Cache Based Power Analysis Attacks on AES

International audienceThis paper describes possible attacks against software implementations of AES running on processors with cache mechanisms, particularly in the case of smart cards. These attacks are based on sidechannel information gained by observing cache hits and misses in the current drawn by the smart card. Two dierent attacks are described. The first is a combination of ideas proposed in [2] and [11] to produce an attack that only requires the manipulation of the plain text and the observation of the current. The second is an attack based on specific implementations of the xtime function [10]. These attacks are shown to also work against algorithms using Boolean data masking techniques as a DPA countermeasure

Defending cache memory against cold-boot attacks boosted by power or EM radiation analysis

Some algorithms running with compromised data select cache memory as a type of secure memory where data is confined and not transferred to main memory. However, cold-boot attacks that target cache memories exploit the data remanence. Thus, a sudden power shutdown may not delete data entirely, giving the opportunity to steal data. The biggest challenge for any technique aiming to secure the cache memory is performance penalty. Techniques based on data scrambling have demonstrated that security can be improved with a limited reduction in performance. However, they still cannot resist side-channel attacks like power or electromagnetic analysis. This paper presents a review of known attacks on memories and countermeasures proposed so far and an improved scrambling technique named random masking interleaved scrambling technique (RM-ISTe). This method is designed to protect the cache memory against cold-boot attacks, even if these are boosted by side-channel techniques like power or electromagnetic analysis.Postprint (author's final draft

Physical Security of Cryptographic Algorithm Implementations

This thesis deals with physical attacks on implementations of cryptographic algorithms and countermeasures against these attacks. Physical attacks exploit properties of an implementation to recover secret cryptographic keys. Particularly vulnerable to physical attacks are embedded devices. In the area of side-channel analysis, this thesis addresses attacks that exploit observations of power consumption or electromagnetic leakage of the device and target symmetric cryptographic algorithms. First, this work proposes a new combination of two well-known attacks that is more efficient than each of the attacks individually. Second, this work studies attacks exploiting leakage induced by microprocessor cache mechanism, suggesting an algorithm that can recover the secret key in the presence of uncertainties in cache event detection from side-channel acquisitions. Third, practical side-channel attacks are discovered against the AES engine of the AVR XMEGA, a recent versatile microcontroller. In the area of fault analysis, this thesis extends existing attacks against the RSA digital signature algorithm implemented with the Chinese remainder theorem to a setting where parts of the signed message are unknown to the attacker. The new attacks are applicable in particular to several widely used standards in modern smart card applications. In the area of countermeasures, this work proposes a new algorithm for random delay generation in embedded software. The new algorithm is more efficient than the previously suggested algorithms since it introduces more uncertainty for the attacker with less performance overhead. The results presented in this thesis are practically validated in experiments with general-purpose 8-bit AVR and 32-bit ARM microcontrollers that are used in many embedded devices

Covert timing channels, caching, and cryptography

Side-channel analysis is a cryptanalytic technique that targets not the formal description of a cryptographic primitive but the implementation of it. Examples of side-channels include power consumption or timing measurements. This is a young but very active field within applied cryptography. Modern processors are equipped with numerous mechanisms to improve the average performance of a program, including but not limited to caches. These mechanisms can often be used as side-channels to attack software implementations of cryptosystems. This area within side-channel analysis is called microarchitecture attacks, and those dealing with caching mechanisms cache-timing attacks. This dissertation presents a number of contributions to the field of side-channel analysis. The introductory portion consists of a review of common cache architectures, a literature survey of covert channels focusing mostly on covert timing channels, and a literature survey of cache-timing attacks, including selective related results that are more generally categorized as side-channel attacks such as traditional timing attacks. This dissertation includes eight publications relating to this field. They contain contributions in areas such as side-channel analysis, data cache-timing attacks, instruction cache-timing attacks, traditional timing attacks, and fault attacks. Fundamental themes also include attack mitigations and efficient yet secure software implementation of cryptosystems. Concrete results include, but are not limited to, four practical side-channel attacks against OpenSSL, each implemented and leading to full key recovery

