The cloud computing landscape has evolved sig-nificantly in recent years, embracing various sandboxes to meet the diverse demands of modern cloud applications. These sandboxes encompass container-based technologies like Docker and gVisor, microVM-based solutions like Fire-cracker, and security-centric sandboxes relying on Trusted Execution Environments (TEEs) such as Intel SGX and AMD SEV. However, the practice of placing multiple tenants on shared physical hardware raises security and privacy concerns, most notably side-channel attacks. In this paper, we investigate the possibility of fingerprinting containers through CPU frequency reporting sensors in Intel and AMD CPUs. One key enabler of our attack is that the current CPU frequency information can be accessed by user-space attackers. We demonstrate that Docker images exhibit a unique frequency signature, enabling the distinction of different containers with up to 84.5 % accuracy even when multiple containers are running simultaneously in different cores. Additionally, we assess the effectiveness of our attack when performed against several sandboxes deployed in cloud environments, including Google's gVisor, AWS’ Firecracker, and TEE-based platforms like Gramine (utilizing Intel SGX) and AMD SEV. Our empirical results show that these attacks can also be carried out successfully against all of these sandboxes in less than 40 seconds, with an accuracy of over 70 % in all cases. Finally, we propose a noise injection-based countermeasure to mitigate the proposed attack on cloud environments.

Hardware peripherals such as GPUs and FPGAs are commonly available in server-grade computing to accelerate specific compute tasks, from database queries to machine learning. CSPs have integrated these accelerators into their infrastructure and let tenants combine and configure these components flexibly, based on their needs. Securing I/O interfaces is critical to ensure proper isolation between tenants in these highly complex, heterogeneous, yet shared server systems, especially in the cloud, where some peripherals may be under control of a malicious tenant.

Messengers have become an essential means of interpersonal interaction. Yet untraceable private communication remains an elusive goal, as most messengers hide content, but not communication patterns. The knowledge of communication patterns can by itself reveal too much, as happened, e. g., in the context of the Arab Spring. Subliminal channels in cryptographic systems enable untraceable private communication in plain sight. In this context, bulletin boards in the form of blockchains are a natural object for subliminal communication: accessing them is innocuous, as they rely on distributed access for verification and extension. At the same time, blockchain users generate hundreds of thousands of transactions per day that are individually signed and placed on the blockchain. Thus blockchains may serve as innocuous repository for publicly accessible cryptographic transactions where subliminal channels can be placed. In this paper, we propose a public-key subliminal channel using secret-recoverable splittable signature schemes on blockchains and prove that our construction is undetectable in the random oracle model under common cryptographic assumptions. Our approach is applicable to any secret-recoverable splittable signature scheme and introduces a constant overhead of a single signature per message. Such schemes are used by 98 of the top 100 cryptocurrencies. We also analyze the applicability of our approach to the Bitcoin, Monero, and RippleNet networks and present proof of concept implementations for Bitcoin and RippleNet.