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Automate criminal identity investigations and unmask threat actors with AI-powered link discovery for faster, deeper insights. Constella Intelligence today announced the launch of Hunter Copilot, a transformative new AI assistant feature within its acclaimed deep OSINT investigations platform, Hunter. This innovative tool sets a new standard for efficiency and effectiveness in threat actor attribution. As …

The post Constella Intelligence Launches HunterTM Copilot AI Assistant for Dark Web Investigations appeared first on Security Boulevard.

There have been a wealth of reports lately articulating the poor state of identity risk....

The post A Wave of Identity Security Reports Defines a Big Problem appeared first on Axiad.

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As organizations increasingly rely on open source software, associated security risks grow, demanding more robust and proactive risk management.

The post The evolution of open source risk: Persistent challenges in software security appeared first on Security Boulevard.

The Cloud Security Alliance, noting the increasing cyberthreats to critical infrastructure in a highly interconnected world, released a report outlining steps organizations can take to implement zero trust policies to protect against nation-state actors and other threat groups.

The post Cloud Security Alliance Advocates Zero Trust for Critical Infrastructure appeared first on Security Boulevard.

Authors/Presenters:Tanya Janca

Our sincere appreciation to DEF CON, and the Presenters/Authors for publishing their timely []DEF CON 32]2 erudite content. Originating from the conference’s events located at the Las Vegas Convention Center; and via the organizations YouTube channel.

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The post DEF CON 32 – AppSec Village – Maturing Your Application Security Program – SheHacksPurple appeared first on Security Boulevard.

Connections on the internet are not secure by default, and bad actors frequently take advantage of users accessing organizations’ applications and resources from remote locations, to burrow into corporate networks. Experts recommend picking a remote access management solution and adjusting the settings to the organization’s individual access control policy to establish remote connections securely. Doing..

The post Citrix Boosts Security for Remote Application Accesses With “More Security Layers” appeared first on Security Boulevard.

Tigran Safari, Client Success Manager AI isn’t some far-off sci-fi concept anymore. It’s here, and it’s coming to a bank near you! Listen to the latest from Tigran Safari and Chris Clements on this month’s Banking Podcast from The Kafafian Group We’ve spent a significant part of our journey working with investment, commercial, and community […]

The post AI in Banking: No Longer Just for the Big Banks appeared first on CISO Global.

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Traceable AI today published a global survey of 1,548 IT and cybersecurity professionals that finds well over half (57%) work for organizations that have experienced a data breach incident involving application programming interfaces (APIs) in the last two years, with nearly three-quarters of those organizations experiencing three or more incidents.

The post Survey Surfaces Fundamental Weaknesses in API Security appeared first on Security Boulevard.

Atlanta, GA, Oct. 30, 2024, CyberNewswire — The American Transaction Processors Coalition (ATPC) Cyber Council will convene “The Tie that Binds: A 21st Century Cybersecurity Dialogue,” on October 31, 2024, at the Bank of America Financial Center Tower’s Convention Hall … (more…)

The post News alert: Cybersecurity, AI priorities for 2025 highlighted at ATPC Cyber Forum in Atlanta first appeared on The Last Watchdog.

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Atlanta, Georgia, 30th October 2024, CyberNewsWire

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Hollywood loves a good thriller—and so do many of us. From Fatal Attraction to You, stalking is a recurring theme that keeps viewers glued to their screens. One of the latest installments in this genre, Netflix’s Baby Reindeer, pulls no punches in showing the terrifying lengths stalkers will go to, be it online or real life.  

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Authors/Presenters:Punkcoder

Our sincere appreciation to DEF CON, and the Presenters/Authors for publishing their timely DEF CON 32 erudite content. Originating from the conference’s events located at the Las Vegas Convention Center; and via the organizations YouTube channel.

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As organizations adopt more modern application strategies, APIs are increasingly important for enabling seamless communication and data exchange. However, this interconnectedness also introduces more significant security risks. APIs are gateways to sensitive information, making them prime targets for attackers. This can result in data breaches, business disruptions, and reputational damage.

To tackle these challenges, Salt Security, a leader in API security, has partnered with Dazz, an application security posture management expert. This collaboration combines the strengths of both platforms to deliver a comprehensive solution that offers exceptional protection and remediation against API threats.

Proactive Risk Reduction and Automated Remediation

Integrating Salt Security and Dazz offers a comprehensive API and Application Security Posture Management (ASPM) solution. Salt Security utilizes its patented platform, powered by AI and machine learning, to analyze API traffic, enabling effective discovery, posture governance, and threat protection. Meanwhile, Dazz consolidates security exposure data across code, cloud, applications, and infrastructure, prioritizing the most critical findings.

