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  1. Side-channel attacks that leak sensitive information through a computing device's interaction with its physical environment have proven to be a severe threat to devices' security, particularly when adversaries have unfettered physical access to the device. Traditional approaches for leakage detection measure the physical properties of the device. Hence, they cannot be used during the design process and fail to provide root cause analysis. An alternative approach that is gaining traction is to automate leakage detection by modeling the device. The demand to understand the scope, benefits, and limitations of the proposed tools intensifies with the increase in the number of proposals. In this SoK, we classify approaches to automated leakage detection based on the model's source of truth. We classify the existing tools on two main parameters: whether the model includes measurements from a concrete device and the abstraction level of the device specification used for constructing the model. We survey the proposed tools to determine the current knowledge level across the domain and identify open problems. In particular, we highlight the absence of evaluation methodologies and metrics that would compare proposals' effectiveness from across the domain. We believe that our results help practitioners who want to use automated leakagemore »detection and researchers interested in advancing the knowledge and improving automated leakage detection.« less
    Free, publicly-accessible full text available May 30, 2023
  2. The recent Spectre attack first showed how to inject incorrect branch targets into a victim domain by poisoning microarchitectural branch prediction history. In this paper, we generalize injection-based methodologies to the memory hierarchy by directly injecting incorrect, attacker-controlled values into a victim's transient execution. We propose Load Value Injection (LVI) as an innovative technique to reversely exploit Meltdown-type microarchitectural data leakage. LVI abuses that faulting or assisted loads, executed by a legitimate victim program, may transiently use dummy values or poisoned data from various microarchitectural buffers, before eventually being re-issued by the processor. We show how LVI gadgets allow to expose victim secrets and hijack transient control flow. We practically demonstrate LVI in several proof-of-concept attacks against Intel SGX enclaves, and we discuss implications for traditional user process and kernel isolation. State-of-the-art Meltdown and Spectre defenses, including widespread silicon-level and microcode mitigations, are orthogonal to our novel LVI techniques. LVI drastically widens the spectrum of incorrect transient paths. Fully mitigating our attacks requires serializing the processor pipeline with lfence instructions after possibly every memory load. Additionally and even worse, due to implicit loads, certain instructions have to be blacklisted, including the ubiquitous x86 ret instruction. Intel plans compiler and assembler-basedmore »full mitigations that will allow at least SGX enclave programs to remain secure on LVI-vulnerable systems. Depending on the application and optimization strategy, we observe extensive overheads of factor 2 to 19 for prototype implementations of the full mitigation.« less
  3. Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who can observe all traffic traveling between the user’s computer and the secure network. In this work we investigate these attacks under a different attack model, in which the adversary is capable of sending a small amount of malicious JavaScript code to the target user’s computer. The malicious code mounts a cache side-channel attack, which exploits the effects of contention on the CPU’s cache, to identify other websites being browsed. The effectiveness of this attack scenario has never been systematically analyzed, especially in the open-world model which assumes that the user is visiting a mix of both sensitive and non-sensitive sites. We show that cache website fingerprinting attacks in JavaScript are highly feasible. Specifically, we use machine learning techniques to classify traces of cache activity. Unlike prior works, which try to identify cache conflicts, our work measures the overall occupancy of the last-level cache. We show that our approach achieves high classification accuracy inmore »both the open-world and the closed-world models. We further show that our attack is more resistant than network-based fingerprinting to the effects of response caching, and that our techniques are resilient both to network-based defenses and to side-channel countermeasures introduced to modern browsers as a response to the Spectre attack. To protect against cache-based website fingerprinting, new defense mechanisms must be introduced to privacy-sensitive browsers and websites. We investigate one such mechanism, and show that generating artificial cache activity reduces the effectiveness of the attack and completely eliminates it when used in the Tor Browser« less