While symbolic execution (SE) can discover software vulnerabilities, it has received limited practical adoption. A key barrier is that SE requires human expertise to understand the program’s state and prioritize paths to analyze. Traditionally, users controlled SE through programmatic API calls, but recent tooling now implements graphical user interfaces (GUI). However, it is unclear how these new features affect human-SE performance. To understand this impact, we conducted a controlled experiment where 24 vulnerability discovery experts were tasked with analyzing a binary using an SE tool with either API or GUI-based features. From this study, we identify (1) experts’ SE process, and (2) the impact of GUI-based features on human-SE performance. Then we propose recommendations to improve SE tool design.

Achieving reproducibility, quantity, and diversity in vulnerability datasets has long been viewed as an inherent three-way trade-off, where improving one dimension often comes at the cost of the others. In practice, reproducibility has been the dimension most often neglected. This has limited what can be automatically extracted from historical bug datasets, and has reduced their utility for downstream security research. In this work, we propose a method to produce a new security dataset which ensures reproducibility for diverse vulnerabilities at scale by identifying the key obstacles to large-scale bug reproduction and addressing them with general solutions. Using this method, we introduce full reproducibility to the largest open source software vulnerability dataset (OSS-Fuzz) and construct the ARVO dataset (an Atlas of Reproducible Vulnerabilities in Open-source software). ARVO is a large-scale dataset consisting of over 6,100 real-world vulnerabilities across 311 projects. Focusing on reproducibility, ARVO differs from existing datasets by providing each vulnerability in a form that can be consistently rebuilt, triggered, and analyzed across versions. Reproducibility also enables automatic identification of the corresponding patch for each vulnerability and supports direct interaction with vulnerabilities after code changes, capabilities that existing large-scale datasets do not provide. In our evaluation, ARVO successfully reproduces 81% of vulnerabilities and achieves 89.4% accuracy on the located patches. We also discuss ARVO's influence on both upstream practices and downstream security research.

The Parcel mechanism is a key component in inter-process communication in Android. However, due to the lack of security considerations, incorrect implementation of the Parcel mechanism can lead to security vulnerabilities. In the past decade, these security vulnerabilities have impacted numerous Android users. In this paper, we identify two major security issues of the Parcel mechanism. First, the reading and writing components are implemented inconsistently in some Parcelable classes, compromising data integrity. Second, malformed Parcels introduce the potential for Denial-of-Service (DoS) attacks on critical apps. We then describe two types of attacks to exploit these two issues: a privilege escalation attack and the Malformed Parcel DoS attack, the latter of which renders phones unusable and prevents users from accessing critical services. To understand the scope of our proposed attacks across the entire Android ecosystem, we perform the first large-scale analysis on 324 Android firmware samples and 10,161 Android apps. Among them, we identify 36 unique data mismatch vulnerabilities and 3,858 apps vulnerable to the DoS attack. We responsibly disclosed our findings to vendors, and 10 of them have been confirmed. Finally, we propose mitigations against the attacks.

Abstract We present a physically‐informed segmentation pipeline for producing fabric formwork for the casting and molding of arbitrary 3D objects. Fabric formworks are molds made by stitching together patches of textile fabric. The mechanical flexibility offered by these molds aids the fabrication of unconventional and complex geometries and allows for greater transportation ease for on‐site fabrication tasks. We employ an isotropic material model to estimate maximal strain directions that result when casting fluid is poured into the formwork. Our physically driven segmentation approach ensures seams and fiber directions align with these maximal strains. Experimental observations indicate that this alignment strategy significantly reduces twisting and shearing artifacts associated with the orthotropic deformation of woven fabrics. Aligning seams with strain directions further limits deformation of the formwork, improving fidelity to the input model. Moreover, our segmentation improves upon that of [ZFS*19] by promoting smoother seams and quad‐like patches, reducing the time and expertise needed to construct the formwork. We validate the efficacy of our pipeline by fabricating and simulating shapes of varying complexity, showing superior geometric reconstruction and fabrication ease.

