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Decentralized Finance (DeFi) offers a whole new investment experience and has quickly emerged as an enticing alternative to Centralized Finance (CeFi). Rapidly growing market size and active users, however, have also made DeFi a lucrative target for scams and hacks, with 1.95 billion USD lost in 2023. Unfortunately, no prior research thoroughly investigates DeFi users' security risk awareness levels and the adequacy of their risk mitigation strategies. Based on a semi-structured interview study (N = 14) and a follow-up survey (N = 493), this paper investigates DeFi users' security perceptions and commonly adopted practices, and how those affected by previous scams or hacks (DeFi victims) respond and try to recover their losses. Our analysis shows that users often prefer DeFi over CeFi due to their decentralized nature and strong profitability. Despite being aware that DeFi, compared to CeFi, is prone to more severe attacks, users are willing to take those risks to explore new investment opportunities. Worryingly, most victims do not learn from previous experiences; unlike victims studied through traditional systems, DeFi victims tend to find new services, without revising their security practices, to recover their losses quickly. The abundance of various DeFi services and opportunities allows victims to continuously explore new financial opportunities, and this reality seems to cloud their security priorities. Indeed, our results indicate that DeFi users' strong financial motivations outweigh their security concerns - much like those who are addicted to gambling. Our observations about victims' post-incident behaviors suggest that stronger control in the form of industry regulations would be necessary to protect DeFi users from future breaches., Comment: In Proceedings of the 33rd USENIX Security Symposium, Philadelphia, PA, USA, Aug. 2024

ARM Memory Tagging Extension (MTE) is a new hardware feature introduced in ARMv8.5-A architecture, aiming to detect memory corruption vulnerabilities. The low overhead of MTE makes it an attractive solution to mitigate memory corruption attacks in modern software systems and is considered the most promising path forward for improving C/C++ software security. This paper explores the potential security risks posed by speculative execution attacks against MTE. Specifically, this paper identifies new TikTag gadgets capable of leaking the MTE tags from arbitrary memory addresses through speculative execution. With TikTag gadgets, attackers can bypass the probabilistic defense of MTE, increasing the attack success rate by close to 100%. We demonstrate that TikTag gadgets can be used to bypass MTE-based mitigations in real-world systems, Google Chrome and the Linux kernel. Experimental results show that TikTag gadgets can successfully leak an MTE tag with a success rate higher than 95% in less than 4 seconds. We further propose new defense mechanisms to mitigate the security risks posed by TikTag gadgets.

Caches on the modern commodity CPUs have become one of the major sources of side-channel leakages and been abused as a new attack vector. To thwart the cache-based side-channel attacks, two types of countermeasures have been proposed: detection-based ones that limit the amount of microarchitectural traces an attacker can leave, and cache prefetching-and-locking techniques that claim to prevent such leakage by disallowing evictions on sensitive data. In this paper, we present the Prime+Retouch attack that completely bypasses these defense schemes by accurately inferring the cache activities with the metadata of the cache replacement policy. Prime+Retouch has three noticeable properties: 1) it incurs no eviction on the victim's data, allowing us to bypass the two known mitigation schemes, 2) it requires minimal synchronization of only one memory access to the attacker's pre-primed cache lines, and 3) it leaks data via non-shared memory, yet because underlying eviction metadata is shared. We demonstrate Prime+Retouch in two architectures: predominant Intel x86 and emerging Apple M1. We elucidate how Prime+Retouch can break the T-table implementation of AES with robust cache side-channel mitigations such as Cloak, under both normal and SGX-protected environments. We also manifest feasibility of the Prime+Retouch attack on the M1 platform imposing more restrictions where the precise measurement tools such as core clock cycle timer and performance counters are inaccessible to the attacker. Furthermore, we first demystify undisclosed cache architecture and its eviction policy of L1 data cache on Apple M1 architecture. We also devise a user-space noise-free cache monitoring tool by repurposing Intel TSX.

