This paper proposes a new and currently very effective method of enhancing the abilities of mail systems to limit the amount of spam that they receive and deliver to their users. For the purposes of this paper, we will call this new method "Greylisting". The reason for choosing this name should become obvious as we progress.
Greylisting has been designed from the start to satisfy certain criteria:
User-level spam blocking, while somewhat effective has a few key drawbacks that make its use in the continuing spam war undesirable. A few of these are:
As a result, Greylisting is designed to be implemented at the MTA level, where we can cause the spammers the most amount of grief.
For the purposes of evaluating and testing Greylisting, an example implementation has been written of a filter that runs at the MTA (Message Transfer Agent) level. The source for this example implementation is available as a link below, and as other implementations or additional utility code become available, they will also be linked.
Greylisting was originally tested on a few small scale mail hosts (less than 100 users, though with a fairly diverse set of senders from all over the world, and volumes over 10,000 email attempts a day). It was designed to be scalable and low impact to both administrators and users, and should be acceptable for use on a wide range of systems. Of course, performance issues are very dependent on implementation details.
Currently, Greylisting is in use on many mail servers, including ones processing several millions of messages per day. And more are being added every day.
The Greylisting method proposed in this paper is a complementary method to other existing and yet-to-be-designed spam control systems, and is not intended as a replacement for those other methods. In fact, it is expected that spammers will eventually try to minimise the effectiveness of this method of blocking, and Greylisting is designed to limit options available to the spammer when attempting to do so.
The great thing about Greylisting is that the only methods of circumventing it will tend to make other spam control techniques just that much more effective (primarily DNS and other methods of blacklisting based on IP address) even after this adaptation by the spammers has occurred.
Greylisting got it's name because it is kind of a cross between black- and white-listing, with mostly automatic maintenance. A key element of the Greylisting method is this automatic maintenance.
The Greylisting method is very simple. It only looks at three pieces of information (which we will refer to as a "triplet" from now on) about any particular mail delivery attempt:
From this, we now have a unique triplet for identifying a mail "relationship". With this data, we simply follow a basic rule, which is:
If we have never seen this triplet before, then refuse this delivery and any others that may come within a certain period of time with a temporary failure.
Since SMTP is considered an unreliable transport, the possibility of temporary failures is built into the core spec (see RFC 821). As such, any well behaved message transfer agent (MTA) should attempt retries if given an appropriate temporary failure code for a delivery attempt (see below for discussion of issues concerning non-conforming MTA's).
During the initial testing of Greylisting in mid-2003, it was observed that the vast majority of spam appears to be sent from applications designed specifically for spamming. These applications appear to adopt the "fire-and-forget" methodology. That is, they attempt to send the spam to one or several MX hosts for a domain, but then never attempt a true retry as a real MTA would. From our testing, this means that in the test environment, based on a fairly conservative interpretation of testing data, we have attained an effectiveness of over 95%, and that is with no legitimate mail ever being permanently blocked.
In addition, with the recent rampant proliferation of email-based viruses, Greylisting has been shown to be extremely effective in blocking these viruses, as they also do not tend to retry deliveries. And since these viruses are fairly large, bandwidth and processing savings are significant versus the standard method of accepting delivery and local virus scanning.
This blocking comes with a minimal price from the terms of local resources. Assuming the use of a local datastore for the triplet and other metadata, there is no required network traffic caused by Greylisting other than that associated with the connection itself. Since we are not checking the contents of the message at all there is very little processing overhead, unlike many other spam blocking methods.
There is one effect that could be seen as either a positive or negative. Since the Greylisting method delays acceptance of unknown mail, that will generate a little more work for the sending MTA of legitimate mail. The flip side is that it generates a lot more work and smarts for the spammer's systems, hopefully enough to make the costs of spamming higher, possibly even to the point of making spamming unprofitable for some of them.
The best part is that since we never permanently fail a message delivery, as long as the delivering MTA's are well behaved, we should never cause a legitimate mail to bounce. There should never be a false positive!
In order to implement the Greylisting method, we will use some form of database to hold a few pieces of information about a specific mail relationship that is keyed off of the triplet described above:
With this data, we have everything necessary for a fully functional Greylisting implementation.
