BloodHound & Cypher Language

In my previous post, I covered the basics of BloodHound. In this post I will dive into the Cypher query language but we will focus on using it from an assessment/auditing angle – rather than as an attacker. Being able to quantify and detail the issues which exist in a large AD estate is an extremely powerful way of reducing the internal attack surface, forcing attackers (or red teamers!) to become more noisy.

Additionally BloodHound can help to focus remediation efforts, instead of blindly trying to remediate hundreds of users with weak passwords, BloodHound can show the level of access that each account has. This data can be analysed to allow prioritised remediation to be performed on the most powerful accounts first, helping to better secure your environment much faster!

What is Neo4j?

As a brief explainer, BloodHound is a nice GUI which sits on top of a neo4j graphing engine. Neo4j uses the ‘Cypher‘ query language, which we can then use to directly query the raw AD data which neo4j is storing. This allows us to produce more complex queries than are possible within the BloodHound GUI. In particular it allows us to generate tables and calculate certain values (i.e. How many users have admin access to another system? How many of them are kerberoastable?)

The query structure is slightly unusual, but it does have a fair amount of similarity to SQL. The main function we will use is the MATCH operator, which allows us to query the dataset in a similar way to SELECT would in SQL. To access the Neo4j console, we will navigate to localhost:7474 on our machine which is running Neo4j.

A Basic Query

We can run a basic query to pull back a single user with the following query:


Whilst this is quite simple, it does show that the syntax is very similar to SQL. If we look in the graph, Neo4j will return all the nodes which match this query. In this case it is just one account.

The u variable above will allow us to refer to all of the users impacted by the query above. For example, we can find all the users whose name begins with ‘BD’ using the query below. This will set u to represent all 5 users:


In order to return a single attribute from these users, we can change the value which we are returning. If we read the BloodHound documentation, there are a number of potential properties to return. We can also select the Table view to see all of the properties stored in the object:

We could then alter our query to just return the displayname field with the following query:

MATCH (u:User) WHERE STARTS WITH "BD" RETURN u.displayname

And we don’t have to just use Users, if we partially complete a query then Neo4j will suggest other types of we can use.

For example, we can return groups which begin with ‘IT’ by altering the query from MATCH (u:User) to MATCH (u:Group).


Analysing Relationships

This is all well and good, but the power of Neo4j comes from being able to identify relationships between nodes. To do this, lets find all the groups which BDELUNG00508 is within. To do this, we will need to search for Users which are a member of a group. This is done by using the following syntax:

MATCH (u:User)-[:MemberOf]->(g:Group)

This uses the ‘MemberOf’ edge in BloodHound to reveal users who are a member of a group. We will then need to filter this dataset down to only include the Brendan Delung user from our previous post, which we can do with the following command:


And finally, return the group names with


Giving us a final command of:

MATCH (u:User)-[:MemberOf]->(g:Group)

This is the equivalent command to the one we used in our original post, which can be represented by the following graph:

We can do some further queries to manipulate the data, for instance, what if we only want to return the nested groups?  To do this, we can use the *2.. Operator on the relationship section of the query to ensure that there are 2 consecutive MemberOf relationships. This will return the two groups on the right of the above image.

MATCH (u:User)-[:MemberOf*2..]->(g:Group)

Or if we want to ignore any of the groups beginning with “Operations“, then we can use the NOT operator:

MATCH (u:User)-[:MemberOf]->(g:Group)


If we want to represent any of these as a ‘path’ rather than a table, we can use the p= operator. For example, if we want to view the previous query as a path we would add p= to the beginning of the query and return the p variable.

MATCH p=(u:User)-[:MemberOf]->(g:Group)

We can run this query in BloodHound using the ‘Raw Query‘ tab at the bottom of the screen. This allows us to interact with the results using the BloodHound GUI, rather than Neo4j. Below we can see the query above but within the GUI.

Finally, using one of the previous queries we can use the COUNT operator to count the amount of results returned. We will use this extensively later on. In this case, we will count the number of groups which BDELUNG00508 is explicitly added to, ignoring any nested groups.

