Malicious Design Research Articles

TVF: A Metric for Quantifying Vulnerability Against Hardware Trojan Attacks

The need for metrics for quantifying trustworthiness of electronic hardware against diverse threats on its integrity and confidentiality has greatly increased due to the increasing reliance on the untrusted global supply chain. Hardware Trojans, or malicious design alterations, has emerged as a major threat to hardware integrity and garnered significant interest in recent times due to its catastrophic potential. Effective protection against hardware Trojan attacks, however, requires well-defined metrics, which fall into two broad classes: 1) measure of a design’s vulnerability to Trojan insertion and 2) measure of effectiveness of a defense solution—a design or verification/test approach—against Trojan attacks, which is often represented as Trojan coverage. The former is important to assess the level of difficulty an adversary would encounter to insert a hard-to-detect Trojan. Previous efforts have assigned vulnerability as a function of the number of suspect nets identified in a design or are required to enumerate a subspace of Trojans. However, these values simplify the problem of hardware Trojan insertion and leave much of the subsequent analysis regarding the viable Trojan space unmeasured. In this article, we address this critical gap by presenting Trojan vulnerability factor ( TVF), a metric for quantifying a design’s vulnerability to Trojan insertion via maximal clique analysis. With such analysis, we can frame the threat to more accurately represent the Trojan behavior and quantify the level of effort required for a designer to cover these Trojan triggers without needing to directly consider Trojan trigger sizes. We also introduce soft thresholding to account for suspect nets, which lie at the boundary of a design. Experimental results highlight the benefits of the proposed approach over existing Trojan vulnerability metrics. Finally, we demonstrate scalability to large designs through partitioning and clique sampling-based estimations.

Securing an Accelerator-rich System from Flooding-based Denial-of-Service Attacks

Increasing demand for high performance and energy efficiency along with time-to-market pressures have led to a growing number of third-party accelerators in modern System-on-Chips (SoCs). However, the third-party accelerators may introduce vulnerabilities due to malicious intent or elusive design bugs that escape through verification processes. These vulnerabilities can be exploited by various attackers which can corrupt the entire system. Flooding is one such attack that is triggered by a malicious third-party accelerator to obstruct on-chip network communication by injecting useless packets leading to Denial-of-Service. To secure accelerator-rich SoCs from flooding attacks, we propose a two-step attack detection framework that leverages machine learning (ML) models to detect an attack scenario. The ML-based solution along with hierarchical feature data aggregation and feature packet prioritization provides an accurate attack detection with minimal performance overheads. Experimental evaluations with real accelerator benchmarks show the effectiveness of our framework achieving a detection accuracy of up to 97 % across various ML classifiers.

Facing Immersive “Post-Truth” in AIVR?

In recent years, prevalent global societal issues related to fake news, fakery, misinformation, and disinformation were brought to the fore, leading to the construction of descriptive labels such as “post-truth” to refer to the supposedly new emerging era. Thereby, the (mis-)use of technologies such as AI and VR has been argued to potentially fuel this new loss of “ground-truth”, for instance, via the ethically relevant deepfakes phenomena and the creation of realistic fake worlds, presumably undermining experiential veracity. Indeed, unethical and malicious actors could harness tools at the intersection of AI and VR (AIVR) to craft what we call immersive falsehood, fake immersive reality landscapes deliberately constructed for malicious ends. This short paper analyzes the ethically relevant nature of the background against which such malicious designs in AIVR could exacerbate the intentional proliferation of deceptions and falsities. We offer a reappraisal expounding that while immersive falsehood could manipulate and severely jeopardize the inherently affective constructions of social reality and considerably complicate falsification processes, humans may neither inhabit a post-truth nor a post-falsification age. Finally, we provide incentives for future AIVR safety work, ideally contributing to a future era of technology-augmented critical thinking.

