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Register a new userDetecting Fault Injection Attacks with Runtime Verification
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Added by
Ali Kassem
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Fault injections are increasingly used to attack/test secure applications. In this paper, we define formal models of runtime monitors that can detect fault injections that result in test inversion attacks and arbitrary jumps in the control flow. Runtime verification monitors offer several advantages. The code implementing a monitor is small compared to the entire application code. Monitors have a formal semantics; and we prove that they effectively detect attacks. Each monitor is a module dedicated to detecting an attack and can be deployed as needed to secure the application. A monitor can run separately from the application or it can be ``weaved inside the application. Our monitors have been validated by detecting simulated attacks on a program that verifies a user PIN.
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As Field-programmable gate arrays (FPGAs) are widely adopted in clouds to accelerate Deep Neural Networks (DNN), such virtualization environments have posed many new security issues. This work investigates the integrity of DNN FPGA accelerators in clouds. It proposes DeepStrike, a remotely-guided attack based on power glitching fault injections targeting DNN execution. We characterize the vulnerabilities of different DNN layers against fault injections on FPGAs and leverage time-to-digital converter (TDC) sensors to precisely control the timing of fault injections. Experimental results show that our proposed attack can successfully disrupt the FPGA DSP kernel and misclassify the target victim DNN application.
Todays mobile devices contain densely packaged system-on-chips (SoCs) with multi-core, high-frequency CPUs and complex pipelines. In parallel, sophisticated SoC-assisted security mechanisms have become commonplace for protecting device data, such as trusted execution environments, full-disk and file-based encryption. Both advancements have dramatically complicated the use of conventional physical attacks, requiring the development of specialised attacks. In this survey, we consolidate recent developments in physical fault injections and side-channel attacks on modern mobile devices. In total, we comprehensively survey over 50 fault injection and side-channel attack papers published between 2009-2021. We evaluate the prevailing methods, compare existing attacks using a common set of criteria, identify several challenges and shortcomings, and suggest future directions of research.
Neural networks have been shown to be vulnerable against fault injection attacks. These attacks change the physical behavior of the device during the computation, resulting in a change of value that is currently being computed. They can be realized by various fault injection techniques, ranging from clock/voltage glitching to application of lasers to rowhammer. In this paper we explore the possibility to reverse engineer neural networks with the usage of fault attacks. SNIFF stands for sign bit flip fault, which enables the reverse engineering by changing the sign of intermediate values. We develop the first exact extraction method on deep-layer feature extractor networks that provably allows the recovery of the model parameters. Our experiments with Keras library show that the precision error for the parameter recovery for the tested networks is less than $10^{-13}$ with the usage of 64-bit floats, which improves the current state of the art by 6 orders of magnitude. Additionally, we discuss the protection techniques against fault injection attacks that can be applied to enhance the fault resistance.
End-user-devices in the current cellular ecosystem are prone to many different vulnerabilities across different generations and protocol layers. Fixing these vulnerabilities retrospectively can be expensive, challenging, or just infeasible. A pragmatic approach for dealing with such a diverse set of vulnerabilities would be to identify attack attempts at runtime on the device side, and thwart them with mitigating and corrective actions. Towards this goal, in the paper we propose a general and extendable approach called Phoenix for identifying n-day cellular network control-plane vulnerabilities as well as dangerous practices of network operators from the device vantage point. Phoenix monitors the device-side cellular network traffic for performing signature-based unexpected behavior detection through lightweight runtime verification techniques. Signatures in Phoenix can be manually-crafted by a cellular network security expert or can be automatically synthesized using an optional component of Phoenix, which reduces the signature synthesis problem to the language learning from the informant problem. Based on the corrective actions that are available to Phoenix when an undesired behavior is detected, different instantiations of Phoenix are possible: a full-fledged defense when deployed inside a baseband processor; a user warning system when deployed as a mobile application; a probe for identifying attacks in the wild. One such instantiation of Phoenix was able to identify all 15 representative n-day vulnerabilities and unsafe practices of 4G LTE networks considered in our evaluation with a high packet processing speed (~68000 packets/second) while inducing only a moderate amount of energy overhead (~4mW).
The rapid growth of Decentralized Finance (DeFi) boosts the Ethereum ecosystem. At the same time, attacks towards DeFi applications (apps) are increasing. However, to the best of our knowledge, existing smart contract vulnerability detection tools cannot be directly used to detect DeFi attacks. Thats because they lack the capability to recover and understand high-level DeFi semantics, e.g., a user trades a token pair X and Y in a Decentralized EXchange (DEX). In this work, we focus on the detection of two types of new attacks on DeFi apps, including direct and indirect price manipulation attacks. The former one means that an attacker directly manipulates the token price in DEX by performing an unwanted trade in the same DEX by attacking the vulnerable DeFi app. The latter one means that an attacker indirectly manipulates the token price of the vulnerable DeFi app (e.g., a lending app). To this end, we propose a platform-independent way to recover high-level DeFi semantics by first constructing the cash flow tree from raw Ethereum transactions and then lifting the low-level semantics to high-level ones, including token trade, liquidity mining, and liquidity cancel. Finally, we detect price manipulation attacks using the patterns expressed with the recovered DeFi semantics. We have implemented a prototype named tool{} and applied it to more than 350 million transactions. It successfully detected 432 real-world attacks in the wild. We confirm that they belong to four known security incidents and five zero-day ones. We reported our findings. Two CVEs have been assigned. We further performed an attack analysis to reveal the root cause of the vulnerability, the attack footprint, and the impact of the attack. Our work urges the need to secure the DeFi ecosystem.
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