MATCH (u:User)-[:MemberOf]->(g:Group)

Assessing AD

Enough of the theory, lets start to combine some of these features together! In this section I will cover a range of queries which I have had success with previously. Most of these queries can be repurposed to cover whatever group of assets you are interested in. Common examples of groups might be:

  • AD Administrators/privileged accounts
  • Tier 0 or Tier 1 assets
  • Privileged Software users
    • ADCS
    • ADFS
    • SCCM
    • AV/EDR
    • Backup systems
    • Exchange Admins
  • Interesting user groups
    • Developers
    • SOC
    • Cloud Admins

Find All Nested Users For A Specific Group

This is handy to find all of the users for a specific AD group. By analysing multiple nested groups, we can find users who have access to our chosen group, but we might not have been aware of. This is very similar to the query used above, except we are finding the users in a group, instead of the groups a user is in. Also we are using the DISTINCT function for the output, to prevent duplicate results.

In this query, several other parameters are also returned which can be handy for further analysis. For example pwdlastset can help us find accounts which have old passwords, or the description field can shed light as to why this user has access to this group. In this example I am using the Domain Admins group, but it works well against systems such as Exchange with groups such as Exchange Recipient Administrators.

The 1..5 operator should be modified depending on the complexity of the environment. I would recommending starting at 1..2 or 1..3 (i.e. A maximum of 2 or 3 nested groups), as this will get exponentially more complex, and can cause BloodHound to crash if you arent careful!

MATCH (u:User)-[:MemberOf*1..5]->(g:Group)
RETURN DISTINCT( AS UserName, u.pwdlastset, u.lastlogon, u.description
ORDER BY u.pwdlastset ASC

Find Exploitable Edges To A Wildcard Group Name

This allows us to find users who have an exploitable relationship with an object which is then a member of a group of interest. An exploitable relationship in this case would mean that further actions need to be taken before the user actually has access to the group. For example, it might have an AddMember permission over a group – so whilst they dont currently have access to the group, they could add themselves into it.

In the example below, we are looking for users who could grant themselves access to an AD object, which is then in turn a member of a group with ‘Exchange’ in its name (WHERE =~ "(?i).*exchange.*").

This is handy to find users with dangerous misconfigurations which could lead to sensitive applications becoming compromised. This deliberately doesn’t include the MemberOf edge in the first section of the MATCH statement – as we are specifically looking for the more hidden misconfigurations.

MATCH (u:User)-[:AddMember|AddSelf|WriteSPN|AddKeyCredentialLink|AllExtendedRights|GenericAll|GenericWrite|WriteDacl|WriteOwner|Owns*1..4]->(o)-[:MemberOf]->(g:Group)
WHERE =~ "(?i).*exchange.*"

Find Users With Admin Access To Domain Controllers

For this query we specifically look for users with nested access to an object, which then has admin access onto computers within a group. This could be altered so that the group name is an Exchange Servers group, or DB servers etc.

MATCH (u:User)-[:AddMember|AddSelf|WriteSPN|AddKeyCredentialLink|AllExtendedRights|GenericAll|GenericWrite|WriteDacl|WriteOwner|Owns|MemberOf*1..4]->(o)-[:AdminTo]->(c:Computer)-[:MemberOf]->(g:Group)
RETURN DISTINCT( AS UserName, AS GrantingObjectName, AS DCName

With this query, we can see a number of users go via the Domain Admins group to get access to the FLLABDC Domain Controller. All of the users in this query are actually legitimate domain admin users, so we could extend the query to ignore any users who go via the Domain Admins group (As Domain Admin users will have admin access to Domain Controllers)

MATCH (u:User)-[:AddMember|AddSelf|WriteSPN|AddKeyCredentialLink|AllExtendedRights|GenericAll|GenericWrite|WriteDacl|WriteOwner|Owns|MemberOf*1..4]->(o)-[:AdminTo]->(c:Computer)-[:MemberOf]->(g:Group)
RETURN DISTINCT( AS UserName, AS GrantingObjectName, AS DCName

Find All Active ‘Decommissioned’ Objects

Focusing on the auditing side of BloodHound, we can search for all objects which mention they are disabled or decommissioned, but are in fact still active. This is very much matter of Active Directory hygiene, and is less about finding a 1337 way of compromising a domain – but you never know!

