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1. CAS-Lock: A Security-Corruptibility Trade-off Resilient Logic Locking Scheme

   Shakya, Bicky; Xu, Xiaolin; Tehranipoor, Mark; Forte, Domenic (January 2020, IACR transactions on cryptographic hardware and embedded systems)

   Logic locking has recently been proposed as a solution for protecting gate level semiconductor intellectual property (IP). However, numerous attacks have been mounted on this technique, which either compromise the locking key or restore the original circuit functionality. SAT attacks leverage golden IC information to rule out all incorrect key classes, while bypass and removal attacks exploit the limited output corruptibility and/or structural traces of SAT-resistant locking schemes. In this paper, we propose a new lightweight locking technique: CAS-Lock (cascaded locking) which nullifies both SAT and bypass attacks, while simultaneously maintaining nontrivial output corruptibility. This property of CAS-Lock is in stark contrast to the well-accepted notion that there is an inherent trade-off between output corruptibility and SAT resistance. We theoretically and experimentally validate the SAT resistance of CAS-Lock, and show that it reduces the attack to brute-force, regardless of its construction. Further, we evaluate its resistance to recently proposed approximate SAT attacks (i.e., AppSAT). We also propose a modified version of CAS-Lock (mirrored CAS-Lock or M-CAS) to protect against removal attacks. M-CAS allows a trade-off evaluation between removal attack and SAT attack resiliency, while incurring minimal area overhead. We also show how M-CAS parameters such as the implemented Boolean function and selected key can be tuned by the designer so that a desired level of protection against all known attacks can be achieved. 

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2. A Complete Study of P.K.I. (PKI’s Known Incidents)

   https://doi.org/10.2139/ssrn.3425554  

   Serrano, Nicolas; Hadan, Hilda; Camp, L. Jean (September 2019, SSRN Electronic Journal)

   null (Ed.)

   In this work, we report on a comprehensive analysis of PKI resulting from Certificate Authorities’ (CAs) behavior using over 1300 instances. We found several cases where CAs designed business models that favored the issuance of digital certificates over the guidelines of the CA Forum, root management programs, and other PKI requirements. Examining PKI from the perspective of business practices, we identify a taxonomy of failures and identify systemic vulnerabilities in the governance and practices in PKI. Notorious cases include the “backdating” of digital certificates, the issuance of these for MITM attempts, the lack of verification of a requester’s identity, and the unscrupulous issuance of rogue certificates. We performed a detailed study of 379 of these 1300 incidents. Using this sample, we developed a taxonomy of the different types of incidents and their causes. For each incident, we determined if the incident was disclosed by the problematic CA. We also noted the Root CA and the year of the incident. We identify the failures in terms of business practices, geography, and outcomes from CAs. We analyzed the role of Root Program Owners (RPOs) and differentiated their policies. We identified serial and chronic offenders in the PKI trusted root programs. Some of these were distrusted by RPOs, while others remain being trusted despite failures. We also identified cases where the concentration of power of RPOs was arguably a contributing factor in the incident. We identify these cases where there is a risk of concentration of power and the resulting conflict of interests. Our research is the first comprehensive academic study addressing all verified reported incidents. We approach this not from a machine learning or statistical perspective but, rather, we identify each reported public incident with a focus on identifying patterns of individual lapses. Here we also have a specific focus on the role of CAs and RPOs. Building on this study, we identify the issues in incentive structures that are contributors to the problems. 

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3. Let's Encrypt: An Automated Certificate Authority to Encrypt the Entire Web

   https://doi.org/10.1145/3319535.3363192  

   Aas, Josh; Barnes, Richard; Case, Benton; Durumeric, Zakir; Eckersley, Peter; Flores-López, Alan; Halderman, J. Alex; Hoffman-Andrews, Jacob; Kasten, James; Rescorla, Eric; et al (November 2019, CCS '19: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security)

   null (Ed.)

