Matter is a standard for interoperable smart homes, governed by a consortium of over 200 companies, like Apple, Google, and Amazon. A Matter network, called a fabric, can operate without an Internet connection and uses popular link layers such as Wi-Fi, Thread, or Bluetooth Low Energy. Matter provides a feature-rich application-layer protocol, including secure session-establishment mechanisms that should guarantee confidentiality, integrity, and availability. Prior work has partially explored DoS threats on Matter, although availability is essential and safety-critical for smart homes. For example, no prior work has covered Matter DDoS. We analyze the Matter standard and SDK and uncover two (D)DoS design vulnerabilities in the specification of Matter status report application-layer messages and an issue in the Matter discovery procedures. The three flaws (V1-V3) affect all versions of the Matter standard, including the latest (v1.4.2).

Bluetooth Low Energy (BLE) is a ubiquitous wireless technology used by billions of devices and defined in an open standard. The BLE specification defines two security protocols: pairing, which establishes a trust relationship between two devices by deriving the Long-Term Key (LTK), and session establishment, which generates a fresh encryption key for each (re)connection. The BLE security model and prior research primarily consider wireless-only adversaries. However, real deployments increasingly face software compromise, where an attacker exploits a vulnerability to gain arbitrary code execution or memory read/write capabilities on the device. Under such a compromise, an attacker can extract the LTK and use it to impersonate trusted devices or decrypt/forge protected traffic.

FIDO2 is a popular technology for single-factor and second-factor authentication. It is specified in an open standard including the WebAuthn and CTAP application layer protocols. We focus on CTAP which allows the communication between FIDO2 clients and authenticators. No prior work explored the CTAP Authenticator API which is a critical protocol-level attack surface as it deals with credential creation, deletion, and management. We address this gap by presenting the first security and privacy evaluation of the CTAP Authenticator API. We uncover two classes of CTAP protocol-level attacks we call CTRAPS.The client impersonation (CI) attacks exploit the lack of client authentication to tamper with FIDO2 authenticators. They include zero-click attacks capable of deleting FIDO2 credentials, including passkeys, without user interaction. The API confusion (AC) attacks abuse the lack of protocol API enforcements and confound FIDO2 authenticators, clients, and users into calling unwanted CTAP APIs while thinking they are calling legitimate ones. For example, a victim thinks is authenticating to a website, when they are deleting their credentials. The CTRAPS attacks are conducted either in proximity or remotely and are effective regardless of the underlying CTAP transport (USB, NFC, or BLE).We detail the eight vulnerabilities in the CTAP specification enabling the CTRAPS attacks. Seven of them are novel and include unauthenticated CTAP clients and trackable FIDO2 credentials. We release CTRAPS, an original toolkit to analyze CTAP and conduct the CTRAPS attacks. We confirm the attacks’ feasibility by exploiting six popular authenticators, including a FIPS-certified one, from Yubico, Feitian, SoloKeys, and Google, and ten widely used relying parties, such as Microsoft, Apple, GitHub, and Facebook. We discuss eight backward-compliant countermeasures to fix the attacks and their root causes. We responsibly disclosed our findings to the FIDO Alliance and the affected vendors.

Threat modeling (TM) is essential to manage, prevent, and fix security and privacy issues in our society. TM requires a data model to represent threats and tools to exploit such data. Current TM data models and tools have significant limitations preventing their usage in real-world scenarios. For example, it is challenging to TM embedded devices with current data models and tools as they cannot model their hardware, firmware, and low-level software. Moreover, it is impossible to TM a device lifecycle or security-privacy tradeoffs as these data models and tools were developed for other use cases (e.g., software security or user privacy). We fill this relevant gap by presenting the AttackDefense Framework (ADF), which provides a novel data model and related tools to augment TM. ADF’s building block is the AD object that can be used to represent heterogeneous and complex threats. Moreover, ADF provides automations to process a collection of AD objects, including ways to create sets, maps, chains, trees, and wordclouds of AD objects. We present ADF , a toolkit implementing ADF composed of four modules (Catalog, Parse, Check, and Analyze). We confirm that the data model and tools provided by ADF are useful by running an extensive set of experiments while threat modeling a crypto wallet and its lifecycle. Our experiments involved seven expert groups from academia and industry, each using the ADF on an orthogonal threat class. The evaluation generated 175 high-quality ADs covering ISA/IEC 62433-4-1 SecDev Lifecycle, side-channels, fault injection, microarchitectural attacks, speculative execution, pre-silicon testing, invasive physical chip modifications, Bluetooth protocol and implementation threats, and FIDO2 authentication.