The proper place in the SMTP session to perform our checks is as soon as possible
in the mail session when we have all of the needed information available.
To remind those who are not familiar with the low level details of an SMTP session,
a normal command sequence would look something like:
-> HELO somedomain.com <- 250 Hello somedomain.com -> MAIL FROM: <email@example.com> <- 250 2.1.0 Sender ok -> RCPT TO: <firstname.lastname@example.org> <- 250 2.1.5 Recipient ok -> DATA <- 354 Enter mail ... <- 250 2.0.0 Message accepted for delivery
This means, in order to minimize the network traffic required when a mail delivery may be rejected we should perform our checks as soon after the sending MTA has given us all the required information, which is to say, immediately after the RCPT command is received.
In the case where we would temporarily fail a particular delivery attempt, the mail
transaction would look similar to this:
-> MAIL FROM: <email@example.com> <- 250 2.1.0 Sender ok -> RCPT TO: <firstname.lastname@example.org> <- 451 4.7.1 Please try again later
One additional feature which has not yet been mentioned is the provision for some method to allow manual whitelisting of relays, recipients, and possibly even senders.
This manual whitelisting capability is not strictly necessary, but for several reasons, a minimum implentation pretty much requires at least manual whitelisting based on IP address for things like localhost, or primary/backup MX hosts for the domains being handled. Since those relays are presumably smart enough to retry, and should never be blocked anyway, there is little point to delaying mail delivery attempts from them.
Likewise, whitelisting recipients (or recipient domains) may be useful in an ISP or similar setting, where particular customers wish to exempt their domains from the possible mail delivery delays that Greylisting may cause.
Whitelisting based on sender address (or sender domain), while easily implemented, is discouraged. The reasons for this are that in most cases, whitelisting the IP addresses of the mail hosts that send for a particular domain is a much better solution because it is much more difficult to forge the IP address than the sending email address. Also, in most cases, domains or emails that would be likely to be whitelisted would also be very easily guessed or discovered, and spammers could take advantage of that to bypass the Greylisting blocks.
Whether these manual whitelisting entries are stored in the database, or are hardcoded into the application does not matter from the standpoint of Greylisting. But of course, an implementation that allows them to be easily updated is preferable.
The specific methodology for a fairly basic Greylisting implementation is as follows:
There are a few issues that were found to be prevalent enough "in the wild" to make it necessary to slightly modify methods in the basic approach.
One issue is that some MTA software (Exim for example) attempts to limit the problem of forged sender addresses by attempting to verify that the claimed sender of an email is a valid address by doing an SMTP callback before accepting mail. Since it is desired to minimize the traffic when a mail may be rejected temporarily, the best course of action would be to issue a tempfail after the RCPT command. However, in the case of a SMTP callback, doing so at that point may cause our outgoing mail to be delayed unnecessarily.
Luckily, most mailers that do this use a sender address of the null sender "<>" to perform this check. This makes it fairly simple to workaround, since we can make a modification to the handling process so that in the special case of the null sender, we delay returning a temporary failure until after the DATA phase of a mail transaction. Since SMTP callbacks abort their test delivery attempt before getting to the data phase, the SMTP callback will succeed, and the outgoing mail should be accepted with no delay.
One mailer that seems to have a related problem is Postfix. Postfix breaks from the normal procedure of using the null sender for its callbacks, and instead uses a configurable sender address in the callback. I tried to get an explanation as to why Postfix didn't use the null sender like other mailers, and was informed that it was because some broken mailers don't accept the null sender even though it is required in the SMTP RFCs.
Unfortunately, this causes a problem when trying to work around the wierd behavior of Postfix. Luckily, the default setting for this address seems to be "postmaster", which leads to an acceptable workaround.
Another issue occurs when a large organization uses a pool of outbound mail servers for sending email to a system using Greylisting. If the pool is configured so that the same mailserver (with the same IP) will always retry deliveries for a particular mail, there is no issue.
But if that pool of mail servers happens to be configured in such a way that subsequent delivery attempts for a particular mail may be made from any one of several sending MTAs, then we have a possibility where legitimate mail deliveries may take significantly longer than expected. The possible maximum delay is dependant on the number of MTAs in the sending pool, and if the distribution of the retry attempts is random or deterministic. In a worst-case scenario, it is even possible that mail may be delayed long enough to cause it to bounce.