The recent past has seen an increasing interest in Heterogeneous Graph Neural Networks (HGNNs) since many real-world graphs are heterogeneous in nature, from citation graphs to email graphs. However, existing methods ignore a tree hierarchy among metapaths, which is naturally constituted by different node types and relation types. In this paper, we present HetTree, a novel heterogeneous tree graph neural network that models both the graph structure and heterogeneous aspects in a scalable and effective manner. Specifically, HetTree builds a semantic tree data structure to capture the hierarchy among metapaths. Existing tree encoding techniques aggregate children nodes by weighting the contribution of children nodes based on similarity to the parent node. However, we find that this tree encoding fails to capture the entire parent-children hierarchy by only considering the parent node. Hence, HetTree uses a novel subtree attention mechanism to emphasize metapaths that are more helpful in encoding parent-children relationships. Moreover, instead of separating feature learning from label learning or treating features and labels equally by projecting them to the same latent space, HetTree proposes to match them carefully based on corresponding metapaths, which provides more accurate and richer information between node features and labels. Our evaluation of HetTree on a variety of real-world datasets demonstrates that it outperforms all existing baselines on open benchmarks and efficiently scales to large real-world graphs with millions of nodes and edges.

Autonomous driving technology nowadays targets to level 4 or beyond, but the researchers are faced with some limitations for developing reliable driving algorithms in diverse challenges. To promote the autonomous vehicles to spread widely, it is important to address safety issues on this technology. Among various safety concerns, the sensor blockage problem by severe weather conditions can be one of the most frequent threats for multi-task learning based perception algorithms during autonomous driving. To handle this problem, the importance of the generation of proper datasets is becoming more significant. In this paper, a synthetic road dataset with sensor blockage generated from real road dataset BDD100K is suggested in the format of BDD100K annotation. Rain streaks for each frame were made by an experimentally established equation and translated utilizing the image-to-image translation network based on style transfer. Using this dataset, the degradation of the diverse multi-task networks for autonomous driving, such as lane detection, driving area segmentation, and traffic object detection, has been thoroughly evaluated and analyzed. The tendency of the performance degradation of deep neural network-based perception systems for autonomous vehicle has been analyzed in depth. Finally, we discuss the limitation and the future directions of the deep neural network-based perception algorithms and autonomous driving dataset generation based on image-to-image translation., Comment: Under Review

Trustworthiness of generative language models (GLMs) is crucial in their deployment to critical decision making systems. Hence, certified risk control methods such as selective prediction and conformal prediction have been applied to mitigating the hallucination problem in various supervised downstream tasks. However, the lack of appropriate correctness metric hinders applying such principled methods to language generation tasks. In this paper, we circumvent this problem by leveraging the concept of textual entailment to evaluate the correctness of the generated sequence, and propose two selective generation algorithms which control the false discovery rate with respect to the textual entailment relation (FDR-E) with a theoretical guarantee: $\texttt{SGen}^{\texttt{Sup}}$ and $\texttt{SGen}^{\texttt{Semi}}$. $\texttt{SGen}^{\texttt{Sup}}$, a direct modification of the selective prediction, is a supervised learning algorithm which exploits entailment-labeled data, annotated by humans. Since human annotation is costly, we further propose a semi-supervised version, $\texttt{SGen}^{\texttt{Semi}}$, which fully utilizes the unlabeled data by pseudo-labeling, leveraging an entailment set function learned via conformal prediction. Furthermore, $\texttt{SGen}^{\texttt{Semi}}$ enables to use more general class of selection functions, neuro-selection functions, and provides users with an optimal selection function class given multiple candidates. Finally, we demonstrate the efficacy of the $\texttt{SGen}$ family in achieving a desired FDR-E level with comparable selection efficiency to those from baselines on both open and closed source GLMs. Code and datasets are provided at https://github.com/ml-postech/selective-generation., Comment: 10 pages

