LDAP Filter Syntax Validation

Today I want to do a deep-dive into a change that will be released in 389 Directory Server 1.4.2. It’s a reasonably complicated change for our server, but it has a simple user interaction for admins and developers. I want to peel back some of the layers to explain what kind of experience, thought and team work goes into a change like this.

TL;DR - just keep upgrading your 389 Directory Server instance, and our ‘correct by default’ policy will apply, and you’ll keep having the best LDAP server we can make :)

LDAP Filters and How They Work

LDAP filters are one of the primary methods of expression in LDAP, and are used in almost every aspect of the system - from finding who you are when you login, to asserting you are member of a group or have other security attributes.

For the purposes of this discussion we’ll look at this filter:

'(|(cn=william)(cn=claire))'

In order to execute these queries quickly (LDAP is designed to handle thousands of operations per second) we heavily rely on indexing. Indexing is often a topic where people believe it to be some kind of “magic” but it’s reasonably simple: indexes are pre-computed partial result sets. So why do we need these?

We’ll imagine we have two entries (invalid, and truncated for brevity).

dn: cn=william,...
cn: william

dn: cn=claire,...
cn: claire

These entries both have entry-ids - these id’s are per-server in a replication group and are integers. You can show them by requesting entryid as an attribute in 389.

dn: cn=william,...
entryid: 1
cn: william

dn: cn=claire,...
entryid: 2
cn: claire

Our entries are stored in the main-entry database in /var/lib/dirsrv/slapd-standalone1/db/userRoot in the file “id2entry.db4”. This is a key-value database where the keys are the entryid, and the value is the serialised entry itself. Roughly, it’s:

[ ID ][ Entry             ]
  1     dn: cn=william,...
        cn: william

  2     dn: cn=claire,...
        cn: claire

Now, if we had NO indexes, to evaluate our filters we have to scan every entry of id2entry to determine if the filter matches. This algorithm is:

candidate_set = []
for id in id-min to id-max:
    entry = load_entry_by_id(id)
    if apply_filter(filter, entry):
        candidate_set.append(entry)

For two entries, this may be fast, but when you have 1000, 10.000, or even millions, this is extremely slow. We call these searches full table scans or in 389 DS, ALLIDS searches.

To make our searches faster we have indexes. An index is a mapping of a partial query term to an id list (In 389 we call these IDLs). An IDL is a set of integers. Our index for these examples would likely be something like:

cn
=william: [1, ]
=claire: [2, ]

These indexes are also stored in key-value databases in userRoot - you can see this as cn.db4.

So when we have an indexed term, to evaluate the query, we’ll load the indexes, then using mathematical set operations, we then produce a candidate_id_set, and we can then load the entries that only match.

For example in psuedo python code:

# Assume query is: (cn=william)

attr = filter.get_attr_name()
with open('%s.db' % attr) as index:
    idl = index.get('=william') # from the filter :)

for id in idl:
    ... # as before.

So we can see now that when we load the idl for cn index, this would give us the set [1, ]. Even if the database had 100 million entries, as our idl is a single value, we only need to load the one entry that matches. Neat!

When we have a more complex operation such as AND and OR, we can now manipulate the idl sets. For example:

(|(uid=claire)(uid=william))
   uid =claire -> idl [2, ]
   uid =william -> idl [1, ]

candidate_idl_set = union([2, ], [1, ])
# [1, 2]

This means again, even with millions of entries, we only need to load entry 1 and 2 to uphold the query provided to us.

So we finally know enough to understand how our example query is executed. PHEW!

Unindexed Attributes

However, it’s not always so easy. When we have an attribute that isn’t indexed, we have to handle this situation. In these cases, while we operate on the idl set, we may insert an idl with the value of ALLIDS (which as previously mentioned, is the “set of all entries”). This can have various effects.

If this was an AND query, we can annotate that the filter is partially resolved. This means that if we had:

(&(cn=william)(unindexed=foo))

Because an AND condition, both filter components must be satisfied, we have a partial candidate set from cn=william of [1, ]. We can load this partial candidate set, and then apply the filter test as in the full table scan case, but as we only apply it to a single entry this is really fast.

The real problem is OR queries. If we had:

(|(cn=william)(unindexed=foo))

Because OR means that both filter components could be satisfied, we have to turn unindexd into ALLIDS, and the result of the OR as a whole is ALLIDS. So even if we have 30 indexed values in the OR, a single ALLIDS (unindexed value) will always turn that OR into a full table scan. This is not good for performance!

Missing Attributes

So as a weirder case … what if the attribute doesn’t exist in schema at all? For example we could search for Microsoft AD attributes in 389 Directory Server, or we could submit bogus filters like “(whargarble=foo)”. What happens here?

Well, historically we treated these the same as unindexed queries. Which means that any term that is not in schema, would be treated as ALLIDS. This led to a “quitely known” denial of service attack again 389 Directory Server where you could emit a large number of queries for attributes that don’t exist, causing the server to attempt many ALLIDS scans. We have some defences like the allids limit (how many entries you can full table scan before giving up). But it can still cause entry cache churn and other performance issues.

I was first made aware of this issue in 2014 while working for University of Adelaide where our VMWare service would query LDAP for MS attributes, causing a large performance issue. We resolved this by adding the MS attributes to schema and indexing them so that they would create empty indexes - now we would call this in 389 Directory Server and “idl_alloc(0)” or “empty IDL”.

When initially hired by Red Hat in 2015 I knew this issue existed but I didn’t know enough about the server core to really fix it, so it went in the back of my mind … it was rare to have a customer raise this issue, but we had the work around and so I was able to advise support services on how to mitigate this.