This potent combination allows for proactive risk reduction by identifying and addressing API vulnerabilities before they can be exploited. Automated root cause analysis traces API misconfigurations and vulnerabilities back to their origin in the code. This gives developers the context to quickly resolve issues and prevent them from reoccurring.

Reduced MTTR and Improved Security Posture

Combining Salt Security's precise API threat detection with Dazz's automated remediation capabilities allows organizations to reduce the Mean Time to Remediation (MTTR) significantly. This integration improves security by providing continuous visibility, proactive threat detection, and automated responses. As a result, it strengthens defenses and lowers the chances of successful attacks.

Key Features and Benefits

The Salt Security and Dazz integration offers a range of benefits:

• Comprehensive API and Application Security Posture Management: Addresses threats specific to APIs and general application vulnerabilities, providing complete visibility and control over your entire attack surface.
• Proactive Risk Reduction: Identifies and mitigates API vulnerabilities before they can be exploited.
• Automated Root Cause Analysis: Traces API misconfigurations and vulnerabilities back to their source, enabling efficient remediation and preventing recurring issues.
• Reduced Mean Time to Resolution (MTTR): Accelerates the resolution of vulnerabilities through automated remediation capabilities.
• Improved Security Posture: Enhances your overall security posture with continuous visibility, proactive threat detection, and automated remediation.

Conclusion

Integrating Salt Security and Dazz provides a robust solution for organizations aiming to enhance their API and application security. By combining the capabilities of both platforms, organizations can gain comprehensive visibility, proactively address risks, automate remediation, and strengthen their overall security posture.

If you want to learn more about how the Salt and Dazz integration provides best-in-class API security, contact Salt and Dazz today!

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TL;DR: BOFHound can now parse Active Directory Certificate Services (AD CS) objects, manually queried from LDAP, for review and attack path mapping within BloodHound Community Edition (BHCE).

Background

My last BOFHound-related post covered the support and usage strategies for Beacon object files (BOFs) enabling the manual collection of data required for BloodHound’s AdminTo and HasSession edges, among others. This brief post will cover the addition of AD CS object parsing (shoutout to GitHub user P-aLu for collaborating on the update) and some queries to get you started.

But first, to clear up some misconceptions…

BOFHound is not a BOF!

Surprised and/or confused? Somewhat angry? Yes, this is the most common misconception I hear about the tool. BOFHound is not a BOF implementation of SharpHound; rather, it is a Python script that runs completely offline from your target network. I repeat — it is neither a BOF, nor a tool that actively enumerates a target Active Directory (AD) network. If this is not news to you, enlightened reader, consider skipping ahead to The Good Stuff.

So What Does BOFHound Do (and Why on Earth Did You Name It That)?

BOFHound was born from repeated red team engagements in a large AD network where the blue team would flag SharpHound and other “loud” methods of LDAP enumeration. (In this environment, “loud” meant an expensive LDAP query — a query that returned a number of results over a threshold the client had determined to be indicative of attacker reconnaissance.) The team I worked with at the time adjusted by primarily relying on the ldapsearch BOF, part of TrustedSec’s situational awareness collection, for LDAP reconnaissance. This had two main benefits:

• It grants the operator full control over the LDAP query filter, meaning discretion can be used to avoid static query strings tied to common offensive tools and leveraged in detections [1, 2]
• The operator can specify a limit on the result count, so that the query returns no more than N entries; this is helpful when consciously avoiding expensive queries (result pools can also be narrowed with search scope, targeted query filters, or through a search base)

Here are some downsides to solely relying on this approach for LDAP enumeration:

• Queries only return text-based results (i.e., there is no visualization or BloodHound-like graph that many operators prefer)
• Identifying ACL abuses is essentially impossible with this approach alone
• Trying to keep track of and organize relationships between objects, like group memberships (let alone nested memberships), by hand in text/Excel files is difficult

The bigger the target environment is, the more these problems are amplified. These also all sound like problems that BloodHound was originally created to solve. Is there any way we could maintain the granular control the ldapsearch BOF offers us over enumeration and still be able to use BloodHound?

Enter BOFHound. It aims to serve this very niche need by reading LDAP objects from your ldapsearch results in C2 logs, processing them, and producing BloodHound JSON files.

Thus the name BOFHound — read output from the ldapsearch BOF in log files and make it BloodHound-usable. LogHound just doesn’t have the same ring to it, does it?

