Cryptology ePrint Archive: Report 2017/783

HAL- The Missing Piece of the Puzzle for Hardware Reverse Engineering, Trojan Detection and Insertion

Marc Fyrbiak and Sebastian Wallat and Pawel Swierczynski and Max Hoffmann and Sebastian Hoppach and Matthias Wilhelm and Tobias Weidlich and Russell Tessier and Christof Paar

Abstract: Hardware manipulations pose a serious threat to numerous systems, ranging from myriads of smart-X devices to military systems. In many attack scenarios an adversary merely has access to the low-level, potentially obfuscated gate-level netlist. In general, the attacker possesses minimal information and faces the costly and time-consuming task of reverse engineering the design to identify security-critical circuitry, followed by insertion of a meaningful hardware Trojan. These challenges have been considered only in passing by the research community. The contribution of this work is threefold: First, we present HAL, a comprehensive reverse engineering and manipulation framework for gate-level netlists. HAL allows automating defensive design analysis (e.g., including arbitrary Trojan detection algorithms with minimal effort) as well as offensive reverse engineering and targeted logic insertion. Second, we present a novel static analysis Trojan detection technique ANGEL, which outperforms state-of-the-art algorithms by a considerable reduction of the false-positive rate. Furthermore, we demonstrate that ANGEL is capable of automatically detecting Trojans obfuscated with DeTrust. Third, we demonstrate how a malicious party can semi-automatically inject hardware Trojans in third-party designs. We present reverse engineering algorithms to disarm and trick cryptographic self-tests, and subtly leak cryptographic keys without any apriori knowledge of the designís internal workings.

Category / Keywords: applications / Hardware Reverse Engineering, Hardware Trojans, Hardware Trojan Detection

Date: received 18 Aug 2017

Contact author: sebastian wallat at rub de

Available format(s): PDF | BibTeX Citation

Version: 20170818:122231 (All versions of this report)

Short URL: ia.cr/2017/783

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