Other than adding a manual entry for networks of this type, one proposed method of dealing with this issue is to perform the IP address checks of the sending relay based on the subnet they are at rather than the specific IP. Since most of the sites that do this have most or all of their email servers on the same /24 subnet, this method works well in avoiding this issue without requiring manual intervention, at the expense of making it a little easier for spammers to circumvent the system.
One other potential issue is with mailing lists that use unique envelope sender addresses for mail sent to an end user, which is useful in order to better track bounces, since the formatting of bounces is not codified, and it is fairly common for mailers to return bounces that are formatted in such a way that it is very difficult, or even impossible, to programmatically discover which address caused the bounce.
This method of handling bounces is called VERP for Variable Envelope Return Paths, and one method of doing this is detailed here. Luckily, most mailing lists do this in a way similar to that described in that document, which is to use the same unique envelope sender for every mail sent to a particular recipient.
However, some mailing lists (such as Ezmlm) also try to track bounces to individual mails, rather than just individual recipients, which creates a variation on the VERP method where each email has it's own unique envelope sender. Since the automatic whitelisting that is built into Greylisting depends on the envelope addresses for subsequent emails being the same, this will cause each email sent to be delayed, rather than just the first email.
While tracking individual bounces may sound like a good idea, in todays internet age when we are trying to authenticate the senders of email, it's probably a bad idea. Hopefully, the Ezmlm maintainers will correct this issue.
There is a simple workaround, which is to manually whitelist any hosts that deliver this sort of traffic. But luckily, even without manual whitelist entries, the impact is not that significant since mailing lists are usually not that timely in their delivery anyway, and the delay will generally not be very significant for most users.
In the spirit of giving the mail system administrators who choose to implement Greylisting as much choice as possible, there are several options which should be easily modified in order to tune the behavior of the Greylisting method on a per-case basis. Below, we detail these options, and some details to keep in mind if it is deemed necessary to change them from the default suggested values.
As a matter of fact, it may be desirable to vary these settings from installation to installation, since it will help keep the spammers guessing.
Initial delay of a previously unknown triplet: 1 Hour
Lifetime of triplets that have not yet allowed a mail to pass: 4 Hours
Lifetime of auto-whitelisted triplets that have allowed mail to pass: 36 Days
The initial delay of 1 hour was picked for several reasons:
The data collected during testing showed that more than 99% of the mail that was blocked with the tested setting of 1 hour would still have been blocked with a delay setting of only 1 minute. However, it is expected that as spammers become aware of this blocking method, they will change their software to retry failed deliveries. At that point, having a larger initial delay will definitely help, as it gives time for other blocking methods to act. For this reason, it is suggested that at least a one hour delay value be kept as a default, since spammers will start adapting as soon as this method becomes known and starts being used.
It is important to keep this delay smaller than a value where a significant number of MTA's will give up and bounce the message. Luckily, most MTA's have failure timeouts of several days. However, there are some special cases like certain financial institutions who want to know that it wasn't delivered in a fairly short period of time. Even in these special cases, the timeouts should be at least a few hours.
It is likely that some form of traffic analysis will be developed using the data from a Greylisting database in order to automatically identify the IP addresses of hosts that are attempting to deliver spam.
While this sort of functionality is not currently included in the example implementation, I would be very interested in seeing this come about, since spammer patterns were usually very identifiable after a few minutes, mainly due to many nearly simultaneous delivery attempts to a large number of different recipients from the same IP address or group of IP addresses, from which no (or very little) previous traffic had ever been observed. (If the organizers or maintainers of any of the DNS blacklists are interested in creating an automatic way of using this data to help update their lists, please contact me.)
Unfortunately, pattern analysis requires a fairly high level of traffic to be useful and accurate, so smaller systems will probably not help much unless the pattern analysis is distributed, which is difficult when you can't necessarily trust other potential collaborators.
The 4 hour initial life of records was picked because:
(Note that in the example implementation, this 4 hour limit includes the initial 1 hour delay, which means the effective window when an email will be accepted is 3 hours.)