This allows for ACL relationships to be parsed (from base64 encoded nTSecurityDescriptor attributes) and for your operators to continue visualizing nodes and relationships in the BloodHound UI where they are happy.

Now you might be wondering “BloodHound collects all data [that we frequently care for] in an AD environment, so how can this work with partial data obtained from manual queries?” Well, it’s simple; the BloodHound UI will only show you what you have enumerated via manual queries and parsed with BOFHound. In other words, it will be incomplete data and, if you’re using this approach, you’re likely fine with that. We can target specific information we want to populate the graph with, such as objects related to our objectives, objects that have privileges we want to compromise, or objects that often make easy targets (i.e., AD CS, SCCM).

But BloodHound will actually show you a little more than only what you’ve queried. Object ACLs return references (SIDs) to other objects in the environment that are granted some permission over the object you queried. When you parse these ACLs without an object tied to the SID, they are represented in the graph by nodes with SIDs for names or question mark icons. For example, if I query only the domain object, run it through BOFHound, upload the result to BloodHound, and check the Inbound Object Control on the domain object, we’ll see this.

Parsing Single Object Reveals the Presence of Many More

I’ve only queried the domain object, but I also now know that these six other objects exist. Knowing their SIDs provides a means to query those objects that might be of interest. As I query them and rerun BOFHound, we can slowly fill in that picture and expand it, essentially providing a way to identify objects of interest, and work backwards from them.

The Fog of War

A while back, I was talking with Jared Atkinson about BOFHound and these very misconceptions when he made a very apt analogy: clearing the fog of war. In games like Civilization or Age of Empires, you start surrounded by the “fog of war,” representing the world you know exists, but otherwise know nothing about. As you scout around, you reveal more of the map, pushing back the fog of war, and gaining more information about the game’s world.

An unsophisticated player may just send their scouts out to chart the entire map as fast as possible. This could end up being advantageous, but it could also backfire by increasing the probability of encounters the player is unprepared for (i.e., a hostile faction). A sophisticated player may choose to scout areas as it’s advantageous to them, or when they’re prepared to deal with potentially hostile discoveries.

Brb, Building a Scout

As a prospective BOFHound user, the fog of war is the great unknown AD environment: objects, relationships, attack paths, etc. Immediately trying to chart the entire map is like trying query every AD object at once (i.e., (objectClass=*), running SharpHound); very beneficial data to have if successful, but you’re at high risk of revealing your presence to the hostile blue team. BOFHound is supposed to aid you in taking the careful, “sophisticated player” approach. Each time you manually run a batch of LDAP queries and parse/process with BOFHound, you clear a bit more of the fog and incrementally learn more about the environment you’re in. It is an iterative process (query → parse → query, etc.) until you feel you have sufficient information to take additional action or move on.

I think it goes without saying, but this approach is quite niche. If you do not care about being detected performing LDAP reconnaissance or know/think an alert will not fire for something like running SharpHound or ADExplorer, then you should run them. Having a more complete dataset will always allow you to spend your time more efficiently. I use this methodology described with BOFHound a handful of times a year, when I care about being stealthy; you really have no need for this in your playbook otherwise.

I hope this detour helped clear up some of the self-inflicted misconceptions around BOFHound’s purpose, intended usage, and name. This took up way more space than I initially intended (and became a bit rant-y), but now we can get into the AD CS updates.

The Good Stuff

In addition to the AD CS support, I recently added parsing support for Havoc log files. The rest of this blog will use the ldapsearch BOF via Havoc to demo one method of AD CS enumeration. While you’re probably not rolling out Havoc on your red team assessments, I realized only supporting log parsing for commercial C2s might otherwise turn users away from testing BOFHound in a lab. Havoc ships with an outdated version (as of 10/22/2024) of the ldapsearch BOF (needs updated for AD CS querying), but that can be easily remedied by copying the latest BOFs over to havoc/client/Modules/SituationalAwareness/ObjectFiles/and updating the ldapsearch_parse_params() function within hacov/client/Moduels/SituationalAwareness/SituationalAwareness.py to the definition in this gist.

On past assessments, I’ve typically leveraged one of the various adcs_enum BOFs included in situational awareness collection for AD CS enumeration and config analysis. These work great and I’ve used them to identify vulnerable templates and AD CS ESC paths during real-world engagements. Why bother switching enumeration playbooks? Manually analyzing a wall of certificate config text can work, but it can also be error prone, especially when it comes to trying to unroll group memberships for permissions like enrollment rights.