Contrastive learning, along with its variations, has been a highly effective self-supervised learning method across diverse domains. Contrastive learning measures the distance between representations using cosine similarity and uses cross-entropy for representation learning. Within the same framework of cosine-similarity-based representation learning, margins have played a significant role in enhancing face and speaker recognition tasks. Interestingly, despite the shared reliance on the same similarity metrics and objective functions, contrastive learning has not actively adopted margins. Furthermore, decision-boundary-based explanations are the only ones that have been used to explain the effect of margins in contrastive learning. In this work, we propose a new perspective to understand the role of margins based on gradient analysis. Based on the new perspective, we analyze how margins affect gradients of contrastive learning and separate the effect into more elemental levels. We separately analyze each and provide possible directions for improving contrastive learning. Our experimental results demonstrate that emphasizing positive samples and scaling gradients depending on positive sample angles and logits are the keys to improving the generalization performance of contrastive learning in both seen and unseen datasets, and other factors can only marginally improve performance.

Recently, deep learning models have shown the potential to predict breast cancer risk and enable targeted screening strategies, but current models do not consider the change in the breast over time. In this paper, we present a new method, PRIME+, for breast cancer risk prediction that leverages prior mammograms using a transformer decoder, outperforming a state-of-the-art risk prediction method that only uses mammograms from a single time point. We validate our approach on a dataset with 16,113 exams and further demonstrate that it effectively captures patterns of changes from prior mammograms, such as changes in breast density, resulting in improved short-term and long-term breast cancer risk prediction. Experimental results show that our model achieves a statistically significant improvement in performance over the state-of-the-art based model, with a C-index increase from 0.68 to 0.73 (p < 0.05) on held-out test sets., Comment: MICCAI 2023 accepted

Fuzzing has gained in popularity for software vulnerability detection by virtue of the tremendous effort to develop a diverse set of fuzzers. Thanks to various fuzzing techniques, most of the fuzzers have been able to demonstrate great performance on their selected targets. However, paradoxically, this diversity in fuzzers also made it difficult to select fuzzers that are best suitable for complex real-world programs, which we call selection burden. Communities attempted to address this problem by creating a set of standard benchmarks to compare and contrast the performance of fuzzers for a wide range of applications, but the result was always a suboptimal decision - the best-performing fuzzer on average does not guarantee the best outcome for the target of a user's interest. To overcome this problem, we propose an automated, yet non-intrusive meta-fuzzer, called autofz, to maximize the benefits of existing state-of-the-art fuzzers via dynamic composition. To an end user, this means that, instead of spending time on selecting which fuzzer to adopt, one can simply put all of the available fuzzers to autofz, and achieve the best, optimal result. The key idea is to monitor the runtime progress of the fuzzers, called trends (similar in concept to gradient descent), and make a fine-grained adjustment of resource allocation. This is a stark contrast to existing approaches - autofz deduces a suitable set of fuzzers of the active workload in a fine-grained manner at runtime. Our evaluation shows that autofz outperforms any best-performing individual fuzzers in 11 out of 12 available benchmarks and beats the best, collaborative fuzzing approaches in 19 out of 20 benchmarks. Moreover, on average, autofz found 152% more bugs than individual fuzzers, and 415% more bugs than collaborative fuzzing., Comment: 22 pages. Extended version of USENIX Security'23 paper

Blockchains with smart contracts are distributed ledger systems that achieve block-state consistency among distributed nodes by only allowing deterministic operations of smart contracts. However, the power of smart contracts is enabled by interacting with stochastic off-chain data, which in turn opens the possibility to undermine the block-state consistency. To address this issue, an oracle smart contract is used to provide a single consistent source of external data; but, simultaneously, this introduces a single point of failure, which is called the oracle problem. To address the oracle problem, we propose an adaptive conformal consensus (ACon$^2$) algorithm that derives a consensus set of data from multiple oracle contracts via the recent advance in online uncertainty quantification learning. Interesting, the consensus set provides a desired correctness guarantee under distribution shift and Byzantine adversaries. We demonstrate the efficacy of the proposed algorithm on two price datasets and an Ethereum case study. In particular, the Solidity implementation of the proposed algorithm shows the potential practicality of the proposed algorithm, implying that online machine learning algorithms are applicable to address security issues in blockchains., Comment: Accepted to USENIX Security 2023

