The ntds.dit file from a domain controller contains all password hashes of the domain. In a company with employees around the globe we were allowed to analyze the hashes. Here are the results, and the reason why you should implement a banned password list.
Compass Security Blog
Offensive Defense
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Passwordless products promise greater security and convenience by allowing users to log in to Windows systems with only their smartphone. But what is going on behind the scenes and how could a domain’s security stance be worsened by such a solution? In this post I will explain how these products are implemented and detail the vulnerabilities and weaknesses discovered in three tested products.
BloodHound is the way to go to for finding attack paths in an Active Directory (AD) environment. However, it is not always clear how the data is gathered without looking at the code of SharpHound, the data ingestor for BloodHound. Microsoft hardened their systems over time through updates, which makes enumeration of Active Directory (AD) […]
BloodHound is the way to go to for finding attack paths in an Active Directory (AD) environment. However, it is not always clear how the data is gathered without looking at the code of SharpHound, the data ingestor for BloodHound. Microsoft hardened their systems over time through updates, which makes enumeration of Active Directory (AD) […]
BloodHound is the way to go to for finding attack paths in an Active Directory (AD) environment. However, it is not always clear how the data is gathered without looking at the code of SharpHound, the data ingestor for BloodHound. Microsoft hardened their systems over time through updates, which makes enumeration of Active Directory (AD) […]
Agile software development models have become the de-facto standard. They are taught at universities and implemented in practice as far as possible. Anyone who doesn’t develop software using agile processes is on the verge, and already tilting towards that. At least that is how it seems. Consequently, the question is not whether the integration of […]
A little bit over a year ago, I wrote an article on this blog about CVE-2020-1113 and how it enabled to execute code on a remote machine through relaying NTLM authentication over RPC triggering a scheduled task on the remote system. History repeats itself and a vulnerability of the same category has been fixed by Microsoft in June this year.
This part will discuss a grey-box approach in defeating malware detection. It will discuss the relevant features used and how they are fed into a malware detection model to classify an input file. After a short theory part, we try to find out which features are especially important for malware analysis and how to modify them. Finally, we will change some of the features of our ransomware to evade our model. But before all this, it is advisable to get familiar with the file format used by our malware.
Modern anti-malware products such as Windows Defender increasingly rely on the use of machine learning algorithms to detect and classify harmful malware. In this two-part series, we are going to investigate the robustness of a static machine learning malware detection model trained with the EMBER dataset. For this purpose we will working with the Jigsaw ransomware.
During internal assessments in Windows environments, we use BloodHound more and more to gather a comprehensive view of the permissions granted to the different Active Directory objects. In this post, we’ll show an advanced usage of this tool by using our additional queries.
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