Fraudulent shopping websites pose a significant threat to online consumers and legitimate businesses: in 2023, victims of such scams reported $392 million in losses to the Federal Trade Commission. This alarming trend not only impacts individuals but also erodes societal trust in e-commerce, necessitating urgent countermeasures. While previous studies have attempted to identify these fraudulent websites at scale, they face limitations such as potential bias in data collection, overreliance on easily manipulated features, and the lack of explainable results. This study explores the potential of Large Language Models (LLMs) in identifying fraudulent shopping websites, revealing that current LLMs underperform compared to existing machine learning models. To address this, we propose ScamNet, a fine-tuned LLM for explainable fraudulent shopping website detection. Our experimental results on real-world datasets demonstrate a breakthrough in detection performance from 22.35% detection rate to 95.59%, particularly in identifying subtle deceptive tactics such as using a legitimate-looking website template. ScamNet offers interpretable insights into its decision-making process, enhancing transparency and overcoming a key limitation of previous approaches.

Research Computing Infrastructures (RCIs) inte-grate high-performance computing, advanced data storage solutions, and sophisticated network protocols, connecting people, data, and computing resources to facilitate scientific collaboration in today's data-driven world. Access control is essential in such highly collaborative environments to prevent resource misutilization, safeguard data integrity, and allocate resources effectively, thereby enabling secure and trusted in-teractions among different users. However, unlocking the full potential of RCIs for collaborative research through effective access control requires more than technological exploration-it demands a deep, human-centered understanding of the stakeholders who operate and utilize these systems. In this paper, we present the first qualitative study that explores the human dimensions of RCI interactions, drawing insights from 24 key stakeholders, including researchers and system administrators, across 12 research institutions to ex-amine the collaborative practices, challenges, and needs with a focus on access control. Our findings reveal operational complexities and project-specific, trust-based resource-sharing dynamics, highlighting tensions between security and usability. Based on these insights, we provide stakeholder-driven rec-ommendations and requirements for adaptive, user-centered access control for RCIs, laying the groundwork for advancing human-centered security practices in RCIs.

Client-side anti-phishing methods are crucial for safeguarding individuals against phishing attacks, offering a proactive approach beyond traditional blocklisting strategies. This study expands the scope to include a comprehensive evaluation of client-side anti-phishing techniques within the Chrome browser, alongside an in-depth analysis of academic research in the field of phishing over the past five years. Our findings highlight the inherent limitations of current client-side anti-phishing measures, which demonstrated a detection rate of only 14% for phishing websites and blocked merely 10% of login-based phishing sites within the first hour, resulting in a substantial false negative rate. Additionally, our analysis reveals that attackers can readily circumvent these defenses by altering the content of phishing websites. The study also critically assesses recent academic contributions to understand their alignment and potential integration with client-side anti-phishing frameworks. Based on these insights, we propose targeted recommendations to enhance the efficacy and responsiveness of the client-side anti-phishing ecosystem, addressing the challenges of low detection coverage, slow response times, and high rates of false negatives.

The security landscape of software systems has witnessed considerable advancements through dynamic testing methodologies, especially fuzzing. Traditionally, fuzzing involves a sequential, cyclic process where software is tested to identify crashes. These crashes are then triaged and patched, leading to subsequent cycles that uncover further vulnerabilities. While effective, this method is not efficient as each cycle potentially reveals new issues previously obscured by earlier crashes, thus resulting in vulnerabilities being discovered sequentially.

Online services leverage various authentication methods with differing usability and reliability trade-offs, such as password-based or multi-factor authentication (MFA). However, financial service providers face a unique challenge; authenticating the user's legal identity, which involves verifying Personally Identifiable Information (PII), which we call PII-based authentication (PII-BA). These methods assume that PII is private; however, identity theft victimizes millions annually and exposes their PII to criminals.

