We explore an emerging threat model for end-to-end (E2E) encrypted applications: an adversary sends chosen messages to a target client, thereby "injecting" adversarial content into the application state. Such state is subsequently encrypted and synchronized to an adversarially-visible storage. By observing the lengths of the resulting cloud-stored cipher-texts, the attacker backs out confidential information.We investigate this injection threat model in the context of state-of-the-art encrypted messaging applications that support E2E encrypted backups. We show proof-of-concept attacks that can recover information about E2E encrypted messages or attachments sent via WhatsApp, assuming the ability to compromise the target user’s Google or Apple account (which gives access to encrypted backups). We also show weaknesses in Signal’s encrypted backup design that would allow injection attacks to infer metadata including a target user’s number of contacts and conversations, should the adversary somehow obtain access to the user’s encrypted Signal backup.While we do not believe our results should be of immediate concern for users of these messaging applications, our results do suggest that more work is needed to build tools that enjoy strong E2E security guarantees.

The increasing harms caused by hate, harassment, and other forms of abuse online have motivated major platforms to explore hierarchical governance. The idea is to allow communities to have designated members take on moderation and leadership duties; meanwhile, members can still escalate issues to the platform. But these promising approaches have only been explored in plaintext settings where community content is public to the platform. It is unclear how one can realize hierarchical governance in the huge and increasing number of online communities that utilize end-to-end encrypted (E2EE) messaging for privacy.We propose private hierarchical governance systems. These should enable similar levels of community governance as in plaintext settings, while maintaining cryptographic privacy of content and governance actions not reported to the platform. We design the first such system, taking a layered approach that adds governance logic on top of an encrypted messaging protocol; we show how an extension to the message layer security (MLS) protocol suffices for achieving a rich set of governance policies. Our approach allows developers to rapidly prototype new governance features, taking inspiration from a plaintext system called PolicyKit. We build a prototype E2EE messaging system called MlsGov that supports content-based community and platform moderation, elections of community moderators, votes to remove abusive users, and more.

In this paper, we present video-based cryptanalysis, a new method used to recover secret keys from a device by analyzing video footage of a device’s power LED. We show that cryptographic computations performed by the CPU change the power consumption of the device which affects the brightness of the device’s power LED. Based on this observation, we demonstrate how attackers can exploit commercial video cameras (e.g., an iPhone 13’s camera or Internet-connected security camera) to recover secret keys from devices. This is done by obtaining video footage of a device’s power LED (in which the frame is filled with the power LED) and exploiting the video camera’s rolling shutter to increase the sampling rate by three orders of magnitude from the frames per second (FPS) rate (60 measurements per second) to the rolling shutter speed (60K measurements per second in the iPhone 13 Pro Max). The frames of the video footage of the device’s power LED are analyzed in the RGB space, and the associated RGB values are used to recover the secret key by inferring the device’s power consumption from the RGB values. We demonstrate the application of video-based cryptanalysis by performing two side-channel cryptanalytic timing attacks and recover: (1) a 256-bit ECDSA key from a smart card by analyzing video footage of the power LED of a smart card reader obtained by a hijacked Internet-connected security camera located 16 meters away from the smart card reader, and (2) a 378-bit SIKE key from a Samsung Galaxy S8 by analyzing video footage of the power LED of Logitech Z120 USB speakers that were connected to the same USB hub used to charge the Galaxy S8 obtained by an iPhone 13 Pro Max’s camera. We also discuss countermeasures, limitations, and the future of video-based cryptanalysis in light of the expected improvements in video camera specifications.

Although power LEDs have been integrated in various devices that perform cryptographic operations for decades, the cryptanalysis risk they pose has not yet been investigated. In this paper, we present optical cryptanalysis, a new form of cryptanalytic side-channel attack, in which secret keys are extracted by using a photodiode to measure the light emitted by a device's power LED and analyzing subtle fluctuations in the light intensity during cryptographic operations. We analyze the optical leakage of power LEDs of various consumer devices and the factors that affect the optical SNR. We then demonstrate end-to-end optical cryptanalytic attacks against a range of consumer devices (smartphone, smartcard, and Raspberry Pi, along with their USB peripherals) and recover secret keys (RSA, ECDSA, SIKE) from prior and recent versions of popular cryptographic libraries (GnuPG, Libgcrypt, PQCrypto-SIDH) from a maximum distance of 25 meters.

Two main classes of optical TEMPEST attacks against the confidentiality of information processed/delivered by devices have been demonstrated in the past two decades; the first class includes methods for recovering content from monitors, and the second class includes methods for recovering keystrokes from physical and virtual keyboards. In this paper, we identify a new class of optical TEMPEST attacks: recovering sound by analyzing optical emanations from a device's power indicator LED. We analyze the response of the power indicator LED of various devices to sound and show that there is an optical correlation between the sound that is played by connected speakers and the intensity of their power indicator LED due to the facts that: (1) the power indicator LED of various devices is connected directly to the power line, (2) the intensity of a device's power indicator LED is correlative to the power consumption, and (3) many devices lack a dedicated means of countering this phenomenon. Based on our findings, we present the Glowworm attack, an optical TEMPEST attack that can be used by eavesdroppers to recover sound by analyzing optical measurements obtained via an electro-optical sensor directed at the power indicator LED of various devices (e.g., speakers, USB hub splitters, and microcontrollers). We propose an optical-audio transformation (OAT) to recover sound in which we isolate the speech from optical measurements obtained by directing an electro-optical sensor at a device's power indicator LED. Finally, we test the performance of the Glowworm attack in various experimental setups and show that an eavesdropper can apply the attack to recover speech from speakers' power LED indicator with good intelligibility from a distance of 15 meters and with fair intelligibility from 35 meters.