Trusty Old Text Wall of Certificate Configs

While it’s still all too common to find paths like ESC1 with groups such as Domain Users, Domain Computers, and Authenticated Users jumping out when checking enrollment rights, unrolling nested rights (like the path below) isn’t always so straightforward when reviewing these config dumps.

ESC1 Path Through Nested Group Membership

The question the remainder of the blog tries to answer — can we piece together this same picture without burning the red team by running SharpHound? My coworker Jonas Bülow Knudsen released a very informative blog about AD CS attack paths in BloodHound when they were introduced and his blog details the types of LDAP objects relied upon for mapping AD CS relationships. In short, there are six “new” types of objects we care about:

• Enterprise CAs
• AIACAs
• Root CAs
• NTAuth Stores
• Certificate Templates
• Issuance Policies

If we query these objects (and the domain object), we can graph out the attack paths. There’s no “right” way to do this with ldapsearch, but a basic approach is a sequence of queries based on object class. All the target objects are nested in the Configuration naming context — technically we could query them all at once with (objectClass=*)and a search base of CN=Cofiguration,DC=domain,DC=local. However, if you’re interested in this manual approach in the first place, you may not be interested in that sort of wide-ranging query 😉.

Here’s a sample query transcript to get you started:

# Query the domain object
ldapsearch (objectclass=domain) *,ntsecuritydescriptor

# Query Enterprise CAs
ldapsearch (objectclass=pKIEnrollmentService) *,ntsecuritydescriptor 0 3 "" "CN=Configuration,DC=domain,DC=local"

# Query AIACAs, Root CAs and NTAuth Stores
ldapsearch (objectclass=certificationAuthority) *,ntsecuritydescriptor 0 3 "" "CN=Configuration,DC=domain,DC=local"

# Query Certificate Templates
ldapsearch (objectclass=pKICertificateTemplate) *,ntsecuritydescriptor 0 3 "" "CN=Configuration,DC=domain,DC=local"

# Query Issuance Policies
ldapsearch (objectclass=msPKI-Enterprise-Oid) *,ntsecuritydescriptor 0 3 "" "CN=Configuration,DC=domain,DC=local"

If you’re new to the ldapsearch BOF (or even just its recent argument updates), here’s a quick breakdown of its positional arguments. Consider this query from the examples: ldapsearch (objectclass=pKIEnrollmentService) *,ntsecuritydescriptor 0 3 "" "CN=Configuration,DC=essos,DC=local" — what does this mean?

https://medium.com/media/095f6961612f6b8e24622c1c90fbdbeb/href

Here’s an example of one transcript query performed with the ldapsearch BOF via Havoc. I’m doing this enumeration across a domain trust in my lab (GOAD, from sevenkingdoms.local to essos.local), hence the explicit definition of domain controller to query and search base (even for objects in the default naming context).

Query for AIACAs, NTAuth Stores, and Root CAs

After running each of the queries in the sample transcript, we can parse that minimal dataset from the Havoc loot logs with BOFHound.

bofhound -i /opt/havoc/data/loot --parser havoc --zip BOFHound Parsing the Log Data

After the data has been parsed, we can load up BHCE and upload the output archive.

curl -L https://ghst.ly/getbhce > docker-compose.yml
sudo docker compose pull
sudo docker compose up

In the UI, we can use some of the prebuilt queries to help us find common misconfigurations and escalation paths. I’ll start with the prebuilt Enrollment rights on ESC1 certificate templates query, which will limit the templates I view to just those that meet ESC1 requirements.

Prebuilt AD CS Queries

Here’s what those results look like with the objects we queried in the lab. We know the SIDs ending in 519 and 512 are Enterprise Admins and Domain Admins, but we’ve got an unknown object with a resource ID (RID) of “1602”.

The “Fog of War” — Unknown Objects in the Graph

If we issue an LDAP query for that mystery SID, we can determine that it’s a group (think back to the original attack path screenshot we’re working towards).

ldapsearch (objectSid=S-1–5–21–2301939034–812057587–2058894929–1602) *,ntsecuritydescriptor Query the Unknown BloodHound Node

That returned properties for the group granted enrollment rights, including its distinguished name, which we can use to unroll the group members.