Memory-safety bugs introduce critical software-security issues. Rust provides memory-safe mechanisms to avoid memory-safety bugs in programming, while still allowing unsafe escape hatches via unsafe code. However, the unsafe code that enhances the usability of Rust provides clear spots for finding memory-safety bugs in Rust source code. In this paper, we claim that these unsafe spots can still be identifiable in Rust binary code via machine learning and be leveraged for finding memory-safety bugs. To support our claim, we propose the tool textttrustspot, that enables reverse engineering to learn an unsafe classifier that proposes a list of functions in Rust binaries for downstream analysis. We empirically show that the function proposals by textttrustspot can recall $92.92\%$ of memory-safety bugs, while it covers only $16.79\%$ of the entire binary code. As an application, we demonstrate that the function proposals are used in targeted fuzzing on Rust packages, which contribute to reducing the fuzzing time compared to non-targeted fuzzing.

The accurate localization of inserted medical tubes and parts of human anatomy is a common problem when analyzing chest radiographs and something deep neural networks could potentially automate. However, many foreign objects like tubes and various anatomical structures are small in comparison to the entire chest X-ray, which leads to severely unbalanced data and makes training deep neural networks difficult. In this paper, we present a simple yet effective `Only-One-Object-Exists' (OOOE) assumption to improve the deep network's ability to localize small landmarks in chest radiographs. The OOOE enables us to recast the localization problem as a classification problem and we can replace commonly used continuous regression techniques with a multi-class discrete objective. We validate our approach using a large scale proprietary dataset of over 100K radiographs as well as publicly available RANZCR-CLiP Kaggle Challenge dataset and show that our method consistently outperforms commonly used regression-based detection models as well as commonly used pixel-wise classification methods. Additionally, we find that the method using the OOOE assumption generalizes to multiple detection problems in chest X-rays and the resulting model shows state-of-the-art performance on detecting various tube tips inserted to the patient as well as patient anatomy., Comment: AMAI Workshop, MICCAI 2022

Low-volume Denial-of-Service ({\mu}DoS) attacks have been demonstrated to fundamentally bypass traditional DoS mitigation schemes based on the flow and volume of network packets. In this paper, we propose a data-driven approach, called ROKI, that accurately tracks internal resource utilization and allocation associated with each packet (or session), making it possible to tame resource exhaustion caused by {\mu}DoS attacks. Since ROKI focuses on capturing the symptom of DoS, it can effectively mitigate previously unknown {\mu}DoS attacks. To enable a finer-grain resource tracking, ROKI provided in concept the accounting capabilities to each packet itself, so we called data-driven: it monitors resource utilization at the link, network, transport layers in the kernel, as well as application layers, and attributes back to the associated packet. Given the resource usages of each packet, ROKI can reclaim (or prevent) the system resources from malicious packets (or attackers) whenever it encounters system-wide resource exhaustion. To provide lightweight resource tracking, ROKI carefully multiplexes hardware performance counters whenever necessary. Our evaluation shows that ROKI's approach is indeed effective in mitigating real-world {\mu}DoS attacks with negligible performance overheads - incurring 3%-4% throughput and latency overheads on average when the system is throttled.

Since Bitcoin appeared in 2009, over 6,000 different cryptocurrency projects have followed. The cryptocurrency world may be the only technology where a massive number of competitors offer similar services yet claim unique benefits, including scalability, fast transactions, and security. But are these projects really offering unique features and significant enhancements over their competitors? To answer this question, we conducted a large-scale empirical analysis of code maintenance activities, originality and security across 592 crypto projects. We found that about half of these projects have not been updated for the last six months; over two years, about three-quarters of them disappeared, or were reported as scams or inactive. We also investigated whether 11 security vulnerabilities patched in Bitcoin were also patched in other projects. We found that about 80% of 510 C-language-based cryptocurrency projects have at least one unpatched vulnerability, and the mean time taken to fix the vulnerability is 237.8 days. Among those 510 altcoins, we found that at least 157 altcoins are likely to have been forked from Bitcoin, about a third of them containing only slight changes from the Bitcoin version from which they were forked. As case studies, we did a deep dive into 20 altcoins (e.g., Litecoin, FujiCoin, and Feathercoin) similar to the version of Bitcoin used for the fork. About half of them did not make any technically meaningful change - failing to comply with the promises (e.g., about using Proof of Stake) made in their whitepapers.

