Author Archive: SEC-LABS R&D

ADCS Auditing for MDI

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certutil –setreg CA\AuditFilter 127 

net stop certsvc && net start certsvc

Configure auditing on the configuration container

Open ADSI Editor

Right click on the configuration Node (CN=…) and select Properties > Security Tab > Advanced

MAKE SURE YOU SELECT THE AUDIT TAB!!

Click Add (VERY IMPORTANT THAT YOU ARE IN AUDIT TAB)

Select principal Everyone, Type: All And Applies to: This object and all descendant objects

Scroll to the bottom and click “Clear” and back up and check the Write all properties

Defender for Identity no longer requires logging 1644 events. If you have this registry setting enabled, you can remove it.

Happy Hunting!

Graph semantics in Kusto

Earlier this month, the Kusto team announced graph semantics feature in Kusto.

This Kusto extension makes it possible to contextualize data in graphs which consists of Nodes and Edges that can be connected. These connections can visualize the relationships between the Nodes.

To describe graph semantics in a none-tech scenario, the best way is to look at social connections where people have connections, one to many, many to many and many to one

let Users = datatable (UserId:string , name:string)[
"1","Mattias",
"2","XboxController",
"3","Stefan",
"4","XBOX"
]; // nodes
let Knows = datatable (FirstUser:string , SecondUser:string)[
"1","2",//Mattias knows xbox controller
"2","4",//Xbox controller knows xbox
"1","3"//Mattias knows Stefan
]; // edges
Knows
| make-graph FirstUser --> SecondUser with Users on UserId
| graph-match (user)-->(middle_man)-->(friendOfAFriend)
    project SecLabs_person = user.name, middle_man = middle_man.name, kusto_friend_of_friend = friendOfAFriend.name

What can it use it for?

  • Many-to-many relations
  • Hierarchical data
  • Networked relationships
  • Social Networks
  • Recommendation systems
  • Connected assets
  • Knowledge graphs

From a hunting perspective, we can connect systems in a unspecified amounts of times. Since we can’t to recursive join, we can use graph to connect unknown number of systems.

Time-aware

In a scenario where we have lateral movement, we could connect all devices involved and at what time (of course depending on the data source, but if we have data from Microsoft Defender XDR data with network data from the endpoint sensor and know about activities involving certain ports, 3389 for example) we could draw all show how and when the threat actor moved laterally.

For further information, please visit: https://learn.microsoft.com/en-us/azure/data-explorer/graph-overview

For Kusto training, CTF mode, Kusto Detective: https://detective.kusto.io/

Threat Hunting and the New Hunts in Sentinel

Establishing a Proactive Hunting program is something which is useful and necessary today.

From working with proactive threat hunting for a long time, from when data was not available at your fingertips, things has become a lot easier in the era of SIEM and over the last years, EDR and XDR.

The technical part is the easy one. The process of establishing your hunting and connect it with existing processes is usually what’s difficult.

What is proactive Hunting?

First, what is threat hunting?

To dive in to this topic I want to point out activities that are somewhat related

The custom detections or scheduled rules is pretty clear what it is. but Tasks is something which sits in between Proactive Threat Hunting and Custom Detections.
Tasks are things discovered in Hunting, and data of interest but cannot be set as a custom detection as of yet.

The reasons could be many, but for example to noisy data and no good correlation values or near-time events that can be used to reduce False-Positives. Even though we have finalized our hypothesis from threat hunting we might want to follow-up on the events, maybe on a daily basis, but we cant have it scheduled since it will give an incident fatigue.

To summarize tasks, it’s queries that might make it to a detection but for now we run it automatically or manual and do manual review on the result on a daily schedule.

To iterate back to threat hunting before we take a look at the hunts feature in Sentinel

Threat Hunting can be divided into 2 main pillars; Proactive and Reactive hunting

Proactive Threat Hunting is when you don’t know something is going on, like playing hide-n-seek, except for that you don’t know anyone is playing with you

Reactive Threat Hunting is post breach/post incident and you use threat hunting to find the outer boundaries of the incident, which could be other devices communicating with a specific IP, list all process communicating, find the same processes on other devices, and then which IP’s they are communicating with.

