Drive-by Key-Extraction Cache Attacks from Portable Code

We show how malicious web content can extract cryptographic secret keys from the user\u27s computer. The attack uses portable scripting languages supported by modern browsers to induce contention for CPU cache resources, and thereby gleans information about the memory accesses of other programs running on the user\u27s computer. We show how this side-channel attack can be realized in both WebAssembly and PNaCl; how to attain very fine-grained measurements; and how to use these to extract ElGamal, ECDH and RSA decryption keys from various cryptographic libraries. The attack does not rely on bugs in the browser\u27s nominal sandboxing mechanisms, or on fooling users. It applies even to locked-down platforms with strong confinement mechanisms and browser-only functionality, such as Chromebook devices. Moreover, on browser-based platforms the attacked software too may be written in portable JavaScript; and we show that in this case even implementations of supposedly-secure constant-time algorithms, such as Curve25519\u27s, are vulnerable to our attack

Undermining User Privacy on Mobile Devices Using AI

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users

PThammer: Cross-User-Kernel-Boundary Rowhammer through Implicit Accesses

Rowhammer is a hardware vulnerability in DRAM memory, where repeated access to memory can induce bit flips in neighboring memory locations. Being a hardware vulnerability, rowhammer bypasses all of the system memory protection, allowing adversaries to compromise the integrity and confidentiality of data. Rowhammer attacks have shown to enable privilege escalation, sandbox escape, and cryptographic key disclosures. Recently, several proposals suggest exploiting the spatial proximity between the accessed memory location and the location of the bit flip for a defense against rowhammer. These all aim to deny the attacker's permission to access memory locations near sensitive data. In this paper, we question the core assumption underlying these defenses. We present PThammer, a confused-deputy attack that causes accesses to memory locations that the attacker is not allowed to access. Specifically, PThammer exploits the address translation process of modern processors, inducing the processor to generate frequent accesses to protected memory locations. We implement PThammer, demonstrating that it is a viable attack, resulting in a system compromise (e.g., kernel privilege escalation). We further evaluate the effectiveness of proposed software-only defenses showing that PThammer can overcome those.Comment: Preprint of the work accepted at the International Symposium on Microarchitecture (MICRO) 2020. arXiv admin note: text overlap with arXiv:1912.0307