In this paper, we introduce a novel side-channel attack for eavesdropping sound using an electro-optical sensor. We show how small vibrations of a hanging bulb (in response to sound hitting its surface), can be exploited by eavesdroppers to recover sound. We evaluate our method's performance in a realistic setup and show that our method can be used by eavesdroppers to recover songs from a target room containing the hanging light bulb.

As the number of drones increases and the era in which they begin to fill the skies approaches, an important question needs to be answered: From a security and privacy perspective, are society and drones really prepared to handle the challenges that a large volume of flights will create? In this paper, we investigate security and privacy in the age of commercial drones. First, we focus on the research question: Are drones and their ecosystems protected against attacks performed by malicious entities? We list a drone’s targets, present a methodology for reviewing attack and countermeasure methods, perform a comprehensive review, analyze scientific gaps, present conclusions, and discuss future research directions. Then, we focus on the research question: Is society protected against attacks conducted using drones? We list targets within society, profile the adversaries, review threats, present a methodology for reviewing countermeasures, perform a comprehensive review, analyze scientific gaps, present conclusions, and discuss future research directions. Finally, we focus on the primary research question: From the security and privacy perspective, are society and drones prepared to take their relationship one step further? Our analysis reveals that the technological means required to protect drones and society from one another has not yet been developed, and there is a tradeoff between the security and privacy of drones and that of society. That is, the level of security and privacy cannot be optimized concurrently for both entities, because the security and privacy of drones cannot be optimized without decreasing the security and privacy of society, and vice versa.

In this paper, we investigate "split-second phantom attacks," a scientific gap that causes two commercial advanced driver-assistance systems (ADASs), Telsa Model X (HW 2.5 and HW 3) and Mobileye 630, to treat a depthless object that appears for a few milliseconds as a real obstacle/object. We discuss the challenge that split-second phantom attacks create for ADASs. We demonstrate how attackers can apply split-second phantom attacks remotely by embedding phantom road signs into an advertisement presented on a digital billboard which causes Tesla's autopilot to suddenly stop the car in the middle of a road and Mobileye 630 to issue false notifications. We also demonstrate how attackers can use a projector in order to cause Tesla's autopilot to apply the brakes in response to a phantom of a pedestrian that was projected on the road and Mobileye 630 to issue false notifications in response to a projected road sign. To counter this threat, we propose a countermeasure which can determine whether a detected object is a phantom or real using just the camera sensor. The countermeasure (GhostBusters) uses a "committee of experts" approach and combines the results obtained from four lightweight deep convolutional neural networks that assess the authenticity of an object based on the object's light, context, surface, and depth. We demonstrate our countermeasure's effectiveness (it obtains a TPR of 0.994 with an FPR of zero) and test its robustness to adversarial machine learning attacks.

In an "open skies" era in which drones fly among us, a new question arises: how can we tell whether a passing drone is being used by its operator for a legitimate purpose (e.g., delivering pizza) or an illegitimate purpose (e.g., taking a peek at a person showering in his/her own house)? Over the years, many methods have been suggested to detect the presence of a drone in a specific location, however since populated areas are no longer off limits for drone flights, the previously suggested methods for detecting a privacy invasion attack are irrelevant. In this paper, we present a new method that can detect whether a specific POI (point of interest) is being video streamed by a drone. We show that applying a periodic physical stimulus on a target/victim being video streamed by a drone causes a watermark to be added to the encrypted video traffic that is sent from the drone to its operator and how this watermark can be detected using interception. Based on this method, we present an algorithm for detecting a privacy invasion attack. We analyze the performance of our algorithm using four commercial drones (DJI Mavic Air, Parrot Bebop 2, DJI Spark, and DJI Mavic Pro). We show how our method can be used to (1) determine whether a detected FPV (first-person view) channel is being used to video stream a POI by a drone, and (2) locate a spying drone in space; we also demonstrate how the physical stimulus can be applied covertly. In addition, we present a classification algorithm that differentiates FPV transmissions from other suspicious radio transmissions. We implement this algorithm in a new invasion attack detection system which we evaluate in two use cases (when the victim is inside his/her house and when the victim is being tracked by a drone while driving his/her car); our evaluation shows that a privacy invasion attack can be detected by our system in about 2-3 seconds.

In the area of espionage between countries, an infiltration covert channel used to trigger a silent malware installed on a network of a critical organization (such as 911 services and missile launching facility) from the outside world is extremely dangerous to the target country's security. In order to prevent attackers from establishing such a channel, these organizations take various steps to secure their networks, to make the establishment of this type of covert channel very challenging and almost impractical to achieve; the current state of the art methods are very limited and ineffective. In this paper, we show that even a strong isolation technique, such as air-gapping the network, can be circumvented by using an organizational multifunction printer (MFP) to establish an infiltration covert channel in order to communicate with a malware installed on an isolated organization from the outside. We show how an attacker can leverage the light sensitivity of an MFP and use different light sources to infiltrate commands to the malware in the organization. We analyze the influence of light intensity, distance, transmission rate, ambient light, and wavelength on the covert channel. In addition we demonstrate the attack on a real organization using: 1) a laser attached to a tripod stand; 2) a laser carried by a drone; and 3) a hijacked smart bulb that is not even connected to the organization's network and is accessed and controlled by an attacker in a passing car. We prove that locating the scanner in an inner room inside an organization does not prevent an attacker from establishing the covert channel. We show how our covert channel can be established from a greater distance (900 m) and at a higher transmission rate of 200 bits/s than other methods used to infiltrate data to an organization, even using invisible light (covertly).