Software Grand Exposure: SGX Cache Attacks Are Practical

Side-channel information leakage is a known limitation of SGX. Researchers have demonstrated that secret-dependent information can be extracted from enclave execution through page-fault access patterns. Consequently, various recent research efforts are actively seeking countermeasures to SGX side-channel attacks. It is widely assumed that SGX may be vulnerable to other side channels, such as cache access pattern monitoring, as well. However, prior to our work, the practicality and the extent of such information leakage was not studied. In this paper we demonstrate that cache-based attacks are indeed a serious threat to the confidentiality of SGX-protected programs. Our goal was to design an attack that is hard to mitigate using known defenses, and therefore we mount our attack without interrupting enclave execution. This approach has major technical challenges, since the existing cache monitoring techniques experience significant noise if the victim process is not interrupted. We designed and implemented novel attack techniques to reduce this noise by leveraging the capabilities of the privileged adversary. Our attacks are able to recover confidential information from SGX enclaves, which we illustrate in two example cases: extraction of an entire RSA-2048 key during RSA decryption, and detection of specific human genome sequences during genomic indexing. We show that our attacks are more effective than previous cache attacks and harder to mitigate than previous SGX side-channel attacks

An Indigenous Perspective on a Period in the History of North America

CLA (Critical Language Analysis) asserts that human language ability involves more than merely reacting to reality. Language use, from a CLA perspective, is about creating realities. When an Ojibwe lady speaks about her life and times in North America, specifically the United States, CLA reveals that she has created a reality, a mixture of positive and negative elements, by her own words

Composite Enclaves: Towards Disaggregated Trusted Execution

The ever-rising computation demand is forcing the move from the CPU to heterogeneous specialized hardware, which is readily available across modern datacenters through disaggregated infrastructure. On the other hand, trusted execution environments (TEEs), one of the most promising recent developments in hardware security, can only protect code confined in the CPU, limiting TEEs' potential and applicability to a handful of applications. We observe that the TEEs' hardware trusted computing base (TCB) is fixed at design time, which in practice leads to using untrusted software to employ peripherals in TEEs. Based on this observation, we propose \emph{composite enclaves} with a configurable hardware and software TCB, allowing enclaves access to multiple computing and IO resources. Finally, we present two case studies of composite enclaves: i) an FPGA platform based on RISC-V Keystone connected to emulated peripherals and sensors, and ii) a large-scale accelerator. These case studies showcase a flexible but small TCB (2.5 KLoC for IO peripherals and drivers), with a low-performance overhead (only around 220 additional cycles for a context switch), thus demonstrating the feasibility of our approach and showing that it can work with a wide range of specialized hardware

Snappy: Fast On-chain Payments with Practical Collaterals

Permissionless blockchains offer many advantages but also have significant limitations including high latency. This prevents their use in important scenarios such as retail payments, where merchants should approve payments fast. Prior works have attempted to mitigate this problem by moving transactions off the chain. However, such Layer-2 solutions have their own problems: payment channels require a separate deposit towards each merchant and thus significant locked-in funds from customers; payment hubs require very large operator deposits that depend on the number of customers; and side-chains require trusted validators. In this paper, we propose Snappy, a novel solution that enables recipients, like merchants, to safely accept fast payments. In Snappy, all payments are on the chain, while small customer collaterals and moderate merchant collaterals act as payment guarantees. Besides receiving payments, merchants also act as statekeepers who collectively track and approve incoming payments using majority voting. In case of a double-spending attack, the victim merchant can recover lost funds either from the collateral of the malicious customer or a colluding statekeeper (merchant). Snappy overcomes the main problems of previous solutions: a single customer collateral can be used to shop with many merchants; merchant collaterals are independent of the number of customers; and validators do not have to be trusted. Our Ethereum prototype shows that safe, fast (<2 seconds) and cheap payments are possible on existing blockchains.Comment: Network and Distributed Systems Security (NDSS) Symposium 2020, 23-26 February 2020, San Diego, CA, US

Censorship-Resilient and Confidential Collateralized Second-Layer Payments

Permissionless blockchains are too slow for applications like point-of-sale payments. While several techniques have been proposed to speed up blockchain payments, none of them are satisfactory for application scenarios like retail shopping. In particular, existing solutions like payment channels require users to lock up significant funds and schemes based on pre-defined validators enable easy transaction censoring. In this paper, we develop Quicksilver, the first blockchain payment scheme that works with practical collaterals and is fast, censorship-resilient, and confidential at the same time.We implement Quicksilver for EVM-compatible chains and show that censoring-resilient payments are fast and affordable on currently popular blockchains platforms like Ethereum and Polygon

Detecting Mobile Application Spoofing Attacks by Leveraging User Visual Similarity Perception

Mobile application spoofing is an attack where a malicious mobile application mimics the visual appearance of another one. If such an attack is successful, the integrity of what the user sees as well as the confidentiality of what she inputs into the system can be violated by the adversary. A common example of mobile application spoofing is a phishing attack where the adversary tricks the user into revealing her password to a malicious application that resembles the legitimate one. In this work, we propose a novel approach for addressing mobile application spoofing attacks by leveraging the visual similarity of application screens. We use deception rate as a novel metric for measuring how many users would confuse a spoofing application for the genuine one. We conducted a large-scale online study where participants evaluated spoofing samples of popular mobile applications. We used the study results to design and implement a prototype spoofing detection system, tailored to the estimation of deception rate for mobile application login screens