In the EDR era or now, the XDR era, threat hunting becomes easier on a technical level. The data collection happens automatically for many workloads and not only collected, it’s streamed.

With the power of Kusto Query Language, you can do advanced aggregations, anomaly calculations and visualizations to truly crunch and bend your data.

In Proactive Threat hunting, you will start your assignment by defining your Hypothesis, which could be something like “I would like to see if any local users have been added outside the process”

Then you check what ever data you need to discover such activities (DeviceEvents/DeviceProcessEvents hint hint) and you continue to develop the query for is and document your result.

Here is what makes Hunts feature so great. It actually allows for process integration, like we have in Microsoft 365 Defender where we can create incidents based on the results and have it handled by the incident process.

Hunts in Sentinel

Common use cases:

  • Proactively hunt based on specific MITRE techniques, potentially malicious activity, recent threats, or your own custom hypothesis.
  • Use security-researcher-generated hunting queries or custom hunting queries to investigate malicious behavior.
  • Conduct your hunts using multiple persisted-query tabs that enable you to keep context over time.
  • Collect evidence, investigate UEBA sources, and annotate your findings using hunt specific bookmarks.
  • Collaborate and document your findings with comments.
  • Act on results by creating new analytic rules, new incidents, new threat indicators, and running playbooks.
  • Keep track of your new, active, and closed hunts in one place.
  • View metrics based on validated hypotheses and tangible results.

In Sentinel go to Hunting and Hunts

Here is the list of previous hunts.

If you select New Hunt we can create a new

You can add the Hypothesis and choose if it’s Validated or not (which can be set later in the process by another Threat hunter)

When we have our new Hunting campaign, we can add queries

Adding Tactics and techniques and map entities

It’s possible to create incidents from the results to map to the incident process, and we can also start automated playbooks (entity-based) from the entity pane.

Summary

There are so many details on this feature and it has some many capabilities. The best part is that it has the process of hunting as its core. Now it’s easier to deliver threat hunting, as a service, as a consultant or if you work internally for an organization.

Happy Hunting

Force release from isolation in MDE

It rarely happens, but if it happens, you have a solution…

One of the best response actions in Microsoft Defender for Endpoint (A part of Microsoft 365 Defender) is isolate device. This locks the device in the network stack and will connect any threat actor immediately from the device, or stop the user from doing what they are doing.

This action do have some extra great features. For instance, it will allow connection to the Defender backend which allow SecOps to continue to monitor, run live response (another action which gives SecOps shell access to the endpoint) to further analyze any suspicious behavior.

So what’s force release from isolation?

force release from Isolation is a batch script which will add some registry values to the endpoint to force it to release from isolation. This could be used if something happens on the network side where the endpoint is connected or if there is any other error that could break the release from isolation function from the portal.

Even though it’s very rarely necessary, it’s great to have such feature if something happens.

Downloading script

  • Go to device page and click on the more actions menu
  • Select force release from isolation
  • Run the script with administrative privileges

Script information

  • The script can only be downloaded from the Defender portal ( https://security.microsoft.com )
  • The script is only working for 3 days after download
  • The script is only working for the specific endpoint you download it for
  • Must be executed with local admin privileges

Minimum Requirements

  • Supports only Windows
  • The following Windows versions are supported:
    • Windows 10 21H2 and 22H2 with KB KB5023773
    • Windows 11 version 21H2, all editions with KB5023774
    • Windows 11 version 22H2, all editions with KB5023778

for further information, please visit https://learn.microsoft.com/en-us/microsoft-365/security/defender-endpoint/respond-machine-alerts?view=o365-worldwide#forcibly-release-device-from-isolation

Happy Hunting!

3 common Defender for Endpoint configuration errors

Both Stefan and Mattias from the Sec-Labs team, have conducted numerous Defender for Endpoint health checks. Apart from other configuration errors, three settings are frequently misconfigured, affecting overall security.

When we discussed this, we realized we should probably write a full configuration recommendation. But let’s start with these 3 settings that are related to each other

Number one – Cloud Protection turned off or not correct cloud block protection

Cloud protection extends the threat detection capabilities in the antimalware engine and is used to enhance real-time protection with the strength of cloud. It’s a common observation in the health checks that this is turned off or not configured to block at the correct level.