Browser fingerprinting is an effective technique to track web users by building a fingerprint from their browser attributes. It is also stealthy because the tracker uses legitimate JavaScript API calls offered by the browser engine, which can be obfuscated before they are sent to a (third-party) server. Current browser fingerprinting methodologies employ coarse-grained collection and classification techniques, such as binary classification of fingerprinters based on the number of non-obfuscated exfiltrated attributes. As a result, they produce inconsistent findings. Meanwhile, the privacy of millions of web users is at risk daily. We address this gap by presenting FP-tracer, a novel methodology to detect and classify browser fingerprinters based on dynamic taint tracking and joint entropy classification. Our methodology enables detecting first- and third-party fingerprinters even when they use obfuscation by tainting attributes, propagating them, and logging when they are leaked (via 62 sources and 25 sinks). Moreover, it discriminates the invasiveness of fingerprinting activities, even from the same service, by measuring the joint entropy of the collected attributes and clustering them. We implement FP-tracer by extending Foxhound, a privacy-oriented Firefox fork with numeric type tainting, more taint tracking sources and sinks, support for multiple sources, and better logging capabilities. We embed our implementation in our automated crawling infrastructure, which is capable of testing websites in parallel using programmable and reproducible logic. We will open-source our implementation. We evaluate FP-tracer by performing a large-scale crawl over the Tranco Top 100K, and detect, amongst others, audio, canvas, and storage fingerprinting on the web. Among others, we find high fingerprinting activities in 8% of domains, with more moderate activity reaching 75%. Notably, fingerprinting is almost five times more likely to be performed by third-party scripts for high activity levels. In addition, we measure that the most severe category of fingerprinting obfuscates 46% of transmitted attributes, and 38% of fingerprinters involve two or more domains. Finally, we find that existing consent banners do not provide an effective defense against browser fingerprinting

Bluetooth is a pervasive technology for wireless communication. Billions of devices use it in sensitive applications and to exchange private data. The security of Bluetooth depends on the Bluetooth standard and its two security mechanisms: pairing and session establishment. No prior work, including the standard itself, analyzed the future and forward secrecy guarantees of these mechanisms, e.g., if Bluetooth pairing and session establishment defend past and future sessions when the adversary compromises the current. To address this gap, we present six novel attacks, defined as the BLUFFS attacks, breaking Bluetooth sessions' forward and future secrecy. Our attacks enable device impersonation and machine-in-the-middle across sessions by only compromising one session key. The attacks exploit two novel vulnerabilities that we uncover in the Bluetooth standard related to unilateral and repeatable session key derivation. As the attacks affect Bluetooth at the architectural level, they are effective regardless of the victim's hardware and software details (e.g., chip, stack, version, and security mode).

Xiaomi is the market leader in the electric scooter (e-scooter) segment, with millions of active users. It provides several e-scooter models and Mi Home, a mobile application for Android and iOS to manage and control an e-scooter. Mi Home and the e-scooter interact via Bluetooth Low Energy (BLE). No prior research evaluated the security of this communication channel, as it employs security protocols proprietary to Xiaomi. Exploiting these protocols results in severe security, privacy, and safety issues, e.g., an attacker could steal an e-scooter or prevent the owner from controlling it. In this work, we fill this research gap by performing the first security evaluation on all proprietary wireless protocols deployed to Xiaomi e-scooters from 2016 to 2021. We identify and reverse-engineer four of them, each having ad-hoc Pairing and Session phases. We develop four attacks exploiting these protocols at the architectural level, and we call them Malicious Pairing (MP) and Session Downgrade (SD). Both attacks can be performed from proximity, if the attacker's machine is within BLE range of the target e-scooter, or remotely, via a malicious application co-located with Mi Home. An adversary can utilize MP and SD to steal a password-protected and software-locked e-scooter, or to prevent a victim from accessing it via Mi Home. We isolate six attack root causes, including the lack of authentication while pairing, and the improper enforcement of the e-scooter password. We open-source the E-Spoofer toolkit. Our toolkit automates the MP and SD attacks, and includes a reverse-engineering module for future research. We empirically confirm the effectiveness of our attacks by exploiting three e-scooters (i.e., M365, Essential, and Mi 3), embedding five BLE subsystem boards and eight BLE firmware versions that support all four Xiaomi protocols. We design and evaluate two practical countermeasures that address our impactful attacks and their root causes, and we release them as part of E-Spoofer. We responsibly disclosed our findings to Xiaomi.