Side-Channel Hardware Trojan for Provably-Secure SCA-Protected Implementations

Hardware Trojans have drawn the attention of academia, industry, and government agencies. Effective detection mechanisms and countermeasures against such malicious designs can only be developed when there is a deep understanding of how hardware Trojans can be built in practice, in particular, Trojans specifically designed to avoid detection. In this article, we present a mechanism to introduce an extremely stealthy hardware Trojan into cryptographic primitives equipped with provably-secure first-order side-channel countermeasures. Once the Trojan is triggered, the malicious design exhibits exploitable side-channel leakage, leading to successful key recovery attacks. Generally, such a Trojan requires neither addition nor removal of any logic which makes it extremely hard to detect. On ASICs, it can be inserted by subtle manipulations at the subtransistor level and on FPGAs by changing the routing of particular signals, leading to zero logic overhead. The underlying concept is based on modifying a securely masked hardware implementation in such a way that running the device at a particular clock frequency violates one of its essential properties, leading to exploitable leakage. We apply our technique to a threshold implementation of the PRESENT block cipher realized in two different CMOS technologies and show that triggering the Trojan makes the ASIC prototypes vulnerable.

Functional Analysis Attacks on Logic Locking

Logic locking refers to a set of techniques that can protect integrated circuits (ICs) from counterfeiting, piracy and malicious functionality changes by an untrusted foundry. It achieves these goals by introducing new inputs, called key inputs, and additional logic to an IC such that the circuit produces the correct output only when the key inputs are set to specific values. The correct values of the key inputs are kept secret from the untrusted foundry and programmed after manufacturing and before distribution, rendering piracy, counterfeiting and malicious design changes infeasible. The security of logic locking relies on the assumption that the untrusted foundry cannot infer the correct values of the key inputs by analysis of the circuit. This paper proposes Functional Analysis attacks on Logic Locking algorithms (abbreviated as FALL attacks). FALL attacks have two stages. Their first stage is dependent on the locking algorithm and involves analyzing structural and functional properties of locked circuits to identify a list of potential locking keys. The second stage is algorithm agnostic and introduces a powerful addition to SAT-based attacks called key confirmation. Key confirmation can identify the correct key from a list of alternatives and works even on circuits that are resilient to the SAT attack. In comparison to past work, the FALL attack is more practical as it can often succeed (90% of successful attempts in our experiments) by only analyzing the locked netlist, without requiring oracle access to an unlocked circuit. Our experimental evaluation shows that FALL attacks are able to defeat 65 out of 80 (81%) circuits locked using Stripped-Functionality Logic Locking (SFLL-HD).

Where is the language of liberty

In George Orwell’s Nineteen Eighty-Four, the state of Oceania is devising a new language out of the old, pre-revolutionary English: Newspeak. “When Newspeak had been adopted once and for all and Oldspeak forgotten,” explains Orwell ([1949] 2008, 312), “a heretical thought . . . should be literally unthinkable.” Modern cog- nitive science provides fascinating evidence for the influence of our language on our thinking (Boroditsky 2017). Unlike Oceania’s Newspeak, our English is not the result of malicious (re-)design. But does it serve us well in thinking clearly and critically about matters of ethics, politics and economics? The compilers of the Newspeak dictionary in Oceania’s “Ministry of Truth” first and foremost purge the old language of words for undesirable ideas (Orwell [1949] 2008, 312). Indeed, it is difficult to think about ideas for which we lack words. Even today, there are Amazonian tribes that do not have words for numbers. The speak- ers of an anumeric language fail even most simple arithmetical tasks (Caleb 2017). However, it seems that what they lack is not innate mathematical ability but a mathematical language—and culture—that we just take for granted (ibid.) Economic thinking needs cultivation no less than mathematical thinking. This means not only developing an adequate terminol- ogy but also avoiding a misleading one. Not only is it difficult to think without suitable words, but it is also difficult to think clearly and coolly with ambiguous and emotionally loaded words (Jevons [1888] 2010, 27; Salmon [1963] 1984, sec. 32). Most of all, it is difficult to think critically with words that carry hidden assumptions into our reasoning. Calling a substance “medicine” means taking for granted that its effects are beneficial, without explicitly stating and specifying this crucial premise. Thus, we may not only fail to prop- erly investigate the question, but to ask it in the first place.