The following table contains information about the components in cloud protection and comes from an older blog from Microsoft https://www.microsoft.com/en-us/security/blog/2019/06/24/inside-out-get-to-know-the-advanced-technologies-at-the-core-of-microsoft-defender-atp-next-generation-protection/

FeatureDescription
Cloud-side
Metadata-based ML engineSpecialized ML models, which include file type-specific models, feature-specific models, and adversary-hardened monotonic models, analyze a featurized description of suspicious files sent by the client. Stacked ensemble classifiers combine results from these models to make a real-time verdict to allow or block files pre-execution.
Behavior-based ML engineSuspicious behavior sequences and advanced attack techniques are monitored on the client as triggers to analyze the process tree behavior using real-time cloud ML models. Monitored attack techniques span the attack chain, from exploits, elevation, and persistence all the way through to lateral movement and exfiltration.
AMSI-paired ML enginePairs of client-side and cloud-side models perform advanced analysis of scripting behavior pre- and post-execution to catch advanced threats like fileless and in-memory attacks. These models include a pair of models for each of the scripting engines covered, including PowerShell, JavaScript, VBScript, and Office VBA macros. Integrations include both dynamic content calls and/or behavior instrumentation on the scripting engines.
File classification ML engineMulti-class, deep neural network classifiers examine full file contents, provides an additional layer of defense against attacks that require additional analysis. Suspicious files are held from running and submitted to the cloud protection service for classification. Within seconds, full-content deep learning models produce a classification and reply to the client to allow or block the file.
Detonation-based ML engineSuspicious files are detonated in a sandbox. Deep learning classifiers analyze the observed behaviors to block attacks.
Reputation ML engineDomain-expert reputation sources and models from across Microsoft are queried to block threats that are linked to malicious or suspicious URLs, domains, emails, and files. Sources include Windows Defender SmartScreen for URL reputation models and Office 365 ATP for email attachment expert knowledge, among other Microsoft services through the Microsoft Intelligent Security Graph.
Smart rules engineExpert-written smart rules identify threats based on researcher expertise and collective knowledge of threats.
Client-side
ML engineA set of light-weight machine learning models make a verdict within milliseconds. These include specialized models and features that are built for specific file types commonly abused by attackers. Examples include models built for portable executable (PE) files, PowerShell, Office macros, JavaScript, PDF files, and more.
Behavior monitoring engineThe behavior monitoring engine monitors for potential attacks post-execution. It observes process behaviors, including behavior sequence at runtime, to identify and block certain types of activities based on predetermined rules.
Memory scanning engineThis engine scans the memory space used by a running process to expose malicious behavior that may be hiding through code obfuscation.
AMSI integration engineDeep in-app integration engine enables detection of fileless and in-memory attacks through Antimalware Scan Interface (AMSI), defeating code obfuscation. This integration blocks malicious behavior of scripts client-side.
Heuristics engineHeuristic rules identify file characteristics that have similarities with known malicious characteristics to catch new threats or modified versions of known threats.
Emulation engineThe emulation engine dynamically unpacks malware and examines how they would behave at runtime. The dynamic emulation of the content and scanning both the behavior during emulation and the memory content at the end of emulation defeat malware packers and expose the behavior of polymorphic malware.
Network engineNetwork activities are inspected to identify and stop malicious activities from threats.

When it’s configured (which is normally default, but should be verified that no policy is actually disabling it) the cloud block level must be configured.

The following blocking levels are available:

SettingDetails
Default blocking levelprovides strong detection without increasing the risk of detecting legitimate files
Moderate blocking levelprovides moderate only for high confidence detections
High blocking levelapplies a strong level of detection while optimizing client performance (but can also give you a greater chance of false positives)
High + blocking levelapplies extra protection measures (might affect client performance and increase your chance of false positives)
Zero toleranceblocking level blocks all unknown executables

It is recommended to at least be on the High blocking level

For more information about cloud block level and how to configure it, please visit https://learn.microsoft.com/en-us/microsoft-365/security/defender-endpoint/specify-cloud-protection-level-microsoft-defender-antivirus

As shown in below picture, the cloud protection feature is really important

More information about Cloud protection is available here: https://learn.microsoft.com/en-us/microsoft-365/security/defender-endpoint/cloud-protection-microsoft-defender-antivirus

Number two – Sample submission

If a suspicious is detected and depending on setting, a sample is sent to the cloud service for analysis while Microsoft Defender Antivirus blocks the file. As soon as a determination is made, the file is either released or blocked by the AV.

