LeakyOhm: Secret Bits Extraction using Impedance Analysis

The threats of physical side-channel attacks and their countermeasures have been widely researched. Most physical side-channel attacks rely on the unavoidable influence of computation or storage on current consumption or voltage drop on a chip. Such data-dependent influence can be exploited by, for instance, power or electromagnetic analysis. In this work, we introduce a novel non-invasive physical side-channel attack, which exploits the data-dependent changes in the impedance of the chip. Our attack relies on the fact that the temporarily stored contents in registers alter the physical characteristics of the circuit, which results in changes in the die's impedance. To sense such impedance variations, we deploy a well-known RF/microwave method called scattering parameter analysis, in which we inject sine wave signals with high frequencies into the system's power distribution network (PDN) and measure the echo of the signals. We demonstrate that according to the content bits and physical location of a register, the reflected signal is modulated differently at various frequency points enabling the simultaneous and independent probing of individual registers. Such side-channel leakage challenges the $t$-probing security model assumption used in masking, which is a prominent side-channel countermeasure. To validate our claims, we mount non-profiled and profiled impedance analysis attacks on hardware implementations of unprotected and high-order masked AES. We show that in the case of the profiled attack, only a single trace is required to recover the secret key. Finally, we discuss how a specific class of hiding countermeasures might be effective against impedance leakage

Information Leakage Attacks and Countermeasures

The scientific community has been consistently working on the pervasive problem of information leakage, uncovering numerous attack vectors, and proposing various countermeasures. Despite these efforts, leakage incidents remain prevalent, as the complexity of systems and protocols increases, and sophisticated modeling methods become more accessible to adversaries. This work studies how information leakages manifest in and impact interconnected systems and their users. We first focus on online communications and investigate leakages in the Transport Layer Security protocol (TLS). Using modern machine learning models, we show that an eavesdropping adversary can efficiently exploit meta-information (e.g., packet size) not protected by the TLS’ encryption to launch fingerprinting attacks at an unprecedented scale even under non-optimal conditions. We then turn our attention to ultrasonic communications, and discuss their security shortcomings and how adversaries could exploit them to compromise anonymity network users (even though they aim to offer a greater level of privacy compared to TLS). Following up on these, we delve into physical layer leakages that concern a wide array of (networked) systems such as servers, embedded nodes, Tor relays, and hardware cryptocurrency wallets. We revisit location-based side-channel attacks and develop an exploitation neural network. Our model demonstrates the capabilities of a modern adversary but also presents an inexpensive tool to be used by auditors for detecting such leakages early on during the development cycle. Subsequently, we investigate techniques that further minimize the impact of leakages found in production components. Our proposed system design distributes both the custody of secrets and the cryptographic operation execution across several components, thus making the exploitation of leaks difficult

Analysis and Mitigation of Remote Side-Channel and Fault Attacks on the Electrical Level

In der fortlaufenden Miniaturisierung von integrierten Schaltungen werden physikalische Grenzen erreicht, wobei beispielsweise Einzelatomtransistoren eine mögliche untere Grenze für Strukturgrößen darstellen. Zudem ist die Herstellung der neuesten Generationen von Mikrochips heutzutage finanziell nur noch von großen, multinationalen Unternehmen zu stemmen. Aufgrund dieser Entwicklung ist Miniaturisierung nicht länger die treibende Kraft um die Leistung von elektronischen Komponenten weiter zu erhöhen. Stattdessen werden klassische Computerarchitekturen mit generischen Prozessoren weiterentwickelt zu heterogenen Systemen mit hoher Parallelität und speziellen Beschleunigern. Allerdings wird in diesen heterogenen Systemen auch der Schutz von privaten Daten gegen Angreifer zunehmend schwieriger. Neue Arten von Hardware-Komponenten, neue Arten von Anwendungen und eine allgemein erhöhte Komplexität sind einige der Faktoren, die die Sicherheit in solchen Systemen zur Herausforderung machen. Kryptografische Algorithmen sind oftmals nur unter bestimmten Annahmen über den Angreifer wirklich sicher. Es wird zum Beispiel oft angenommen, dass der Angreifer nur auf Eingaben und Ausgaben eines Moduls zugreifen kann, während interne Signale und Zwischenwerte verborgen sind. In echten Implementierungen zeigen jedoch Angriffe über Seitenkanäle und Faults die Grenzen dieses sogenannten Black-Box-Modells auf. Während bei Seitenkanalangriffen der Angreifer datenabhängige Messgrößen wie Stromverbrauch oder elektromagnetische Strahlung ausnutzt, wird bei Fault Angriffen aktiv in die Berechnungen eingegriffen, und die falschen Ausgabewerte zum Finden der geheimen Daten verwendet. Diese Art von Angriffen auf Implementierungen wurde ursprünglich nur im Kontext eines lokalen Angreifers mit Zugriff auf das Zielgerät behandelt. Jedoch haben bereits Angriffe, die auf der Messung der Zeit für bestimmte Speicherzugriffe basieren, gezeigt, dass die Bedrohung auch durch Angreifer mit Fernzugriff besteht. In dieser Arbeit wird die Bedrohung durch Seitenkanal- und Fault-Angriffe über Fernzugriff behandelt, welche eng mit der Entwicklung zu mehr heterogenen Systemen verknüpft sind. Ein Beispiel für neuartige Hardware im heterogenen Rechnen sind Field-Programmable Gate Arrays (FPGAs), mit welchen sich fast beliebige Schaltungen in programmierbarer Logik realisieren lassen. Diese Logik-Chips werden bereits jetzt als Beschleuniger sowohl in der Cloud als auch in Endgeräten eingesetzt. Allerdings wurde gezeigt, wie die Flexibilität dieser Beschleuniger zur Implementierung von Sensoren zur Abschätzung der Versorgungsspannung ausgenutzt werden kann. Zudem können durch eine spezielle Art der Aktivierung von großen Mengen an Logik Berechnungen in anderen Schaltungen für Fault Angriffe gestört werden. Diese Bedrohung wird hier beispielsweise durch die Erweiterung bestehender Angriffe weiter analysiert und es werden Strategien zur Absicherung dagegen entwickelt