WHERE o.enabled = true AND (o.description =~ "(?i).*disabled.*" OR o.description =~ "(?i).*decom.*")
RETURN AS Name, o.description AS Description

This does rely on the description field containing either ‘disabled’ or ‘decom’, so it isn’t 100% accurate. You could also do a query to look at last login times and perform some sort of logic based on that.

Find The Most Dangerous ‘Decommissioned’ Objects

As a rough way of quantifying the most ‘dangerous’ decommissioned objects, we can perform a basic way of finding outbound access from any users we identified in the query above.

MATCH (o)-[:MemberOf*1..3]->(g:Group)-[r]->(n)
WHERE o.enabled = true AND (o.description =~ "(?i).*disabled.*" OR o.description =~ "(?i).*decom.*") AND r.isacl = true
RETURN AS Name, o.description AS Description, COUNT(DISTINCT(n)) AS OutboundAccess
ORDER BY OutboundAccess DESC

This will look for any groups which our ‘decommissioned’ objects are within, then find the number of objects which those groups can access. We could alter this to look for groups which grant local admin rights, or look for exploitable AD permissions (i.e. GenericAll, WriteDacl etc) rather than nested group membership (i.e. -[:MemberOf*1..3]-> )

Finding Legacy Privileged Accounts

We can leverage the highvalue attribute within BloodHound to find users of interest, and then use the pwdlastset attribute to find accounts with older passwords, which might not be subject to more modern password requirements and could well be very weak.

MATCH (u:User)-[:MemberOf*1..3]->(o)
WHERE o.highvalue = true AND u.enabled = true
RETURN DISTINCT( AS Name, u.pwdlastset AS PasswordLastSet
ORDER BY PasswordLastSet ASC

We could also modify this to find users who mistakenly have access to high value targets. If we assume that an internal naming scheme of T0_ is used for all Tier 0 accounts, we could ignore them in our query to find all users who arent Tier 0. For example:

MATCH (u:User)-[:MemberOf*1..3]->(o)
WHERE o.highvalue = true AND u.enabled = true AND NOT( STARTS WITH "T0_")

Evaluate Local Admin For A List Of Users

If you have a list of users of interest, you might want to evaluate how much onward administrative access they have. For example, users with weak passwords or legacy accounts which are due to be decommissioned. The query below is a basic way of performing this query.

MATCH (u:User)-[:MemberOf*1..3]->(g:Group)-[:AdminTo]->(c:Computer)
RETURN AS Username, COUNT(DISTINCT(c)) AS AdminCount

Which returns the number of systems which each user has local admin access to, showing that RSTITCH01791 is extremely powerful.

Evaluate Devices Which Allow Unconstrained Delegation

Unconstrained Delegation is a very dangerous attack primitive which can allow for a range of attacks. Therefore, it is essential that any devices which allow this attack are known about (and ideally removed!)

We can find devices which support it via the unconstraineddelegation attribute. For example, the query below will find users who are in nested groups, which have local admin access to a device which supports unconstrained delegation.

MATCH (u:User)-[:MemberOf*1..3]->(g:Group)-[:AdminTo]->(c:Computer)
WHERE c.unconstraineddelegation = true AND c.enabled = true AND u.enabled = true
RETURN AS ComputerName, COUNT(DISTINCT(u)) AS AdminCount

This could be extended to include any exploitable AD permissions (i.e. GenericAll, WriteDacl etc) rather than just group membership, but for now we can get a sense of how many users in our organisation have access:

This is the same data as from the Unrolled Admins view on an individual asset – except we can now find this value for every vulnerable device in the BloodHound dataset!

Finding Shortest Path To Unconstrained Delegation

Given our data above, we could simply begin remediation from the devices with the most admins to the least. Whilst this would be a valid way of evaluating the data, we should also consider other factors – such as how easy is it to gain admin access? For example, if one of the admins is kerberoastable then we are in big trouble!