How does this process work? Cloud protection and sample submission at Microsoft Defender Antivirus | Microsoft Learn

  1. Client-based machine-learning models, blocking new and unknown malware.
  2. Local behavioral analysis, analyze and stopping file-based and file-less attacks.
  3. Antivirus, detecting common malware through generic and heuristic techniques.
  4. Cloud-based protection is provided for cases when Antivirus running on the endpoint needs more intelligence to verify the intent of a suspicious file
  5. file metadata is sent to the cloud protection service. Often within milliseconds, the cloud protection service can determine based on the metadata as to whether the file is malicious or not a threat.
  6. The cloud query of file metadata can be a result of behavior, mark of the web, or other characteristics where a clear verdict isn’t determined.
  7. A small metadata payload is sent, with the goal of reaching a verdict of malware or not a threat. The metadata doesn’t include personally identifiable information (PII). Information such as filenames, are hashed.
  8. Can be synchronous or asynchronous. For synchronous, the file won’t open until the cloud renders a verdict. For asynchronous, the file opens while cloud protection performs its analysis.
  9. Metadata can include PE attributes, static file attributes, dynamic and contextual attributes, and more

For the settings…

The Sample submission has 4 different settings. In the health checks, a common observation is that it is disabled (do not send) or always prompt.

For cloud protection to work properly, it must be configured to Send all Samples automatically or (at least) Send safe samples automatically.

To stress further, you will not have full protection if this is configured in the wrong way!

Se below for details.

  1. After examining the metadata, if Microsoft Defender Antivirus cloud protection can’t reach a conclusive verdict, it can request a sample of the file for further inspection. This request honors the settings configuration for sample submission:
    1. Send safe samples automatically
      • Safe samples are samples considered to not commonly contain PII data like: .bat, .scr, .dll, .exe.
      • If file is likely to contain PII, the user gets a request to allow file sample submission.
      • This option is the default on Windows, macOS, and Linux.
    2. Always Prompt
      • If configured, the user is always prompted for consent before file submission
      • This setting isn’t available in macOS and Linux cloud protection
    3. Send all samples automatically
      • If configured, all samples are sent automatically
      • If you would like sample submission to include macros embedded in Word docs, you must choose “Send all samples automatically”
      • This setting isn’t available on macOS cloud protection
    4. Do not send
      • Prevents “block at first sight” based on file sample analysis
      • “Don’t send” is the equivalent to the “Disabled” setting in macOS policy and “None” setting in Linux policy.
      • Metadata is sent for detections even when sample submission is disabled
  2. After files are submitted to cloud protection, the submitted files can be scanneddetonated, and processed through big data analysis machine-learning models to reach a verdict. Turning off cloud-delivered protection limits analysis to only what the client can provide through local machine-learning models, and similar functions.

Number 3 – Extended cloud block timeout

The last feature in this post, is the extended cloud block timeout.

If you read the whole post, you have probably noticed that all these 3 features should be configured together since they depend on each other to better protect the endpoint.

While the cloud protection is analyzing the file that was found suspicious, antivirus can block the file from running. As default, it will prevent the file from running for 10 seconds but this can be configured to up to 50 seconds (plus the 10 default seconds) to allow cloud protection to do more analysis like detonation before the file is released.

This happens on suspicious files, and should not have impact on end users!

Block at first sight and its prerequisites must be enabled before you can specify an extended timeout period.

https://learn.microsoft.com/en-us/microsoft-365/security/defender-endpoint/configure-cloud-block-timeout-period-microsoft-defender-antivirus

Happy Hunting!

The Security Dojo Podcast

Stefan and I have talked about starting a podcast for a long time and now we have, together with Pierre Thoor, released episode 1 of the Security Dojo.

Our first episode is about the podcast itself and we’re also touching previous and upcoming events.