In 2019 however, while investigating an issue related to filter optimisation, I was made aware of an issue with FreeIPA where they were doing certmap queries that requested MS Cert attributes. However it would cause large performance issues. We now had the issue again, and in a large widely installed product so it was time to tackle it.

How to handle this?

A major issue in this is “never breaking customers”. Because we had always supported this behaviour there is a risk that any solution would cause customer queries to “silently” begin to break if we issued a fix or change. More accurately, any change to how we execute the filters could cause results of the filters to change, which would disrupt customers.

Saying this, there is also precedent that 389 Directory Server was handling this incorrectly. From the RFC for LDAP it was noted:

Any assertion about the values of such an attribute is only defined if the AttributeType is known by the evaluating mechanism, the purported AttributeValue(s) conforms to the attribute syntax defined for that attribute type, the implied or indicated matching rule is applicable to that attribute type, and (when used) a presented matchValue conforms to the syntax defined for the indicated matching rules. When these conditions are not met, the FilterItem shall evaluate to the logical value UNDEFINED. An assertion which is defined by these conditions additionally evaluates to UNDEFINED if it relates to an attribute value and the attribute type is not present in an attribute against which the assertion is being tested. An assertion which is defined by these conditions and relates to the presence of an attribute type evaluates to FALSE.

Translation: If a filter component (IE nonexist=foo) is in a filter but NOT in the schema, the result of the filter’s evaluation is an empty-set aka undefined.

It was also clear that if an engaged and active consumer like FreeIPA is making this mistake, then it must be overlooked by many others without notice. So there is sometimes value in helping to raise the standard so that everyone benefits, and highlight mistakes quicker.

The Technical Solution

This is the easy part - we add a new configuration option with three states. “on”, “off”, “warn”. “On” would enable the strictest handling of filters, rejecting them an not continuing if any attribute requested was not in the schema. “Warn” would provide the rfc compliant behaviour, mapping to empty-set index, and notifying in the logs that this occured. Finally, “off” would be the previous “silently allow” behaviour.

This was easily achieved in filter parsing, by checking the attribute of each filter component against our schema hashmap. We then tag the filter element, and depending on the current setting level reject or continue.

In the query execution code, we now check the filter tag to understand if the attribute is schema present or not. If it’s flagged as “undefined”, then we immediately shortcut to return idl_alloc(0) instead of returning ALLIDS on the failure to find the relevant index db.

We can show the performance impact of this change:

Before with non-existant attribute

Average rate:    7.40/thr

After with “warn” enabled (map to empty set)

Average rate: 4808.70/thr

This is a huge improvement, and certainly shows the risk of DOS and how effective the solution was!

The Social Solution

Of course, this is the hard part - the 389 Directory Server team are all amazingly smart people, from many countries, and all live around the world. They all care that the server is the best possible, and that our standards as a team are high. That means when introducing a change that has a risk of affecting query result sets like this, they pay attention, and ask many difficult questions about how the feature will be implemented.

The first important justification - is a change like this worth while? We can see from the performance results that the risk of DOS is reasonable, so the answer there becomes Yes from a security view. But it’s also important to consider the cost on consumers - is this change going to benefit FreeIPA for example? As I am biased being the author I want to say “yes” - by notifying or rejecting invalid filters earlier, we can help FreeIPA developers improve their code quality, without expecting them to know LDAP inside and out.

The next major question is performance - before the feature was developed there is clearly a risk of DOS, but when we implement this we are performing additional locking on the schema. Is that a risk to our standalone performance or normal operating conditions. This had to also be discussed and assessed.

A really important point that was raised by Thierry is how we communicated these errors too. Previously we would use the “notes=” field of the access log. It looks like this:

conn=1 op=4 RESULT err=0 tag=101 nentries=13 etime=0.0003795424 notes=U

The challenge with the notes= field, is that it’s easy to overlook, and unless you are familar, hard to see what this is indicating. In this case, notes=U means partially unindexed query (one filter component but not all returned ALLIDS).

We can’t change the notes field due to the risk of breaking our own scripts like logconv.pl, support tools developed by RH or SUSE, or even integrations to platforms like splunk. But clearly we need a way to detail what is happening with your filter. So Thierry suggested an extension to have details about the provided notes. Now we get:

conn=1 op=4 RESULT err=0 tag=101 nentries=13 etime=0.0003795424 notes=U details="Partially Unindexed Filter"
conn=1 op=8 RESULT err=0 tag=101 nentries=0 etime=0.0001886208 notes=F details="Filter Element Missing From Schema"

So we have extended our log message, but without breaking existing integrations.

The final question is what our defaults should be. It’s one thing to have this feature, but what should we ship with? Do we strictly reject filters? Warn? Or disable, and expect people to turn this on.

This became a long discussion with Ludwig, Thierry and I - we discussed the risk of DOS in the first place, what the impact of the levels could be, how it could break legacy applications or sites using deprecated features or with weird data imports. Many different aspects were considered. We decided to default to “warn” (non-existant becomes empty-set), and we settled on communication with support to advise them of the upcoming change, but also we considered that our “back out” plan is to change the default and ship a patch if there is a large volume of negative feedback.

Conclusion

As of today, the PR is merged, and the code on it’s way to the next release. It’s a long process but the process exists to ensure we do what’s best for our users, while we try to balance many different aspects. We have a great team of people, with decades of experience from many backgrounds which means that these discussions can be long and detailed, but in the end, we hope to give what is the best product possible to our community.

It’s also valuable to share how much thought and effort goes into projects - in your life you may only interact with 1% of our work through our configuration and system, but we have an iceberg of decisions and design process that affects you every day, where we have to be responsible and considerate in our actions.

I hope you enjoyed this exploration of this change!

References

PR#50379