Andreas Aßmuth

Amir Pakmehr, Andreas Aßmuth, Christoph P. Neumann et al.

Security challenges for Cloud or Fog-based machine learning services pose several concerns. Securing the underlying Cloud or Fog services is essential, as successful attacks against these services, on which machine learning applications rely, can lead to significant impairments of these applications. Because the requirements for AI applications can also be different, we differentiate according to whether they are used in the Cloud or in a Fog Computing network. This then also results in different threats or attack possibilities. For Cloud platforms, the responsibility for security can be divided between different parties. Security deficiencies at a lower level can have a direct impact on the higher level where user data is stored. While responsibilities are simpler for Fog Computing networks, by moving services to the edge of the network, we have to secure them against physical access to the devices. We conclude by outlining specific information security requirements for AI applications.

Simon Liebl, Ian Ferguson, Andreas Aßmuth et al.

A growing number of Internet of Things (IoT) devices are used across consumer, medical, and industrial domains. They interact with their environment through sensors and actuators and connect to networks such as the Internet. Because sensors may collect sensitive data and actuators can trigger physical actions, security, privacy, and safety are major challenges. Threat modelling can help identify risks, but established IT-focused methods transfer to the IoT only to a limited extent. In this paper, a new modelling technique specifically for IoT devices called Cyber-Physical Data Flow Diagram (CPDFD) is proposed that also allows modelling of hardware with the aim to support manufacturers in identifying threats and developing countermeasures. The technique was examined through an experimental study and a survey with interviews. The results suggest that numerous other attack scenarios can be found through the modelling technique, improving the identification of threats to IoT devices.

Alexander Berladskyy, Andreas Aßmuth

Phishing attacks remain one of the most prevalent threats to online security, with the Anti-Phishing Working Group reporting over 890,000 attacks in Q3 2025 alone. Traditional password-based authentication is particularly vulnerable to such attacks, prompting the development of more secure alternatives. This paper examines passkeys, also known as FIDO2, which claim to provide phishing-resistant authentication through asymmetric cryptography. In this approach, a private key is stored on a user's device, the authenticator, while the server stores the corresponding public key. During authentication, the server generates a challenge that the user signs with the private key; the server then verifies the signature and establishes a session. We present passkey workflows and review state-of-the-art attack vectors from related work alongside newly identified approaches. Two attacks are implemented and evaluated: the Infected Authenticator attack, which generates attacker-known keys on a corrupted authenticator, and the Authenticator Deception attack, which spoofs a target website by modifying the browser's certificate authority store, installing a valid certificate, and intercepting user traffic. An attacker relays a legitimate challenge from the real server to a user, who signs it, allowing the attacker to authenticate as the victim. Our results demonstrate that successful attacks on passkeys require substantial effort and resources. The claim that passkeys are phishing-resistant largely holds true, significantly raising the bar compared to traditional password-based authentication.

Lena Sinterhauf, Andreas Aßmuth, Roland Kaltefleiter

Critical vulnerabilities with Common Vulnerability Scoring System scores of 9.0 or higher pose severe risks to organisations' information systems. Timely detection and remediation are essential to minimise economic and reputational damage from cyberattacks. This paper provides a thorough analysis of the identification and resolution timelines of such critical vulnerabilities. A mixed-methods approach is employed, integrating quantitative data from global vulnerability databases analysing 245,456 Common Vulnerabilities and Exposures records spanning from 2009 to 2024, of which 12.8 % were critical, with qualitative case studies of notable incidents. This methodical combination of quantitative and qualitative data sources enables the identification of patterns and delay factors in vulnerability management. The findings indicate significant delays in public disclosure and patch deployment, influenced by industry-specific factors, resource availability and organisational processes. The paper concludes with a series of actionable recommendations to improve the efficiency of vulnerability responses. Despite faster disclosure, the remediation gap for critical vulnerabilities remains a systemic risk, driven by organisational inertia and system complexity.

Anket Mehra, Andreas Aßmuth, Malte Prieß

With AI-based software becoming widely available, the risk of exploiting its capabilities, such as high automation and complex pattern recognition, could significantly increase. An AI used offensively to attack non-AI assets is referred to as offensive AI. Current research explores how offensive AI can be utilized and how its usage can be classified. Additionally, methods for threat modeling are being developed for AI-based assets within organizations. However, there are gaps that need to be addressed. Firstly, there is a need to quantify the factors contributing to the AI threat. Secondly, there is a requirement to create threat models that analyze the risk of being attacked by AI for vulnerability assessment across all assets of an organization. This is particularly crucial and challenging in cloud environments, where sophisticated infrastructure and access control landscapes are prevalent. The ability to quantify and further analyze the threat posed by offensive AI enables analysts to rank vulnerabilities and prioritize the implementation of proactive countermeasures. To address these gaps, this paper introduces the Graph of Effort, an intuitive, flexible, and effective threat modeling method for analyzing the effort required to use offensive AI for vulnerability exploitation by an adversary. While the threat model is functional and provides valuable support, its design choices need further empirical validation in future work.

Pitpimon Choorod, Tobias J. Bauer, Andreas Aßmuth

For journalists reporting from a totalitarian regime, whistleblowers and resistance fighters, the anonymous use of cloud services on the Internet can be vital for survival. The Tor network provides a free and widely used anonymization service for everyone. However, there are different approaches to distinguishing Tor from non-Tor encrypted network traffic, most recently only due to the (relative) frequencies of hex digits in a single encrypted payload packet. While conventional data traffic is usually encrypted once, but at least three times in the case of Tor due to the structure and principle of the Tor network, we have examined to what extent the number of encryptions contributes to being able to distinguish Tor from non-Tor encrypted data traffic.