sheepl

sheepl Sheepl : Creating realistic user behaviour for supporting tradecraft development within lab environments Introduction There are lots of resources available online relating to how you can build AD network environments for the development of blue team and red team tradecraft. However the...

Is It Real? Exploiting Virtual-Physical Discrimination Vulnerability in Mixed Reality

Consumer mixed reality MR headsets seamlessly blend virtual content into physical environments with sufficient fidelity that users may be unable to distinguish virtual objects from physical ones. We identify this virtual-physical discrimination vulnerability as an exploitable security primitive...

Simplifying AWS defense with Microsoft Sentinel UEBA

In this article 1. Under the hood: The tables 2. Traditional vs. new approach 3. Real-world attack scenarios: Microsoft Sentinel UEBA in action 4. Practical implementation: Getting started 5. Limitations and constraints 6. From raw logs to behavioral context With the expansion of Microsoft Sentin...

Simplifying AWS defense with Microsoft Sentinel UEBA

In this article 1. Under the hood: The tables 2. Traditional vs. new approach 3. Real-world attack scenarios: Microsoft Sentinel UEBA in action 4. Practical implementation: Getting started 5. Limitations and constraints 6. From raw logs to behavioral context With the expansion of Microsoft Sentin...

[Video] The TTP Ep 21: When Attackers Become Trusted Users

!\Video\ The TTP Ep 21: When Attackers Become Trusted Usershttps://storage.ghost.io/c/af/a0/afa04ee3-414f-4481-8d23-7e7c146f192e/content/images/2026/04/YiR2025cover2x1.jpg In this episode of the Talos Threat Perspective, we explore how identity is being used to gain, extend, and maintain access...

Application-Specific Power Side-Channel Attacks and Countermeasures: A Survey

Side-channel attacks try to extract secret information from a system by analyzing different side-channel signatures, such as power consumption, electromagnetic emanation, thermal dissipation, acoustics, time, etc. Power-based side-channel attack is one of the most prominent side-channel attacks i...

EUVD-2018-17659

Malware in sbrugna...

EUVD-2021-16231

Malware in sbrugna...

EUVD-2022-39471

Malicious code in bioql PyPI...

EUVD-2021-7847

Malicious code in bioql PyPI...

EUVD-2021-7809

Malicious code in bioql PyPI...

Why Your Security Culture is Critical to Mitigating Cyber Risk

After two decades of developing increasingly mature security architectures, organizations are running up against a hard truth: tools and technologies alone are not enough to mitigate cyber risk. As tech stacks have grown more sophisticated and capable, attackers have shifted their focus. They are...

Malicious code in wm-plugin-user-behavior (npm)

The package wm-plugin-user-behavior was found to contain malicious code...

MAL-2025-39376 Malicious code in wm-plugin-user-behavior (npm)

The package wm-plugin-user-behavior was found to contain malicious code...

iPhone vs. Android: iPhone users more reckless, less protected online

The smartphone wars have a winner, and it’s Android. No, this isn’t about which device has the best camera, the snappiest processor, or the flashiest AI features—this is about which device owners are safer online, and in many ways, it is Android users who take the crown. According to a new analys...

CVE-2018-11922

Wrong configuration in Touch Pal application can collect user behavior data without awareness by the user...

Cybersecurity Threat Detection Based on a UEBA Framework Using Deep Autoencoders

User and Entity Behaviour Analytics UEBA is a broad branch of data analytics that attempts to build a normal behavioural profile in order to detect anomalous events. Among the techniques used to detect anomalies, Deep Autoencoders constitute one of the most promising deep learning models on UEBA...

User Behavior Analysis in Privacy Protection with Large Language Models: a Study on Privacy Preferences with Limited Data

With the widespread application of large language models LLMs, user privacy protection has become a significant research topic. Existing privacy preference modeling methods often rely on large-scale user data, making effective privacy preference analysis challenging in data-limited environments...

Care what you share

Welcome to this week's edition of the Threat Source newsletter. As we navigate our daily routines, certain tasks become second nature to us, especially if they are integral to our professions. However, what feels instinctive to one person might be foreign to another. This disparity is akin to a...

Clustering and Analysis of User Behaviour in Blockchain: a Case Study of Planet IX

Decentralised applications dApps that run on public blockchains have the benefit of trustworthiness and transparency as every activity that happens on the blockchain can be publicly traced through the transaction data. However, this introduces a potential privacy problem as this data can be track...