Reporting phishing attacks can significantly shorten the time required to take down their operations and deter further victimization by the same phishing websites. However, little research has been conducted to understand the phishing reporting ecosystem and its effectiveness. In this paper, we comprehensively evaluate the phishing reporting ecosystem to identify the critical challenges people face and their concerns when reporting smishing, vishing, and phishing email attacks. First, we analyze the existing security advice and channels for reporting phishing attacks in both the public and private sectors. Then, we conduct a scenario-based experiment involving 89 participants to investigate what factors affect a participant’s decision to report a phishing attack and what challenges they face in preparing the report. Third, we report phishing attacks ourselves and monitor the status of the reported phishing websites to empirically measure how reports are acted upon and how that affects the reported phishing websites. Finally, we propose approaches under five major concern categories to mitigate the challenges that we discover in the phishing reporting ecosystem.

The complex and diverse nature of software systems necessitates a careful manual approach to unveil vulnerabilities, involving deep analysis, creative problem-solving, and specialized expertise. Like all complex tasks, it’s susceptible to mistakes stemming from cognitive limitations and behavioral factors that hinder optimal performance. Although there are significant research efforts focused on vulnerability discovery, little attention has been given to comprehending mistakes within the process. Understanding these mistakes could pave the way for better-designed education programs and automated tools, aiming to mitigate and prevent potential mistakes and enhance the efficiency of vulnerability research.In this paper, we leverage social media, specifically YouTube, to examine mistakes made by security content creators exploiting vulnerabilities in CTF-style challenges. Analyzing 30 screencasts from 11 hackers, we identified 124 distinct issues and investigated their types, underlying causes, and time investments. Additionally, we delved into the cognitive and behavioral aspects associated with these issues.

Dynamic Taint Analysis (DTA) is a widely used data flow tracking technique and helps security researchers in various applications, such as fuzzing and vulnerability analysis. One critical problem that affects its practicability is the significant overhead. According to our analysis, in some scenarios, the state-of-the-art works even slow the program execution efficiency by more than 100x. The high overhead is mainly because most of them conduct taint analysis at the instruction level and use just-in-time instrumentation methods to insert the tracking codes into the original program.In this paper, we propose AirTaint, a novel approach that combines the basic block-level abstraction of taint rules and assembly code-level instrumentation to conduct high-level dynamic taint analysis. Specifically, AirTaint leverages instruction-level emulation to identify the in and out operands (i.e., registers and memory variables) of each basic block, and then uses the existing taint engine to infer the taint rule abstraction for each basic block. Finally, it inserts the assembly code of the taint rule abstraction into the original program. While running, the program will execute the inserted taint analysis code quickly. In our evaluation based on 14 CVEs in 9 real-world applications, AirTaint detects all these vulnerabilities successfully. And in the comparison experiments, AirTaint performs much better than the existing tools in efficiency on 29 real-world applications, with maximum improvements of 931.0x, 5.97x, and 328.3x than libdft, SelectiveTaint, and TaintRabbit, respectively.

Leveraging a control flow hijacking primitive (CFHP) to gain root privileges is critical to attackers striving to exploit Linux kernel vulnerabilities. Such attack has become increasingly elusive as security researchers propose capable kernel security mitigations, leading to the development of complex (and, as a trade-off, brittle and unreliable) attack techniques to regain it. In this paper, we obviate the need for complexity by proposing RetSpill, a powerful yet elegant exploitation technique that employs user space data already present on the kernel stack for privilege escalation.

Despite recent advancements in malicious website detection and phishing mitigation, the security ecosystem has paid little attention to Fraudulent e-Commerce Websites (FCWs), such as fraudulent shopping websites, fake charities, and cryptocurrency scam websites. Even worse, there are no active large-scale mitigation systems or publicly available datasets for FCWs.In this paper, we first propose an efficient and automated approach to gather FCWs through crowdsourcing. We identify eight different types of non-phishing FCWs and derive key defining characteristics. Then, we find that anti-phishing mitigation systems, such as Google Safe Browsing, have a detection rate of just 0.46% on our dataset. We create a classifier, BEYOND PHISH, to identify FCWs using manually defined features based on our analysis. Validating BEYOND PHISH on never-before-seen (untrained and untested data) through a user study indicates that our system has a high detection rate and a low false positive rate of 98.34% and 1.34%, respectively. Lastly, we collaborated with a major Internet security company, Palo Alto Networks, as well as a major financial services provider, to evaluate our classifier on manually labeled real-world data. The model achieves a false positive rate of 2.46% and a 94.88% detection rate, showing potential for real-world defense against FCWs.

