Kubernetes is a widely used open-source container orchestration system that helps to reduce workloads when dealing with container management in distributed systems. Its built-in authorization module is enabled by default and provides authorization mechanisms that prevent unauthorized access to resources. However, certain permissions enable their subject to escalate their privileges to a potentially Cluster compromising extent.
In this blog post we present a Bayesian statistical model to detect cryptographic timing attacks. This model is one of the results of a customer hardware assessment performed by the SCHUTZWERK GmbH. The assessment was performed in a gray box context, i.e., we were able to interact with the encryption hardware, but were not given any internal implementation details.
In software dealing with security, randomness is often necessary to generate keys or tokens for resetting passwords or identifying sessions. There, randomness is required to be unpredictable for an attacker. However, sometimes developers do not use cryptographically secure pseudo random number generators (CSPRNG) in this scenario. Instead they utilize faster pseudorandom number generators (PRNG). Consequently, the question arises how hard it is to attack a common (not cryptographically secure) random number generator.
In the previous post a setup and a technique to extract a representative section of a powertrace of a specific instruction of a STM32F3 processor were described. This section is called a “template”. These templates should later be used to identify instructions via a power sidechannel and reconstruct the flow of an unknown program on a controller that can not be dumped via JTAG. In this part of our poweranalysis series the extracted templates from the previous post will be analyzed to determine whether they are representative enough to reverse engineer entire programs from a powertrace.
Recent malware campaigns targeting private individuals and organizations quickly adapted to new spreading methods: Content related to COVID-19 is currently one of the main ways to distribute spam emails and malicious software [1]. This post outlines how opening a malicious Word document enables an attacker to conduct industrial espionage. This specific malware was likely sent to a selected list of potential victims, embedded in a document that was attached to a COVID-19-related phishing email.
This is the first part of a three part series about power analysis based software reverse engineering. It is part of our work in the SecForCARs project and the bachelor thesis ‘Poweranalyse basiertes Software Reverse Engineering mit Hilfe von Fuzzing’. The results will be summarized in this blogpost series. In this first part the goals of the research and the power analysis template extraction process are presented.
SCHUTZWERK at the first IT-GRC Congress in Berlin.
On the 15th and 16th June 2016 the first IT-GRC Congress took place in Berlin. The organizers "ISACA Germany Chapter e.V." and “Quadriga Hochschule Berlin” focus the event on “Cyber Security & Digital Transformation”. But the topics went beyond Governance, Revision and Compliance and covered also the current threat landscape in information technology.
At the event Christoph Wolfert, Senior Security Consultant at SCHUTZWERK GmbH, spoke about targeted attacks.