Side-Channel Attacks on Intel SGX: How SGX Amplifies The Power of Cache Attack

In modern computing environments, hardware resources are commonly shared, and parallel computation is more widely used. Users run their services in parallel on the same hardware and process information with different confidentiality levels every day. Running parallel tasks can cause privacy and security problems if proper isolation is not enforced. Computers need to rely on a trusted root to protect the data from malicious entities. Intel proposed the Software Guard eXtension (SGX) to create a trusted execution environment (TEE) within the processor. SGX allows developers to benefit from the hardware level isolation. SGX relies only on the hardware, and claims runtime protection even if the OS and other software components are malicious. However, SGX disregards any kind of side-channel attacks. Researchers have demonstrated that microarchitectural sidechannels are very effective in thwarting the hardware provided isolation. In scenarios that involve SGX as part of their defense mechanism, system adversaries become important threats, and they are capable of initiating these attacks. This work introduces a new and more powerful cache side-channel attack that provides system adversaries a high resolution channel. The developed attack is able to virtually track all memory accesses of SGX execution with temporal precision. As a proof of concept, we demonstrate our attack to recover cryptographic AES keys from the commonly used implementations including those that were believed to be resistant in previous attack scenarios. Our results show that SGX cannot protect critical data sensitive computations, and efficient AES key recovery is possible in a practical environment. In contrast to previous attacks which require hundreds of measurements, this is the first cache side-channel attack on a real system that can recover AES keys with a minimal number of measurements. We can successfully recover the AES key from T-Table based implementations in a known plaintext and ciphertext scenario with an average of 15 and 7 samples respectively

Micro-architectural Threats to Modern Computing Systems

With the abundance of cheap computing power and high-speed internet, cloud and mobile computing replaced traditional computers. As computing models evolved, newer CPUs were fitted with additional cores and larger caches to accommodate run multiple processes concurrently. In direct relation to these changes, shared hardware resources emerged and became a source of side-channel leakage. Although side-channel attacks have been known for a long time, these changes made them practical on shared hardware systems. In addition to side-channels, concurrent execution also opened the door to practical quality of service attacks (QoS). The goal of this dissertation is to identify side-channel leakages and architectural bottlenecks on modern computing systems and introduce exploits. To that end, we introduce side-channel attacks on cloud systems to recover sensitive information such as code execution, software identity as well as cryptographic secrets. Moreover, we introduce a hard to detect QoS attack that can cause over 90+\% slowdown. We demonstrate our attack by designing an Android app that causes degradation via memory bus locking. While practical and quite powerful, mounting side-channel attacks is akin to listening on a private conversation in a crowded train station. Significant manual labor is required to de-noise and synchronizes the leakage trace and extract features. With this motivation, we apply machine learning (ML) to automate and scale the data analysis. We show that classical machine learning methods, as well as more complicated convolutional neural networks (CNN), can be trained to extract useful information from side-channel leakage trace. Finally, we propose the DeepCloak framework as a countermeasure against side-channel attacks. We argue that by exploiting adversarial learning (AL), an inherent weakness of ML, as a defensive tool against side-channel attacks, we can cloak side-channel trace of a process. With DeepCloak, we show that it is possible to trick highly accurate (99+\% accuracy) CNN classifiers. Moreover, we investigate defenses against AL to determine if an attacker can protect itself from DeepCloak by applying adversarial re-training and defensive distillation. We show that even in the presence of an intelligent adversary that employs such techniques, DeepCloak still succeeds

Implicit Cache Lockdown on ARM: An Accidental Countermeasure to Cache-Timing Attacks

As Moore`s law continues to reduce the cost of computation at an exponential rate, embedded computing capabilities spread to ever-expanding application scenarios, such as smartphones, the Internet of Things, and automation, among many others. This trend has naturally caused the underlying technology to evolve and has introduced increasingly complex microarchitectures into embedded processors in attempts to optimize for performance. While other microarchitectures, like those used in personal computers, have been extensively studied, there has been relatively less research done on embedded microarchitectures. This is especially true in terms of their security, which is growing more important as widespread adoption increases. This thesis explores an undocumented cache behavior found in ARM Cortex processors that we call implicit cache lockdown. While it was presumably implemented for performance reasons, it has a large impact on the recently popular class of cybersecurity attacks that utilize cache-timing side-channels. These attacks leverage the underlying hardware, specifically, the small timing differences between algorithm executions due to CPU caches, to glean sensitive information from a victim process. Since the affected processors are found in an overwhelming majority of smart phones, this sensitive information can include cryptographic secrets, credit card information, and passwords. As the name implies, implicit cache lockdown limits the ability for an attacker to evict certain data from a CPU`s cache. Since this is precisely what known cache-timing attacks rely on, they are rendered ineffective in their current form. This thesis analyzes implicit cache lockdown in great detail, including the methodology we used to discover it, its implications on all existing cache-timing attacks, and how it can be circumvented by an attacker