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Controller Area Network (CAN) is still the most used network technology in today's connected cars. Now and in the near future, penetration tests in the area of automotive security will still require tools for CAN media access. More and more open source automotive penetration tools and frameworks are presented by researchers on various conferences, all with different properties in terms of usability, features and supported use-cases. Choosing a proper tool for security investigations in automotive network poses a challenge, since lots of different solutions are available. This paper compares currently available CAN media access solutions and gives advice on competitive hard-and software tools for automotive penetration testing.
IOT Backdoors in Cars
(2019)
Connecting cheap IoT devices to the safety-critical network of a car can be an extremely bad idea, but at least it allows us to hack together our own automotive gadget. This talk explains the complete procedure involved in transforming a cheap OBD GSM dongle designed for fleet management into a open source automotive hacking tool. First, the hardware reverse engineering is demonstrated, showing how each component is interconnected and working together. With this knowledge, it was possible to capture the communication of the GSM module and understand the OTA protocol used by this dongle, which can be used to extract the firmware. A quick reverse engineering of the software will show that no cryptographic authentication is used for the OTA updates, and therefore a pirate GSM BTS can be used to obtain remote code execution. After that, a new open source firmware is written for the device, which can easily be extended and controlled remotely with the LUA scripting language. Examples on how hacking this dongle remotely can affect the safety of the driver will be also given.
SIM SIMulator
(2019)
Der Vortrag präsentierte ein Tool, das die Simulation einer SIM-Karte durch einen Standard-Mikrocontroller ermöglicht. Mit diesem Täuschungsmanöver kann die Authentifizierung des 3G-Mobilfunkstandards seitens der SIM-Karte umgangen werden. Tritt zusätzliche Hardware hinzu, die eine 3G-Basisstation vortäuscht, lässt sich eine Man-in-the-Middle-Attacke im 3G-Netz durchführen.
In diesem Szenario ist es möglich, die sensiblen Kommunikationsdaten, also den gesamten Datenverkehr beispielsweise zwischen einem Pkw und den Backend-Servern des Herstellers, auszulesen und zu untersuchen. Zudem eignet sich das Tool zum Pentesten von Modems oder SIM- beziehungsweise Smartcard-Applikationen. Der komplette Aufbau des sogenannten SIMulators ist als Open-Source-Software frei verfügbar und auf GitHub abrufbar.
Modern cars include more and more features that first emerged from the consumer electronics industry. Technologies like Bluetooth and Internet-connected services found their way into the vehicle industry. The secure implementation of these functions presents a great challenge for the manufacturers because products originating from the consumer industry can often not be easily transferred to the safety-sensitive traffic environment due to security concerns. However, common automotive interfaces like the diagnostics port are now also used to implement new services into the car. With dongles designed to read out certain vehicle data and transfer it to the Internet via the cellular network, the owner can access information about gas consumption or vehicle location through a mobile phone app, even when he is away from the car. This paper wants to emphasize new threats that appear due to the ongoing interconnection in modern cars by discussing the security of the diagnostics interface in combination with the use of an Internet-connected dongle. Potential attack vectors, as well as proof-of-concept exploits will be shown and the implications of security breaches on the safe state of the vehicle will be investigated.
This talk will provide a general overview on how Scapy can be used for automotive penetration testing. All present features of Scapy for automotive penetration will be introduced and explained. Also an overview of higher level automotive protocols will be given.
As automotive penetration testing becomes more important, the lack of free tools for automotive network penetration testing led us to integrate new features in Scapy. Scapy is a well established framework for packet manipulation. The flexibility of Scapy allowed us to implement automotive interfaces (CAN) and automotive protocols (ISOTP, GMLAN, UDS, DoIP, OBD-II).
This talk explains the basics of these automotive protocols, the workflow with Scapy for automotive network penetration testing. A live demonstration with some embedded hardware will be given.
The modular addition is used as a non-linear operation in ARX ciphers because it achieves the requirement of introducing non-linearity in a cryptographic primitive while only taking one clock cycle to execute on most modern architectures. This makes ARX ciphers especially fast in software implementations, but comes at the cost of making it harder to protect against side-channel information leakages using Boolean masking: the best known 2-shares masked adder for ARM Thumb micro-controllers takes 83 instructions to add two 32-bit numbers together. Our approach is to operate in bitsliced mode, performing 32 additions in parallel on a 32-bit microcontroller. We show that, even after taking into account the cost of bitslicing before and after the encryption, it is possible to achieve a higher throughput on the tested ciphers (CRAX and ChaCha20) when operating in bitsliced mode. Furthermore, we prove that no first-order information leakage is happening in either simulated power traces and power traces acquired from real hardware, after sufficient countermeasures are put into place to guard against pipeline leakages.
The modular addition is a popular building block when designing lightweight ciphers. While algorithms mainly based on the addition can reach very high performance, masking their implementations results in a huge penalty. Since efficient protection against side-channel attacks is a requirement in lots of use cases, we focus on optimizing the Boolean masking of the modular addition. Contrary to recent related work, we target evolving a masked full adder instead of parts of a parallel prefix adder. We study how techniques typically found in neural network evolution and genetic algorithms can be adapted in order to help in evolving an efficiently masked adder. We customize a well-known neuroevolution algorithm, develop an optimized masked adder with our new approach and implement the ChaCha20 cipher on an ARM Cortex-M3 controller. We compare the performance of the protected neuroevolved implementation to solutions found by traditional search methods. Moreover, the leakage of our new solution is validated by a t-test conducted with a leakage simulator. We present under which circumstances our masked implementation outperforms related work and prove the feasibility of successfully using neuroevolution when searching for complex Boolean networks.
The National Institute of Standards and Technology (NIST) started the standardization process for lightweight cryptography algorithms in 2018. By the end of the first round, 32 submissions have been selected as 2nd round candidates. NIST allowed designers of 2nd round submissions to provide small updates on both their specifications and implementation packages. In this work, we introduce a benchmarking framework for evaluating the performance of NIST Lightweight Cryptography (LWC) candidates on embedded platforms. We show the features and application of the framework and explain its design rationale. Moreover, we provide information on how we aim to present up-to-date performance figures throughout the NIST LWC competition. In this paper, we present an excerpt of our software benchmarking results regarding speed and memory requirements of selected ciphers. All up-to-date results, including benchmarking different test cases for multiple variants of each 2nd round algorithm on five different microcontrollers, are periodically published to a public website. While initially only the reference implementations were available, the ability of automatically testing the performance of the candidate algorithms on multiple platforms becomes especially relevant as more optimized implementations are developed. Finally, we show how the framework can be extended in different directions: support for more target platforms can be easily added, different kinds of algorithms can be tested, and other test metrics can be acquired. The focus of this paper should rather lay on the framework design and testing methodology than on the current results, especially for reference code.