We can check this with the following query:

MATCH p=shortestpath((u:User)-[*1..]->(c:Computer))
WHERE c.unconstraineddelegation = true AND c.enabled = true AND u.enabled = true AND u.hasspn = true

Which we will run in BloodHound’s GUI:

Now we can see that there are a range of kerberoastable users who eventually have access to systems which support unconstrained delegation – including routes via the Domain Admins group!


In summary, BloodHound is an extremely flexible way of evaluating an AD environment. The ‘standard’ version is excellent at allowing this point-in-time evaluation, with BloodHound Enterprise being far better suited to continuous evaluation.

There are an enormous amount of BloodHound queries both within the tool, and on GitHub which will show even more ways in which Cypher can be used and are a great way of understanding the Cypher language!

Further Reading

BloodHound Basics

Over the past months and years at $dayjob, I have done a lot of work with BloodHound to remove attack paths and improve the attack surface of our Active Directory environment. During this time I have found a number of ways to leverage BloodHound to perform what is effectively an audit of Active Directory, by identifying key attack paths and quantifying issues within large enterprise environments.

Initially, I found the more advanced query language (Cypher) to be quite complex, but it is very powerful and just happens to use a slightly different structure to other languages such as SQL.

To start, Ill generate a BloodHound dataset using the DBCreator script provided by SpecterOps. Following that, I will cover what Cypher is and explain some of its features in a later post. Finally, I will share some queries which can help to audit your environment.

For this guide, I wont cover what BloodHound is or the very basics of the program. There are other guides which already exist which do a great job of this, and the documentation is very thorough.

Environment Prep

Lets use the DBCreator script, but we will use byt3bl33d3r’s PR to fix some of the issues in the original version. This section does get a bit techy, so skip over the install section if you just want to learn about BloodHound!

I had a lot of issues getting this to work, so I simplified the usage of pickle in the MainMenu class. By changing the assignment to first_names and last_names to something simpler:

first_pickle = open("data/first.pkl",'rb')
last_pickle = open("data/last.pkl",'rb')

self.first_names = pickle.load(first_pickle)
self.last_names = pickle.load(last_pickle)


I also found the environment variables didn’t work, so I opted to clear them, and finally the group nesting function uses a hardcoded value (dept = group[0:-19]) for the length of the default ‘TESTLAB.LOCAL‘ domain name. I changed this to the value of self.domain + 6 to return the correct value & work as expected.

dept = group[0:-(len(self.domain)+6)]

The final group nesting logic is:

I will make a PR for this if I get around to it one day!

And lets load up BloodHound to verify it worked correctly:

And we get a pretty neat graph out of it when we run one of the pre-built queries – but more on this later on!

Basic Analysis

When we have our data loaded into BloodHound, we are presented with a view which shows all of the Domain Admins in the data we gathered. In my example, there are a lot of Domain Admins, so the graph is quite large!

We can click on any of these users to load details on that specific user. For example we can see that BDELUNG00508@HTTP418INFOSEC.COM is the account for Mr Brendan Delung.

We can use this to show some basic information on the user, such as their name (Brendan Delung), when they last logged in (Sat 19th November 2022)

If we scroll down a bit to the Group Membership section, we can see the First Degree Group Membership entry. This complex name is another way of saying the groups which this user is a member of. From when we first loaded up BloodHound, we know that Brendan is a member of the Domain Admins group (i.e. DOMAIN ADMINS@HTTP418INFOSEC.COM). From the screenshot below, we can see that Brendan is a member of 8 groups (including the Domain Admins).

If we click on this row, BloodHound will run a query in the background to show the groups which Brendan is a member of in the graph view. The view now shows us the groups:

Another option to represent the groups which a user is a member of is the Unrolled Group Membership, which is below the First Degree Group Membership feature we just used.

This takes the output from above, and then checks if any of these groups are within other groups and so on. Again, by clicking on the row we can see the graph which it creates, showing a further 2 groups which BDELUNG00508 is part of:

As we can see, the first ‘column’ of yellow nodes show the groups we could see before (Starting with OPERATIONS00039), but now we can see that the OPERATIONS0122 group is a member of another group (OPERATIONS00826), which itself is within another group (OPERATIONS01589)!

