hendrickson SOS4Net, Inc. Centennial, CO. U.S.A.
At present, my content parsing tools lack that sophistication.   I wnder if there is open source software which does.

Zach Sylvester Replied
Employee Post
Hey Guys, 

This is something we could do. We already extract email content for URIBLs so it could be done. We would just have to add extra regex to detect phone numbers and such. I will add this to the feature request list. 

Kind Regards,
Zach Sylvester Software Developer SmarterTools Inc. www.smartertools.com
Regex in Security -> EHLO / Email Address + Domain Blocking PLEASE while you're at it.
MailEnable survivor / convert --
As we add complexity to the parsing process, we also need to think about performance.  Not every message requires the same degree of analysis.   I suggest that the flow looks like this:
- Perform authentication tests so that whitelisting can be done accurately
- If any test triggers a whitelisting result, stop filtering
- After whitelisting is ruled out, if any test produces a blacklist result, stop filtering
- After whitelisting and blacklisting are ruled out, if any test produces quarantine, stop filtering.   
- After all tests are processed with none of these dispositions, deliver the message.

A bit of philosophy relates to this sequence:  Content filtering is error-prone, and hostile content indicates a hostile sender who needs to be blocked.   So I argue that suspicious content should always send to quarantine, so that the false positives can be allowed and the true positives can lead to sender block rules.   Blocks should only be applied to certainty, which is based on sender reputation, and can be determined early in the filtering process.   Collectively, this means that the first quarantine result is the only one that I need. 

Back to the appeal of this service:
Traditional content filtering is inefficient because it checks every filter rule against every message.  The cost increases linearly with the number of checks being performed, so the feasible number of checks is measured in 10s rather than 1000s.   Cost also increases as the size of the message being checked.   This service is interesting because cost is driven by the number of extracted content items, which is limited, not by the number of known threats, which may be very large.

Nonetheless, it could be a big waste of time to do this full suite of tests on highly trusted messages.   Most or all of the threats come from less than 10% of all messages (particularly messages from previously unknown senders).  Everything related to message filtering is improved if you know your prior senders from new senders.

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