There is another reason why this delay should be a small as possible. If a spammer discovers and uses a poorly maintained relay host, hopefully it will bog that relay down enough so that it gets very slow. That increases the possibility that the relay will be slowed down enough that it won't be able to process the queue fast enough for the spam to get through within this time window.
The lifetime limit of whitelisted records is updated every time an email is successfully passed, and was chosen to be 36 days to:
Based on testing with the example implementation, over a testing period of about 6 weeks, we had raw numbers of:
So we have a better than 97 percent efficiency assuming that all email is spam, but it's actually better than that, since most of the email that got through was not spam. Unfortunately, telling exactly how much better we did is impossible without individually inspecting each email, which of course we did not do.
Now lets look at our inefficiency:
Unfortunately, this is a pretty poor number. But let's correct it a bit. Almost all of these delayed emails were mailing list traffic which used a unique id for the sender address (see above note regarding VERP). So if we disregard all triplets that passed only one email, we should exclude that type of traffic, and we get a new set of numbers:
This puts things in a much more favorable light, and merely disregards delays for emails that are generally not timely anyway.
Now let's see what effect greylisting would have on network bandwidth, based on some general averages.
These numbers are based on spam collected via various methods before the testing period. We picked these as nice round numbers that are pretty closely in line with analysis of previously seen spam. As for the SMTP overhead, in most cases it was less than 500 bytes, but we decided to err on the conservative side.
From this, it follows that for every spam blocked using Greylisting, we save enough bandwidth to "pay" for 10 deferred delivery attempts. If we total that up to give a real-world number (using the unadjusted numbers to give a worst case picture):
338018 (# spams) x 5000 bytes = 1.69 Gbytes of bandwidth saved
33586 (# blocks) x 500 bytes = 16.7 Mbytes of bandwidth wasted
This gives us a net gain of over 1.67 Gbytes of traffic that was saved by implementing Greylisting in our tests. And that's just on a fairly small site.
Greylisting will not be nearly as effective against spam unless ALL of the MX hosts for a particular domain use mail software that incorporates it.
A fair number of spamming software packages are already smart enough to retry delivery to other MX hosts for a domain if delivery through one MX fails. Since presumably all MX hosts will be whitelisted for each other (what is the point to delaying acceptance of email from a host that you know is a real MTA that will retry?) if the spammers can deliver to one of the MX's without a delay, then you have no more protection than you did before.
In addition, Greylisting, while already having a fairly minimal negative impact, can be made less intrusive if all of the MX hosts use a common database for tracking delivery attempts. To illustrate this, lets take an example where we have several hosts listed as mail exchangers for a domain, with seperate Greylisting databases.
A legitimate sending relay with a retry time of an hour attempts to deliver to one of the listed MX hosts. This host has never seen this triplet before, and so it generates a record in its own Greylisting database for the triplet, and refuses to accept the mail. An hour passes, and the sending MTA knows that the last attempt to deliver failed, so it decides not to retry delivery to the same MX host, and so it picks a different one and tries to deliver to it. This new MX host it picked is using a seperate database, and it does not know about the past attempt, and since it has not seen the triplet, it generates a new record in its own database for it, and refuses delivery again.
From this example, it can be seen fairly easily that there is the possibility that the delay in delivery of a legitimate piece of mail may get significantly longer than expected if there are enough MX hosts in the mix, even to the point that the sending server may give up and bounce the mail.
To avoid this possible problem, it is STRONGLY suggested that when there is a case of multiple MX hosts for a domain, they should all use a common database for tracking the mail triplets. There may be cases when the MX hosts are too widely seperated (network-wise) to be able to do this efficiently and robustly, but even in those cases it is possible that Greylisting will still be useful enough that this example worst-case scenario can tolerated or worked around to minimize the impact.
This section details a few of the most prevalent spammer attack methods that were observed during the testing period, and how the Greylisting system deals with them.
A significant number of spam emails specifically target non-primary MX hosts for domains, for the simple reason that backup MX servers will usually accept and relay all of the spam to the primary MX host without checking it, which reduces the load on the spammers system, requires little or no additional processing for mails that are rejected, and usually results in faster delivery transactions because the receiving system has to do less work (in the short term while the attack is occuring).