This shows the power of BloodHound, as running queries like this gets very complex with large environments. Whilst the output here is a little boring to us as an attacker, it becomes far more interesting if one of these unrolled groups has access which was not expected, such as local admin to a server.


BloodHound allows us to find paths between AD objects easily, using the ‘Pathfinding’ option in the UI.

If we click on this icon, we can now enter a ‘Start Node’ and ‘Target Node’ – in other words, where are we and where do we want to get to.

In the context of a red team, the Start Node could be a user who has been phished, and the End Node could be the Domain Admins group (Or whatever we want to ultimately compromise), which would show attack paths to obtain domain admin rights.

We can also fill this detail in by right clicking on a node and then selecting either ‘Set as Starting Node’ or ‘Set as Ending Node’.

To show this, we will use DBERENDT00668 as our starting point.

As we type in a group, BloodHound will autofill suggestions:

After some thinking, BloodHound will show us an ‘attack path’ – the steps we would need to take as an attacker to become a Domain Admin user.

To explain the above attack path, DBERENDT00668 user is a member of the IT00928 group. Members of this group can then RDP onto the COMP01364 server. This server then has a session for MSCHIVELY01554, who is a Domain Admin user.

If we wanted to learn more about any of these permissions, we can right click on the ‘edge’ (The line between the coloured nodes) and then click on ‘Help’.

This will then give a short overview on how it could be exploited:

High Value Groups

BloodHound has the concept of ‘High Value Groups’, which represent the traditionally highly powerful groups within Active Directory such as Domain Admins, Enterprise Admins and so on. In short, if any of these AD objects are compromised by an attacker, it is very bad news! In the graph view, these objects have a small diamond on the top right of their icon.

Owned Users

Another core concept is marking users as ‘owned’, which can be done by right clicking on a user and clicking on ‘Mark User as Owned’. This does two things:

  1. Marks the user object with a little skull symbol to show they are owned
  2. Allows us to filter on ‘owned’ users in our queries

BloodHound has a number of queries to search from users who are owned – for example the Shortest Paths to Domain Admins from Owned Principals query, which will search from every owned user to find the shortest route to becoming Domain Admin.

I have found this feature to be very useful when combined with other datasets. For example, if a password spraying or cracking exercise is performed, then any weak accounts could be marked as ‘owned’. We can then use Bloodhound to highlight the issues posed by these accounts in a really visual way – showing just how ‘close’ a weak account might be to becoming a domain admin!

Moving Laterally

Another key use case for BloodHound is for attackers, when they have first landed in an environment and are looking to move laterally. If we assume that we have infected the DBERENDT00668@HTTP418INFOSEC.COM user, it would be time consuming to establish our access purely through LDAP or PowerShell queries.

If we load up the user, we can see that they have a lot of interesting outbound access. In the screenshot below we will focus on the Execution Rights section of BloodHound. This shows the permissions that our user has. For example First Degree RDP Privileges will show the servers where our user has been explicitly granted access via RDP.

The Group Delegated RDP Privileges will show servers where our user is in a group (or nested groups) which has been granted access to a resource via RDP. More information on how this could be abused can be found on the BloodHound wiki.

If we click on the Group Delegated RDP Privileges entry above, BloodHound will again render this into a graph for us – showing that 6 different groups are granting access to servers via RDP for this user.

Custom Queries

Finally, at the bottom of the graph view is the ‘Raw Query’ tab, which allows us to run our own custom queries in the ‘Neo4j’ language – which we will cover in my post on the more advanced usage of BloodHound. This allows us to run far more complex queries and quantify a lot of the data in AD rapidly.

Attacking Password Managers: LastPass

This is the second post in a series I have done looking at password managers. My first post was on KeePass and covered some techniques which can be used against local password managers.

For this post, LastPass will be used as an example of a cloud-based password manager. In my opinion, these managers often have better UI/UX, so are easier for non-techy people to use.

LastPass has unfortunately had a *pretty rough* history, with several high-profile breaches in 2015, August 2022 and November 2022. Despite this, I would consider it as one of the most popular password managers – so it is worth using for this post!