   Let's Encrypt is a free, open, and automated HTTPS certificate authority (CA) created to advance HTTPS adoption to the entire Web. Since its launch in late 2015, Let's Encrypt has grown to become the world's largest HTTPS CA, accounting for more currently valid certificates than all other browser-trusted CAs combined. By January 2019, it had issued over 538 million certificates for 223 million domain names. We describe how we built Let's Encrypt, including the architecture of the CA software system (Boulder) and the structure of the organization that operates it (ISRG), and we discuss lessons learned from the experience. We also describe the design of ACME, the IETF-standard protocol we created to automate CA--server interactions and certificate issuance, and survey the diverse ecosystem of ACME clients, including Certbot, a software agent we created to automate HTTPS deployment. Finally, we measure Let's Encrypt's impact on the Web and the CA ecosystem. We hope that the success of Let's Encrypt can provide a model for further enhancements to the Web PKI and for future Internet security infrastructure. 

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4. Click This, Not That: Extending Web Authentication with Deception

   https://doi.org/10.1145/3433210.3453088  

   Barron, Timothy; So, Johnny; Nikiforakis, Nick (May 2021, Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security (ASIACCS))

   null (Ed.)

   With phishing attacks, password breaches, and brute-force login attacks presenting constant threats, it is clear that passwords alone are inadequate for protecting the web applications entrusted with our personal data. Instead, web applications should practice defense in depth and give users multiple ways to secure their accounts. In this paper we propose login rituals, which define actions that a user must take to authenticate, and web tripwires, which define actions that a user must not take to remain authenticated. These actions outline expected behavior of users familiar with their individual setups on applications they use often. We show how we can detect and prevent intrusions from web attackers lacking this familiarity with their victim's behavior. We design a modular and application-agnostic system that incorporates these two mechanisms, allowing us to add an additional layer of deception-based security to existing web applications without modifying the applications themselves. Next to testing our system and evaluating its performance when applied to five popular open-source web applications, we demonstrate the promising nature of these mechanisms through a user study. Specifically, we evaluate the detection rate of tripwires against simulated attackers, 88% of whom clicked on at least one tripwire. We also observe web users' creation of personalized login rituals and evaluate the practicality and memorability of these rituals over time. Out of 39 user-created rituals, all of them are unique and 79% of users were able to reproduce their rituals even a week after creation. 

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5. DeepHammer: Depleting the Intelligence of Deep Neural Networks through Targeted Chain of Bit Flips

   Yao, Fan; Rakin, Adnan Siraj; Fan, Deliang (August 2020, 29th {USENIX} Security Symposium ({USENIX} Security 20))

   null (Ed.)

   Security of machine learning is increasingly becoming a major concern due to the ubiquitous deployment of deep learning in many security-sensitive domains. Many prior studies have shown external attacks such as adversarial examples that tamper the integrity of DNNs using maliciously crafted inputs. However, the security implication of internal threats (i.e., hardware vulnerabilities) to DNN models has not yet been well understood. In this paper, we demonstrate the first hardware-based attack on quantized deep neural networks–DeepHammer–that deterministically induces bit flips in model weights to compromise DNN inference by exploiting the rowhammer vulnerability. DeepHammer performs an aggressive bit search in the DNN model to identify the most vulnerable weight bits that are flippable under system constraints. To trigger deterministic bit flips across multiple pages within a reasonable amount of time, we develop novel system-level techniques that enable fast deployment of victim pages, memory-efficient rowhammering and precise flipping of targeted bits. DeepHammer can deliberately degrade the inference accuracy of the victim DNN system to a level that is only as good as random guess, thus completely depleting the intelligence of targeted DNN systems. We systematically demonstrate our attacks on real systems against 11 DNN architectures with 4 datasets corresponding to different application domains. Our evaluation shows that DeepHammer is able to successfully tamper DNN inference behavior at run-time within a few minutes. We further discuss several mitigation techniques from both algorithm and system levels to protect DNNs against such attacks. Our work highlights the need to incorporate security mechanisms in future deep learning systems to enhance the robustness against hardware-based deterministic fault injections. 

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