Just One More Turn

To unroll the group’s members (return first and Nth degree members) we can take the group’s distinguished name and issue an LDAP query using the LDAP_MATCHING_RULE_TRANSITIVE_EVAL matching rule.

ldapsearch (memberOf:1.2.840.113556.1.4.1941:=CN=Nested1,CN=Users,DC=essos,DC=local) *,ntsecuritydescriptor Unroll Group Membership

That query returns two results in the lab, though only one can be seen in the screenshot. However, if you look closely at the user object returned, you’ll notice the memberOf attribute doesn’t contain the Nested1 group from our query filter (meaning the object must be a Nth degree member).

Now we rerun BOFHound, same as we did before.

bofhound -i /opt/havoc/data/loot --parser havoc --zip

After we ingest the data again, rerunning the same ESC1 enrollment rights query shows the previously unknown SID with enrollment rights belonged to the Nested1 group.

Slowly Clearing the Fog of War

This prebuilt query doesn’t unroll the group members, though. If we click the [email protected] object to bring up the properties pane on the right side, we can click the Inbound Object Control section to unroll enrollment rights (and potentially other ACL-based abuses).

The Unrolled Enrollment Rights Leading to ESC1

This reveals the path we initially examined, showing a way to perform ESC1 through nested enrollment rights. Should we return to BloodHound’s pathfinding tab and search for paths starting at [email protected] and ending at ESSOS.LOCAL, we now have all the data necessary to map the attack path shown at the beginning of this section (if you’re following along in GOAD, your pathfinding might surface a shorter ASCSESC3 path that you have to filter out).

Assessment Debrief

BloodHound currently supports edges for ESC1, ESC3, ESC4 and ESC13. You can quickly query any escalation path abusing one of them with:

MATCH p=()-[:ADCSESC1|ADCSESC3|ADCSESC4|ADCSESC13]->()
RETURN p ADCSESC1, ADCSESC3, ADCSESC4 Edges from GOAD “Misconfigurations” Is Anything Missing?

The ManageCA and ManageCertificates edges from BloodHound are not currently implemented in BOFHound. These relationships require reading values from the registry on CAs, which would require some other “collector” and parser (something akin to how BOFHound supports AdminTo and HasSession edges). These relationships are not currently relied on for any ESC attacks that BHCE supports, but will be if or when BloodHound gains ESC7 support.

Conclusion

This post served to clear up some common misconceptions about BOFHound, introduce the ability to parse AD CS objects, and provide a starting point for querying relevant LDAP objects. I hope that the added support for Havoc makes it easier to follow along with this post in a lab and give BOFHound a try. If you encounter any bugs, please feel free to open an issue on GitHub or shoot me a message in the BloodHoundGang Slack (@Tw1sm).

BOFHound: AD CS Integration was originally published in Posts By SpecterOps Team Members on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Cybersecurity is a leading concern for risk managers as AI-related cyber risks surge, and despite growing investments, many businesses still lack comprehensive cyber insurance, according to a Nationwide survey.

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CHOROLOGY.ai this week added an Automated Compliance Engine (ACE) to its data security posture management (DSPM) platform that makes use of multiple types of artificial intelligence (AI) to rapidly classify sensitive data at scale.

The post CHOROLOGY.ai Extends AI Reach to Classify Sensitive Data appeared first on Security Boulevard.

Cybersecurity firm Proofpoint, which is eyeing an IPO in the next 18 months, is buying startup Normalyze to improve its data protection capabilities and mitigate the threat humans present in an increasingly fast-paced, interconnected, and AI-focus world.

The post Proofpoint Boosting Data Security with Normalyze Acquisition appeared first on Security Boulevard.

Organizations spend 32.4% of security budgets on code security, yet only 44% of developers follow secrets management best practices. Get the full insights in our 2024 report.

The post Voice of Practitioners 2024 appeared first on Security Boulevard.

Introduction

Within today’s quickly transforming threat context, security operations centers (SOCs) need robust tools to detect, analyze, and respond to threats efficiently. ReversingLabs’ integration with Microsoft Sentinel, a cloud-native Security Information and Event Management (SIEM) solution, empowers SOCs with advanced threat intelligence and file enrichment capabilities. This blog post explores how these integrations enhance security operations and streamline incident response.

The post Enhancing Security Operations with ReversingLabs and MicrosoftSentinel Integrations appeared first on Security Boulevard.

Open banking has revolutionized the financial services industry, allowing customers to share their financial data with third-party providers through secure APIs (Application Programming Interfaces). While this has opened up new avenues for innovation, it also brings significant security risks. Securing APIs in open banking is essential to protect sensitive financial data and maintain customer trust. […]

The post Why Does Every Retailer Need Penetration Testing to Ensure Customer Safety? first appeared on StrongBox IT.

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