There are several methods which I will cover here, most of which will apply to any cloud/browser-based password manager:

  1. Dumping Saved Credentials
  2. Dump The LastPass Vault From Memory
  3. Session Theft – Browser Pivoting
  4. Session Theft – Cookies via DPAPI
  5. Remote Debugging – Accessing The LastPass Vault
  6. Remote Debugging – Accessing Cookies Via The Debug WebSocket

I was only able to successfully access the vault through the ‘Remote Debugging – Accessing The LastPass Vault’ section. With the other techniques I was able to get to a position where I believe I had the correct cookies or position, but I was unable to successfully load the vault. I suspect this is due to LastPass performing host or session checking – but it could well also be due to me needing to get better at Pass-The-Cookie attacks!

Dumping Saved Credentials

To dump any saved Chrome passwords, we can use SharpChrome with the logins /password:PASSWORD parameters. This does require us to get the plaintext password of the user, but there are many ways of obtaining this 😉

To dump other Chromium based browsers such as Brave or Edge, we can use the /browser flag. For example, we would use the following command to interact with an Edge browser: SharpChrome.exe logins /password:PASSWORD /browser:edge

One thing to bear in mind with this approach is that LastPass will detect new sign in locations, such as different browsers, IP addresses and so on – so we cannot dump creds for LastPass and login directly to it!

Dump The LastPass Vault From Memory

For LastPass to work as an extension, it loads the vault into memory – but there are some weaknesses in the way in which it does this. Notably a BlackHat talk from 2015 covers this in technical detail if you are interested.

Luckily TrustedSec have automated a lot of this with their lastpass BOF, which is covered in an excellent 15-minute demo on YouTube. The BOF is part of their CS-Remote-OPs-BOF‘ repository

To start with, I will ensure that the LastPass extension is logged in but I will not have open in Chrome

I will then use Cobalt Strike to list the processes and pass the PIDs for Chrome processes into the lastpass BOF.

It will begin to parse memory and dump out any relevant strings, for example the master password:

As well as credentials from within the vault:

I did find that the BOF would only pull credentials from the vault after I had specifically searched for them in the extension, though it did appear as if LastPass have altered the way in which the extension works since the BOF was written, as the parsing was a little off when I used it. Nonetheless, this is a great BOF and serves as a very useful starting point for parsing Chrome’s memory.

Session Theft – Browser Pivoting

If our target has authenticated into LastPass, we can use Cobalt Strike’s Browser Pivot feature to leverage this already-established connection, but the target would have to be using Internet Explorer 😔.

We can use FoxyProxy to route our traffic through the HTTP proxy which CS has established

After adding an exception and relaxing rules on HSTS and the network.stricttransportsecurity.preloadlist option, this did partially load LastPass, but I had issues with it successfully loading the vault.

Session Theft – Cookies via DPAPI

I then turned to dumping values via DPAPI, which was a bit of a struggle. I believe that Chrome has altered the way in which it stores cookies, which prevents some of the ‘older’ tooling from working automagically. To counter this, I went to the Chrome/User Data folder on my test lab and searched for ‘cookie’, revealing the new location!

It turns out that the Cookie DB is within %LOCALAPPDATA%\Google\Chrome\User Data\Default\Network, which isnt where SharpChrome looks – most other blog posts mention to look in the \Default\Cookies folder. We can use this file path in SharpChrome to dump the cookies using the following command:

SharpChrome.exe cookies /target:"C:\Users\vagrant\AppData\Local\Google\Chrome\User Data\Default\Network\Cookies" /password:PASSWORD

Looking at the documentation, we need to provide this with the /statekey:X parameter. We can obtain these values by running SharpChrome.exe statekeys

We will use the value ***EC73E9 for the /statekey parameter, and will also use the /url parameter to only include cookies for lastpass. This leaves us with the final command:

execute-assembly /home/user/SharpChrome.exe cookies /target:"C:\Users\user\AppData\Local\Google\Chrome\User Data\Default\Network\Cookies" /password: /statekey:YADDAYADDA_EC73E9 /url:lastpass /format:json

This /format:json allows us to export directly into the Cookie-Editor extension.