The plan is to invite guests and talk about news and things we find interesting. We appreciate feedback

You can listen to the pod here

Apple podcast:
https://podcasts.apple.com/us/podcast/security-dojo/id1686552862
Spotify:
https://open.spotify.com/show/52EVzPioqoALzeFyou6mM6

Embedded player (Spotify):

Website:
https://securitydojo.ms
LinkedIn profile:
https://www.linkedin.com/company/security-dojo-podcast

Happy Hunting!

Microsoft Sentinel Parsing tips – Whitespace control

This post will be a part of a multiple posts to cover data parsing in Microsoft Sentinel.

Intro

Kusto is a powerfull query language and easy to adopt.

Even if Kusto is very powerfull, working with custom log sources is, sometimes, a mess. Some parsers requires more effort and some are very simple.

In general, when it’s possible to use operators like “parse” (link) function or “parse-kv” (link) it’s very welcome. However, the reality has a different challenge for us.

In this post we want to share a quick pro tip to solve the mystic of hidden whitespaces

The challenge of whitespaces

Whitespaces ” ” exists everywhere, the challenge is how it’s presented in log analytics.

Log analytics does a lot for the user in terms of nicely present data. It actually removes duplicate whitespaces, as well as leading and trailing whitespace.

This could result in problems like failing parsers, regex and string operators like “==”, “startswith”, “endswith” etc will fail. Especially if it’s not consistent.

Marking the string in the output view does not show the extra whitespaces

Copying the text and paste into a text-editor will not show it either like in below example where we copied the output into VS Code (we can only see one dot to show one whitespace between foo and bar)

However, the double whitespaces are interpreted during execution, and it’s only in the presentation view the extra is removed. As in below example, we used split on ” ” to show the existence of the double whitespace.

When working with multiple log sources you don’t want to search and see if they exist (which may change during the log source life cycle), you rather want a way to always make the log to look good in your parser.

Solution

To properly address this (if there aren’t any good ways to change the audit settings of the system sending the logs)

To handle the duplicate white spaces we use the replace_regex function (link here) and use the whitespace “\s” with the quantifier “+” which means one or multiple times and replace it with a space ” “.

This will search for spaces (one or more) and replace it with a single, because we don’t want to remove single spaces. And by using the same column name “SyslogMessage” we will actually reuse the same column for our clean output.

Please note that this will not change the message in the database, only during execution.

Doing this gives us the following output.

The next step is that we want to remove the leading and trailing whitespaces. If we for instance expect the first character to be a value, the leading whitespace could make our parser to fail or an analytic rule.

We have seen occasions where this happens from time to time and not all messages in a log source.

To fix the leading and trailing whitespaces we use another regex to look for start of string and end of string. But this time we want to replace with “nothing/null” which is why we can’t use this regex in the first cleaning.

In the second run we use the same column name again to cleanup the SyslogMessage. There is a best practice to always keep the original message, however, this is to solve an error from the log source and not to alter the SyslogMessage.

The regex starts with an anchor “^” to define the start of the string and followed by a whitespace “\s” since we cleaned all double whitespaces we don’t need to use the quantifier. To handle the trailing whitespace we use the OR operand “|” and check for a whitespace “\s” followed by the anchor “$” to determine the end of the string. If we get any hits it will be replaced with null and we have a clean string.

By adding these 2 lines of code to the parser, we will avoid running into strange issues which could take some time to troubleshoot.

//Sample
CustomLogSource_CL
| extend SyslogMessage = replace_regex(SyslogMessage,@"\s+",@" ") //Remove duplicate whitespaces
| extend SyslogMessage = replace_regex(SyslogMessage,@"^\s|\s$",@"") //Remove leading and trailing whitespaces

Happy Hunting!

Automated attack disruption of Ransomware and BEC – public preview

Automated attack disruption of Ransomware and BEC (Business email compromise uses high-confidence Extended Detection and Response (XDR) signals across all workloads; endpoints, identities, email, and SaaS apps, to contain the threat quickly and effectively, to stop further impact.

These 2 scenarios are common attacks and it’s really great that they are supported by the feature in Microsoft 365 Defender.

Business Email Compromise, BEC

Threat actors are impersonating executives to trick, for example, Economic department to transfer money by impersonating the CFO or the CEO.