LeakyOhm: Secret Bits Extraction using Impedance Analysis

The threat of physical side-channel attacks and their countermeasures is a widely researched field. Most physical side-channel attacks rely on the unavoidable influence of computation or storage on voltage or current fluctuations. Such data-dependent influence can be exploited by, for instance, power or electromagnetic analysis. In this work, we introduce a novel non-invasive physical side-channel attack, which exploits the data-dependent changes in the impedance of the chip. Our attack relies on the fact that the temporarily stored contents in registers alter the physical characteristics of the circuit, which results in changes in the die\u27s impedance. To sense such impedance variations, we deploy a well-known RF/microwave method called scattering parameter analysis, in which we inject sine wave signals with high frequencies into the system\u27s power distribution network (PDN) and measure the echo of the signals. We demonstrate that according to the content bits and physical location of a register, the reflected signal is modulated differently at various frequency points enabling the simultaneous and independent probing of individual registers. Such side-channel leakage violates the $t$-probing security model assumption used in masking, which is a prominent side-channel countermeasure. To validate our claims, we mount non-profiled and profiled impedance analysis attacks on hardware implementations of unprotected and high-order masked AES. We show that in the case of profiled attack, only a single trace is required to recover the secret key. Finally, we discuss how a specific class of hiding countermeasures might be effective against impedance leakage

Synesthesia: Detecting Screen Content via Remote Acoustic Side Channels

We show that subtle acoustic noises emanating from within computer screens can be used to detect the content displayed on the screens. This sound can be picked up by ordinary microphones built into webcams or screens, and is inadvertently transmitted to other parties, e.g., during a videoconference call or archived recordings. It can also be recorded by a smartphone or "smart speaker" placed on a desk next to the screen, or from as far as 10 meters away using a parabolic microphone. Empirically demonstrating various attack scenarios, we show how this channel can be used for real-time detection of on-screen text, or users' input into on-screen virtual keyboards. We also demonstrate how an attacker can analyze the audio received during video call (e.g., on Google Hangout) to infer whether the other side is browsing the web in lieu of watching the video call, and which web site is displayed on their screen

Don’t Learn What You Already Know

Over the past few years, deep-learning-based attacks have emerged as a de facto standard, thanks to their ability to break implementations of cryptographic primitives without pre-processing, even against widely used counter-measures such as hiding and masking. However, the recent works of Bronchain and Standaert at Tches 2020 questioned the soundness of such tools if used in an uninformed setting to evaluate implementations protected with higher-order masking. On the opposite, worst-case evaluations may be seen as possibly far from what a real-world adversary could do, thereby leading to too conservative security bounds. In this paper, we propose a new threat model that we name scheme-aware benefiting from a trade-off between uninformed and worst-case models. Our scheme-aware model is closer to a real-world adversary, in the sense that it does not need to have access to the random nonces used by masking during the profiling phase like in a worst-case model, while it does not need to learn the masking scheme as implicitly done by an uninformed adversary. We show how to combine the power of deep learning with the prior knowledge of scheme-aware modeling. As a result, we show on simulations and experiments on public datasets how it sometimes allows to reduce by an order of magnitude the profiling complexity, i.e., the number of profiling traces needed to satisfyingly train a model, compared to a fully uninformed adversary