Remote Debugging – Accessing The LastPass Vault

At this point I started looking into the remote-debugging-port technique which has been covered a lot recently. I then stumbled upon MDSec’s post which covered the technique below in far greater (and better) detail – go check out their post!

I found that using a redirector and the CS SOCKS proxy was very slow in my lab, even when using an interactive beacon so is probably too intensive for real world usage unless you absolutely need to! Ultimately I used Chisel to directly connect from the target to my team server, in the real-world you would want to ensure this is way more locked down!

To start, I spawned Chrome with chrome.exe --remote-debugging-port=10999. As covered elsewhere, there is a --headless argument which wont create a GUI window, but I had serious issues getting it to work!

Following a lot of errors, I realised we need to Chrome with the victims own user profile. I didn’t have success specifying a user profile directory, but did have success just using --profile-directory="Default". I believe that this profile directory value can change depending on how the target’s environment is configured – so this might change!

Using Chromium I used the flags described within the MDSec blog post to use the Chisel SOCKS proxy inside Chrome:

--proxy-server="socks5://" --proxy-bypass-list="<-loopback>"

This did appear to work on Chromium in Ubuntu 22.04 – but it would not successfully load the LastPass Vault for me. Instead I used a Windows VM with Chrome to more closely mimic the target user’s environment.

To get the remote debugging URL, we need to visit http://localhost:10999/json/list and get the devtoolsFrontendUrl value.

Then we can use the following magic URL from MDSec to spawn a new instance of the LastPass vault extension:


We then visit /json/list to see that it has spawned a new instance

Which we can then visit in our attackers browser which we started earlier.

The demo above will replicate any actions you take onto the targeted users desktop. This is has obvious, significant drawbacks so would require some *heavy* social engineering to work if you pursue this method without using the headless mode or another technique!

Ultimately, I couldn’t get this to work in headless mode through Firefox and Chromium on an Ubuntu machine. I suspect that this is a combination of:

  • LastPass is likely doing some sort of host checking, which fails when using a different host to the target (Windows/Chrome vs Ubuntu/Chromium&Firefox)
  • Using headless mode introduced other errors
  • Lack of ability

Remote Debugging – Accessing Cookies Via The Debug WebSocket

Accessing the vault via a GUI felt like a pretty long winded and noisy way of accessing LastPass. I did some hunting on Google and found a post by SpecterOps on dumping cookies via the debug port which we were using in the previous example.

This post mentions the CookieNapper tool by GreyCatSec, which uses WebSockets exposed by the debug port to simply query for all of the cookies. This has several advantages:

  • No need to use SharpChromium or load extra binaries
  • Invisible to the user
  • Should have less latency as the attack would require less usage of proxies

Using the SOCKS proxy we set up with Chisel, we can use the tool to dump cookies:

From here, we can use the EditThisCookie extension to import the cookies. Again I had issues getting access to the vault, but the cookies did appear to be successfully dumped.


In summary, I learned a lot digging into these 2 password managers – in no particular order:

  • KeePass has a number of pretty significant design flaws, even in the latest version
  • The backdooring of KeePass via the trigger system is pretty neat
  • Scanning memory is a really handy technique for password managers and programs such as these…
  • I need to improve at pass-the-cookie attacks
    • Or just don’t target LastPass I guess ¯\_(ツ)_/¯
  • Password managers are still a very good solution, and are hugely more secure than other solutions (Cough passwords.xlsx)

For what its worth, I think LastPass (or more broadly cloud-based password managers) are the best solution for most users. They tend to be much more usable than local applications such as KeePass, which should in theory encourage end users to use it more, thus making their credentials more likely to be more secure. For more technical or computer-savvy users, KeePass is ultimately more secure from an architectural point of view in my opinion.

That being said, cloud-based password managers are a huge target for attackers and you do end up putting a lot of trust into the provider, rather than an endpoint which is under your control. With LastPass now having suffered multiple breaches, this is an area which attackers will continue to target!

Further Reading