Greylisting handles this attack very well, since the whole point of the attack is to minimize bounces and delivery delays.
Many spammers are now resorting to "trolling", that is, sending spams to common usernames (tom@, harry@) at domains (also known as a dictionary attack), or sending to generated usernames made from real names harvested from other sources. They usually seem to be operating from a dictionary of common user names, but the "generated" usernames tactic may be getting more common.
The spammers probably use this method in order to reach people who have either taken steps to try to keep their email address from being harvestable from the web, or who are fairly novice users that may not have the resources or inclination to create their own web pages. Probably the latter case, since novice users are probably more likely to purchase something that has been advertised through spam.
This type of attack is very often combined with the non-primary MX attack, since most of these emails will result in bounces on domains that don't have a fairly large user population. Consequently, the spammers target the backup MX hosts. That way, they don't have to handle all the bounces and failures that these messages generate.
Greylisting handles these very well, since they almost always come from random short-lived dynamic IP addresses. And because most of these emails will ultimately generate bounces, it is costly for spammers to attempt redelivery of this type of attack. Also, since this attack is so distinctive (A high number of bounces generated in a short period of time from a particular IP address or set of addresses), it should be very easy to recognize and add to other blacklisting methods if given enough time to do so, which Greylisting provides.
Many spammer attacks seem to come in a pattern that looks very much like a moderated DDOS (Distributed Denial Of Service), lets call this type of spamming an "Organized Distributed Spammer Attack" (ODSA).
On the systems where spammer methods were evaluated, it was observed to be fairly common that there were spam delivery attempts that happened in a fairly short window of time, where the SMTP connections were originating from many different and seemingly unrelated IP addresses. Yet all of the envelope sender addresses were the same or similar, and the envelope recipient addresses were fairly sequential.
Obviously, Greylisting (as defined here) currently handles these attacks extremely well. However, if (when) the spammers adapt and learn to retry the delivery attempts, it may not be as effective by itself.
That being said, it is quite possible to adapt the Greylisting method to help thwart the described workaround. For example, at the cost of a little additional processing, it should be fairly simple to look at delivery attempts that have happened in a fairly recent time period, and after the first few attempts have been seen, submit all of the relays exhibiting this behavior to various blacklists as probable spam sites.
A significant portion of spam seems to come from relays that appear to be CacheFlow Server or other types of proxies. These can usually be identified by returning "CacheFlowServer" to an ident probe.
Greylisting will block these particular attacks completely, since those servers are not "real" MTA's, and will never retry.
Greylisting as proposed is fairly immune to possible routes of adaptation by spammers to get around the blocking. The possible methods of adaptation may make Greylisting by itself less effective, but the ways of getting around it will only make other spamblocking methods more effective.
The normal spammer behavior is to change IP's when normal IP blacklists have listed their current IP. Unfortunately for the spammers, changing their IP does not help with our delaying method, as every mail (and it's delay) is tied to the IP address of the sending relay. If the IP address changes, it effectively "resets" the timer on the delay, even if the envelope sender and recipient addresses stay exactly the same.
The other adaptation that is expected will result in the current versions of client spam software becoming obsolete, since most of those spamming applications are not intelligent enough to retry a delivery after getting any type of error. Spammers will be required to either use more intelligent software that retries, or to relay through smart relays.
We may see spammers gravitate toward using open third party relays, but most of them are already locked down or are quickly becoming so. Or, they may setup their own relays. In either case, it does nothing to negate the likelihood that those relays are or will quickly become listed in blacklists, thereby reducing their effectiveness for sending spam.
If spammers setup their own relays, the fact that email transmissions are delayed and that they may each take several attempts to deliver, only increases the storage and bandwidth requirements on the spammers side, which also raises the costs to the spammer. And if we can make it less profitable, then we are well on the way to solving the spam problem.
The delaying tactic that is the core of Greylisting may cause undesired delays if the host it is running on allows clients that will be using regularly changing IP's to relay mail through it. For example, if clients on non-local networks are allowed to relay through the server after doing a POP or IMAP auth, this implementation does not handle allowing these clients to deliver their mail for forwarding without incurring a probably undesired delay.