Hardware Trojan Detection Game: A Prospect-Theoretic Approach

Outsourcing integrated circuit (IC) manufacturing to offshore foundries has grown exponentially in recent years. Given the critical role of ICs in the control and operation of vehicular systems and other modern engineering designs, such offshore outsourcing has led to serious security threats due to the potential of insertion of hardware trojans—malicious designs that, when activated, can lead to highly detrimental consequences. In this paper, a novel game-theoretic framework is proposed to analyze the interactions between a hardware manufacturer, acting as an attacker, and an IC testing facility, acting as a defender. The problem is formulated as a noncooperative game in which the attacker must decide on the type of trojan that it inserts while taking into account the detection penalty as well as the damage caused by the trojan. Meanwhile, the resource-constrained defender must decide on the best testing strategy that allows optimizing its overall utility which accounts for both damages and the fines. The proposed game is based on the robust behavioral framework of prospect theory (PT) which allows capturing the potential uncertainty, risk, and irrational behavior in the decision making of both the attacker and defender. For both the standard rational expected utility (EUT) case and the PT case, a novel algorithm based on fictitious play is proposed and shown to converge to a mixed-strategy Nash equilibrium. For an illustrative case study, thorough analytical results are derived for both EUT and PT to study the properties of the reached equilibrium as well as the impact of key system parameters such as the defender-set fine. Simulation results assess the performance of the proposed framework under both EUT and PT and show that the use of PT will provide invaluable insights on the outcomes of the proposed hardware trojan game, in particular, and system security, in general.

Reverse Engineering Digital Circuits Using Structural and Functional Analyses

Integrated circuits (ICs) are now designed and fabricated in a globalized multivendor environment making them vulnerable to malicious design changes, the insertion of hardware Trojans/malware, and intellectual property (IP) theft. Algorithmic reverse engineering of digital circuits can mitigate these concerns by enabling analysts to detect malicious hardware, verify the integrity of ICs, and detect IP violations. In this paper, we present a set of algorithms for the reverse engineering of digital circuits starting from an unstructured netlist and resulting in a high-level netlist with components such as register files, counters, adders, and subtractors. Our techniques require no manual intervention and experiments show that they determine the functionality of ${>}{45\%}$ and up to 93% of the gates in each of the test circuits that we examine. We also demonstrate that our algorithms are scalable to real designs by experimenting with a very large, highly-optimized system-on-chip (SOC) design with over 375000 combinational elements. Our inference algorithms cover 68% of the gates in this SOC. We also demonstrate that our algorithms are effective in aiding a human analyst to detect hardware Trojans in an unstructured netlist.

The Verifiability of Value Judgments

IT IS a historical fact that the advancement of the (or the Geisteswissenschaften, as the Germans say) stands no comparison with the brilliant success of the natural sciences. The reasons in terms of which this discrepancy has been explained are varied. A well-known explanation is that in terms of the peculiarity of the subject matter of the social sciences. In the spirit of Bacon's maxim, Scientia propter potentiam (which was in the French enlightenment-I forget by which philosopher or savant-expanded into Savoir pour prevoir,prevoir pourpouvoir), one is inclined to measure the achievements of a science by its ability to predict (at least this holds with respect to dynamic sciences). Now, predictability depends on the fulfilment of two essential conditions: (a) the knowledge of laws asserting that if an event of the kind A happens, then an event of the kind B will, with a finite degree of probability, happen; (b) the possibility of acquiring adequate factual knowledge of the so-called conditions-the state at a given time-of the system whose behavior is to be predicted. The subject matter of the social sciences involves at some point or other human behavior. But reflective behavior, so the apology runs, is by its very nature unpredictable. For suppose a law of behavior is proclaimed, according to which a given individual or group may be expected to act in a specified way at a specified time. Then the individuals about whose behavior a prediction is made may, by a decree of free willand perhaps motivated by the malicious design to prove the futility of a dynamics of human behavior-decide to refrain from acting the way they are expected to act. For example, if Marxists proclaim that the revolution of the proletariat and the ensuing era of universal communism is necessitated by the dialectic law of history, according to which, as the Hegelian jargon runs, every negation must be negated, and such a prediction should penetrate to capitalist ears, the capitalistic nations will presumably take measures to falsify the prediction and to prevent expropriation. The planets are no conscious agents (unless, indeed, Kepler's opinion that they are guided, in their regular courses, by spirits, should still be upheld); hence they never had a chance tostudy the laws of planetary motion; that is why astronomers are so successful in their predictions. Part of the initial conditions of human behavior, however, are motives; and human individuals may be motivated by the desire to falsify a prediction of their behavior; hence the very prediction of human behavior may, as it were, create new forces which modify the initial state upon which the prediction was based. If an analogous predicament obtained in astronomy, if, fantastic as it sounds, every prediction brought a new planet into existence, successful predictions would likewise be in principle impossible; for it would be in principle impossible to base a prediction upon a complete knowledge of initial conditions. The reason why I have outlined this way of accounting for the superiority of