Web browsers are integral parts of everyone's daily life. They are commonly used for security-critical and privacy sensitive tasks, like banking transactions and checking medical records. Unfortunately, modern web browsers are too complex to be bug free (e.g., 25 million lines of code in Chrome), and their role as an interface to the cyberspace makes them an attractive target for attacks. Accordingly, web browsers naturally become an arena for demonstrating advanced exploitation techniques by attackers and state-of-the-art defenses by browser vendors. Web browsers, arguably, are the most exciting place to learn the latest security issues and techniques, but remain as a black art to most security researchers because of their fast-changing characteristics and complex code bases. To bridge this gap, this paper attempts to systematize the security landscape of modern web browsers by studying the popular classes of security bugs, their exploitation techniques, and deployed defenses. More specifically, we first introduce a unified architecture that faithfully represents the security design of four major web browsers. Second, we share insights from a 10-year longitudinal study on browser bugs. Third, we present a timeline and context of mitigation schemes and their effectiveness. Fourth, we share our lessons from a full-chain exploit used in 2020 Pwn2Own competition. and the implication of bug bounty programs to web browser security. We believe that the key takeaways from this systematization can shed light on how to advance the status quo of modern web browsers, and, importantly, how to create secure yet complex software in the future.

This paper presents an in-kernel, hardware-based control-flow integrity (CFI) protection, called PAL, that utilizes ARM's Pointer Authentication (PA). It provides three important benefits over commercial, state-of-the-art PA-based CFIs like iOS's: 1) enhancing CFI precision via automated refinement techniques, 2) addressing hindsight problems of PA for in kernel uses such as preemptive hijacking and brute-forcing attacks, and 3) assuring the algorithmic or implementation correctness via post validation. PAL achieves these goals in an OS-agnostic manner, so could be applied to commodity OSes like Linux and FreeBSD. The precision of the CFI protection can be adjusted for better performance or improved for better security with minimal engineering efforts if a user opts in to. Our evaluation shows that PAL incurs negligible performance overhead: e.g., <1% overhead for Apache benchmark and 3~5% overhead for Linux perf benchmark on the latest Mac mini (M1). Our post-validation approach helps us ensure the security invariant required for the safe uses of PA inside the kernel, which also reveals new attack vectors on the iOS kernel. PAL as well as the CFI-protected kernels will be open sourced.

A wide range of binary analysis applications, such as bug discovery, malware analysis and code clone detection, require recovery of contextual meanings on a binary code. Recently, binary analysis techniques based on machine learning have been proposed to automatically reconstruct the code representation of a binary instead of manually crafting specifics of the analysis algorithm. However, the existing approaches utilizing machine learning are still specialized to solve one domain of problems, rendering recreation of models for different types of binary analysis. In this paper, we propose DeepSemantic utilizing BERT in producing the semantic-aware code representation of a binary code. To this end, we introduce well-balanced instruction normalization that holds rich information for each of instructions yet minimizing an out-of-vocabulary (OOV) problem. DeepSemantic has been carefully designed based on our study with large swaths of binaries. Besides, DeepSemantic leverages the essence of the BERT architecture into re-purposing a pre-trained generic model that is readily available as a one-time processing, followed by quickly applying specific downstream tasks with a fine-tuning process. We demonstrate DeepSemantic with two downstream tasks, namely, binary similarity comparison and compiler provenance (i.e., compiler and optimization level) prediction. Our experimental results show that the binary similarity model outperforms two state-of-the-art binary similarity tools, DeepBinDiff and SAFE, 49.84% and 15.83% on average, respectively., Comment: 16 pages