Automatic attack disruption can help to detect these attacks and remove the access to the accounts by disabling the compromised account, limiting their ability to send fraudulent email

Human-operated ransomware, HumOR

This attack, commonly used today, is devastating for an organization. The threat actors has full control of the environment and have usually controlled the environment for some time.

The challenge from a SecOps perspective is to be fast enough to respond to the incidents and mitigate accounts and the devices fast enough.

When the threat actors has gained privileged access, things move very quick and automatic attack disruption will contain the spreader device and disable the compromised user account

Automatic attack disruption operates in 3 key stages

  • Detect malicious activity and establish high confidence
  • Classification of scenarios and identification of assets controlled by the attacker
  • Trigger automatic response actions using the Microsoft 365 Defender protection stack to contain the active attack

First the detection will happen, which is achieved by AI, research-information etc.., to establish a high level of confidence in accurately detecting ransomware spread and encryption activity. The XDR-level capability correlates insights across endpoints, identities, email and SaaS apps to establish high-fidelity alerts.

A second stage will aggregate automatic analyze the activities like tampering, backup deletion, credential theft, mass lateral movement and many more to flag the assets included in the chain and trace the activities back to the remote execution TTP

Distrupting the attack

Response actions against the entities which are identified as compromised and in the public preview these two are the main actions:

When this happens, it will be visible in the:

Incident queue

  • A tag titled “Attack Disruption” next to affected incidents

If you really must exclude some user from the automatic attack disruption, then you can do it in the MDI settings

Incident page

  • A tag titled “Attack Disruption”.
  • A yellow banner at the top of the page that highlights the automatic action taken.
  • The current asset status is shown in the incident graph if an action is done on an asset, e.g., account disabled or device contained.

Some thoughts

It’s important that prerequisites are fixed, like MDI Action Account (if not using built-in system account) and

For further reading, please visit
Automatic attack disruption in Microsoft 365 Defender | Microsoft Learn

Stay safe, Protect the world and Happy Hunting!

Near Real Time Rules in Defender

If you want to find threats earlier it is now possible to use NRT rules in Defender.

Before this, we had the options to use 24h, 12h, 3h, and 1h as the schedule. This gives Defenders the possibility to detect and respond to threat much earlier.


Tables that support Continuous (NRT) frequency

  • AlertEvidence
  • DeviceEvents
  • DeviceFileCertificateInfo
  • DeviceFileEvents
  • DeviceImageLoadEvents
  • DeviceLogonEvents
  • DeviceNetworkEvents
  • DeviceNetworkInfo
  • DeviceInfo
  • DeviceProcessEvents
  • DeviceRegistryEvents
  • EmailAttachmentInfo
  • EmailEvents
  • EmailPostDeliveryEvents
  • EmailUrlInfo
  • UrlClickEvents

The NRT rules does not support externaldata operator and you can only query one table

Configuring NRT (Continuous) Rule

From the Advanced Hunting, develop your query and click and configure the Alert Details

Click Next and select impacted entities (in this case we are using an email table and therefore the impacted entities will be mailbox)
Click Next and configure the actions.
It’s important to think about what the actions means and make sure your query will detect exactly what you want.

Be cautious with the Isolate device when querying Device tables. If you have an error in your detection you may isolate all machines by mistake

It’s now completed!

Don’t forget that you can use the hunting if you want to take response actions on multiple entities very quickly.

From the Result of your hunting query, select the rows where you want to take action and click Take Actions

This brings your the Actions pane and you can choose which actions you need.

Depending on your query (which tables and output) you get different options for your actions. ‘

Stay safe, and Happy Hunting!

Live response is GA for Linux and macOS

Live response is designed to enhance investigations by enabling your security operations team to collect forensic data, run scripts, send suspicious entities for analysis, remediate threats, and proactively hunt for emerging threats.

Capabilities

  • Run basic and advanced commands to do investigative work on a device.
  • Download files such as malware samples and outcomes of PowerShell scripts.
  • Download files in the background (new!).
  • Upload a PowerShell script or executable to the library and run it on a device from a tenant level.
  • Take or undo remediation actions.

Requirements

  • macOS – Minimum required version: 101.43.84. Supported for Intel-based and ARM-based macOS devices.
  • Linux – Minimum required version: 101.45.13

Happy Hunting