Bluetooth is a pervasive wireless technology specified in an open standard. The standard defines Bluetooth Classic (BT) for high-throughput wireless services and Bluetooth Low Energy (BLE) very low-power ones. The standard also specifies security mechanisms, such as pairing, session establishment, and cross-transport key derivation (CTKD). CTKD enables devices to establish BT and BLE security keys by pairing just once. CTKD was introduced in 2014 with Bluetooth 4.2 to improve usability. However, the security implications of CTKD were not studied carefully.

Bluetooth (BR/EDR) is a pervasive technology for wireless communication used by billions of devices. The Bluetooth standard includes a legacy authentication procedure and a secure authentication procedure, allowing devices to authenticate to each other using a long term key. Those procedures are used during pairing and secure connection establishment to prevent impersonation attacks. In this paper, we show that the Bluetooth specification contains vulnerabilities enabling to perform impersonation attacks during secure connection establishment. Such vulnerabilities include the lack of mandatory mutual authentication, overly permissive role switching, and an authentication procedure downgrade. We describe each vulnerability in detail, and we exploit them to design, implement, and evaluate master and slave impersonation attacks on both the legacy authentication procedure and the secure authentication procedure. We refer to our attacks as Bluetooth Impersonation AttackS (BIAS).Our attacks are standard compliant, and are therefore effective against any standard compliant Bluetooth device regardless the Bluetooth version, the security mode (e.g., Secure Connections), the device manufacturer, and the implementation details. Our attacks are stealthy because the Bluetooth standard does not require to notify end users about the outcome of an authentication procedure, or the lack of mutual authentication. To confirm that the BIAS attacks are practical, we successfully conduct them against 31 Bluetooth devices (28 unique Bluetooth chips) from major hardware and software vendors, implementing all the major Bluetooth versions, including Apple, Qualcomm, Intel, Cypress, Broadcom, Samsung, and CSR.

Bluetooth (BR/EDR) and Bluetooth Low Energy (BLE) are pervasive wireless technologies specified in the Bluetooth standard. The standard includes key negotiation protocols used to generate long-term keys (during pairing) and session keys (during secure connection establishment). In this work, we demonstrate that the key negotiation protocols of Bluetooth and BLE are vulnerable to standard-compliant entropy downgrade attacks. In particular, we show how an attacker can downgrade the entropy of any Bluetooth session key to 1 byte, and of any BLE long-term key and session key to 7 bytes. Such low entropy values enable the attacker to brute-force Bluetooth long-term keys and BLE long-term and session keys, and to break all the security guarantees promised by Bluetooth and BLE. As a result of our attacks, an attacker can decrypt all the ciphertext and inject valid ciphertext in any Bluetooth and BLE network. Our key negotiation downgrade attacks are conducted remotely, do not require access to the victims’ devices, and are stealthy to the victims. As the attacks are standard-compliant, they are effective regardless of the usage of the strongest Bluetooth and BLE security modes (including Secure Connections), the Bluetooth version, and the implementation details of the devices used by the victims. We successfully attack 38 Bluetooth devices (32 unique Bluetooth chips) and 19 BLE devices from different vendors, using all the major versions of the Bluetooth standard. Finally, we present effective legacy compliant and non-legacy compliant countermeasures to mitigate our key negotiation downgrade attacks.