Global corporations (e.g., Google and Microsoft) have recently introduced a new model of cloud services, fuzzing-as-a-service (FaaS). Despite effectively alleviating the cost of fuzzing, the model comes with privacy concerns. For example, the end user has to trust both cloud and service providers who have access to the application to be fuzzed. Such concerns are due to the platform is under the control of its provider and the application and the fuzzer are highly coupled. In this paper, we propose P2FaaS, a new ecosystem that preserves end user's privacy while providing FaaS in the cloud. The key idea of P2FaaS is to utilize Intel SGX for preventing cloud and service providers from learning information about the application. Our preliminary evaluation shows that P2FaaS imposes 45% runtime overhead to the fuzzing compared to the baseline. In addition, P2FaaS demonstrates that, with recently introduced hardware, Intel SGX Card, the fuzzing service can be scaled up to multiple servers without native SGX support.

We present Recipe, a principled approach for converting concurrent DRAM indexes into crash-consistent indexes for persistent memory (PM). The main insight behind Recipe is that isolation provided by a certain class of concurrent in-memory indexes can be translated with small changes to crash-consistency when the same index is used in PM. We present a set of conditions that enable the identification of this class of DRAM indexes, and the actions to be taken to convert each index to be persistent. Based on these conditions and conversion actions, we modify five different DRAM indexes based on B+ trees, tries, radix trees, and hash tables to their crash-consistent PM counterparts. The effort involved in this conversion is minimal, requiring 30-200 lines of code. We evaluated the converted PM indexes on Intel DC Persistent Memory, and found that they outperform state-of-the-art, hand-crafted PM indexes in multi-threaded workloads by up-to 5.2x. For example, we built P-CLHT, our PM implementation of the CLHT hash table by modifying only 30 LOC. When running YCSB workloads, P-CLHT performs up to 2.4x better than Cacheline-Conscious Extendible Hashing (CCEH), the state-of-the-art PM hash table., Comment: 3pages: Added one more reference

We present SplitFS, a file system for persistent memory (PM) that reduces software overhead significantly compared to state-of-the-art PM file systems. SplitFS presents a novel split of responsibilities between a user-space library file system and an existing kernel PM file system. The user-space library file system handles data operations by intercepting POSIX calls, memory-mapping the underlying file, and serving the read and overwrites using processor loads and stores. Metadata operations are handled by the kernel PM file system (ext4 DAX). SplitFS introduces a new primitive termed relink to efficiently support file appends and atomic data operations. SplitFS provides three consistency modes, which different applications can choose from, without interfering with each other. SplitFS reduces software overhead by up-to 4x compared to the NOVA PM file system, and 17x compared to ext4-DAX. On a number of micro-benchmarks and applications such as the LevelDB key-value store running the YCSB benchmark, SplitFS increases application performance by up to 2x compared to ext4 DAX and NOVA while providing similar consistency guarantees.

Heap exploitation techniques to abuse the metadata of allocators have been widely studied since they are application independent and can be used in restricted environments that corrupt only metadata. Although prior work has found several interesting exploitation techniques, they are ad-hoc and manual, which cannot effectively handle changes or a variety of allocators. In this paper, we present a new naming scheme for heap exploitation techniques that systematically organizes them to discover the unexplored space in finding the techniques and ArcHeap, the tool that finds heap exploitation techniques automatically and systematically regardless of their underlying implementations. For that, ArcHeap generates a set of heap actions (e.g. allocation or deallocation) by leveraging fuzzing, which exploits common designs of modern heap allocators. Then, ArcHeap checks whether the actions result in impact of exploitations such as arbitrary write or overlapped chunks that efficiently determine if the actions can be converted into the exploitation technique. Finally, from these actions, ArcHeap generates Proof-of-Concept code automatically for an exploitation technique. We evaluated ArcHeap with real-world allocators --- ptmalloc, jemalloc, and tcmalloc --- and custom allocators from the DARPA Cyber Grand Challenge. ArcHeap successfully found 14 out of 16 known exploitation techniques and found five new exploitation techniques in ptmalloc. Moreover, ArcHeap found several exploitation techniques for jemalloc, tcmalloc, and even for the custom allocators. Further, ArcHeap can automatically show changes in exploitation techniques along with version change in ptmalloc using differential testing.