Workarounds for this issue exist, but are not implemented in the example code. Essentially all that is necessary to allow this without incurring a delay penalty is to simply insert a short-lived record into the Greylisting database at the same time that authorized relaying is enabled, which allows that originating IP address the ability to send mail for some small but sufficient amount of time.
Reception of mails from legitimate hosts that either do not pay attention to the temporary failure nature of the rejections, or never attempt any retries will be adversely affected by this system. Hopefully, any mailers that have these problems will be quickly fixed once Greylisting has been implemented at a significant number of sites.
Unfortunately, a few isolated systems with these issues have been discovered during testing. The affected systems either do a poor job of following the SMTP spec, or are outright violating it. Since SMTP is by nature an unreliable transport method, systems that do not retry deliveries are poorly advised and need to be fixed.
An SMTP session log generated by one specific example of a non-compliant MTA follows:
-> HELO somedomain.com <- 250 Hello -> MAIL FROM: <email@example.com> <- 250 2.1.0 Sender ok -> RCPT TO: <firstname.lastname@example.org> <- 451 4.7.1 Please try again later -> DATA <- 551 No valid recipients
From this, it is fairly obvious that the sending MTA did not check the status from the RCPT command, and continued on to issue DATA, which caused a permanent failure code to be issued, which is not a valid step when no recipients addresses have been accepted. In the case of this particular mailer, it did pay attention to the later 551 error code, which is considered a "permanent" failure code. This caused the message to be bounced back to the sender. But that is incorrect behavior because it failed to observe the earlier "temporary" failure and abort the transaction at that point.
The provided example implementation (available here) is a perl based milter for Sendmail, using version 0.18 of the Sendmail::Milter interface (also available from CPAN) and has been tested with Sendmail 8.12.9, though it should work with all versions of sendmail after 8.12.5. Sendmail::Milter requires a threaded perl installation and was tested with perl 5.8.0 (available from perl.org or from CPAN).
Also available are database definitions used for this implementation, and a sample configuration file. Since the implementation is in perl, it is easily modifiable. Not available on CPAN (yet...).
The database used was Mysql 3.23.54, though it should work with any later version, and most likely will work with earlier versions as well. In addition, the test systems were also using amavisd-new with the amavisd-new-milter interface, which was configured to do additional spamblocking with the help of Spamassassin 2.53.
In the interests of keeping the example implementation simple and easy to understand, some features that could easily be optimized have been left in their unoptimized state. Even so, during testing under heavy spam loads, the added time for the checks was unnoticeable in most cases, and in the remaining cases, the cause was due to network delays accessing the database (which was remotely hosted).
One detail of the implementation will probably strike horror in the hearts of diehard "structured" programmers. In several places, goto is used. Because of the way that the milter interface works, this seemed more straightforward than other methods.
Successful mails that have an envelope sender of the null sender are considered a special case where we will expire the record immediately in order to avoid whitelisting it, once we allow the mail to go through. Mails from the null sender are (according to RFC 821) only to be used for special administrative mails like bounces. Consequently, they are almost never used for more than one legitimate email. For that reason, there is no need to maintain them any longer once an email has been passed.
Unfortunately, many spammers are misusing this sender address because it generally won't generate a bounce from the recipient server (there's no point in generating a bounce message for a mail that is already a bounce). Expiring these records immediately helps limit the possibility that spammers using this sender address incorrectly can send multiple spams to the same recipient in a small time frame.
In addition, there are several other small features incorporated into the example implementation that are not part of the Greylisting system itself, but are attempts at enhancing or refining the general purpose of spam blocking.
The database layout used is not normalized. This was a conscious choice so that people who may not be that familiar with database design could more easily understand it. However, reworking the database implementation to normalize it should be fairly trivial.
One thing that is not incorporated is any kind of database maintenance. There is no provided method of inserting manual whitelisting entries other than the example sql statements in the above dbdef.sql file. I expect that eventually a nice web cgi for maintaining the database will be written, but haven't had time to create one yet. Or maybe someone will create one and share it.
If you are interested in the paper in its original first published form, the original can be found here.