Black-box web application vulnerability scanners attempt to automatically identify vulnerabilities in web applications without access to the source code. However, they do so by using a manually curated list of vulnerability-inducing inputs, which significantly reduces the ability of a black-box scanner to explore the web application’s input space and which can cause false negatives. In addition, black-box scanners must attempt to infer that a vulnerability was triggered, which causes false positives.To overcome these limitations, we propose Witcher, a novel web vulnerability discovery framework that is inspired by grey-box coverage-guided fuzzing. Witcher implements the concept of fault escalation to detect both SQL and command injection vulnerabilities. Additionally, Witcher captures coverage information and creates output-derived input guidance to focus the input generation and, therefore, to increase the state-space exploration of the web application. On a dataset of 18 web applications written in PHP, Python, Node.js, Java, Ruby, and C, 13 of which had known vulnerabilities, Witcher was able to find 23 of the 36 known vulnerabilities (64%), and additionally found 67 previously unknown vulnerabilities, 4 of which received CVE numbers. In our experiments, Witcher outperformed state of the art scanners both in terms of number of vulnerabilities found, but also in terms of coverage of web applications.

Temporal memory safety violations, such as use-after-free (UAF) vulnerabilities, are a critical security issue for software written in memory-unsafe languages such as C and C++.

Phishing is a ubiquitous and increasingly sophisticated online threat. To evade mitigations, phishers try to "cloak" malicious content from defenders to delay their appearance on blacklists, while still presenting the phishing payload to victims. This cat-and-mouse game is variable and fast-moving, with many distinct cloaking methods---we construct a dataset identifying 2,933 real-world phishing kits that implement cloaking mechanisms. These kits use information from the host, browser, and HTTP request to classify traffic as either anti-phishing entity or potential victim and change their behavior accordingly.

Cross-site scripting (XSS) is the most common vulnerability class in web applications over the last decade. Much research attention has focused on building exploit mitigation defenses for this problem, but no technique provides adequate protection in the face of advanced attacks. One technique that bypasses XSS mitigations is the scriptless attack: a content injection technique that uses (among other options) CSS and HTML injection to infiltrate data. In studying this technique and others, we realized that the common property among the exploitation of all content injection vulnerabilities, including not just XSS and scriptless attacks, but also command injections and several others, is an unintended context switch in the victim program’s parsing engine that is caused by untrusted user input.

The academic computer security community has traditionally adopted peer review as an integral part of scientific publishing and dissemination, in a process that grows organically and nourishes itself by internal communications and intuitions, rather than repeatable experiments and investigations. Recently, key community members have shared a series of concerns regarding this process in public. To support or disprove some of these concerns, this paper presents the first qualitative study to examine the peer review process in the computer security field. Through semi-structured interviews (n=21) with Program Committee members, we systematically collect the reviewers’ insights on how papers are evaluated in top-tier security conferences and investigate their concerns regarding the current security peer review system. Based on the collected data, we identify several issues in the security review system: whereas some have been previously observed by the community (e.g., the randomness in reviewers’ decisions), others (e.g., reviewers have much more diverse and concrete opinions on the metrics of rejecting papers) have been observed for the first time in our study. Finally, through a series of recommendations, we aim to encourage the collaborative establishment of community norms that will significantly improve the security peer review process.

Exploring many execution paths in a binary program is essential to discover new vulnerabilities. Dynamic Symbolic Execution (DSE) is useful to trigger complex input conditions and enables an accurate exploration of a program while providing extensive crash replayability and semantic insights.

Phishing is a critical threat to Internet users. Although an extensive ecosystem serves to protect users, phishing websites are growing in sophistication, and they can slip past the ecosystem’s detection systems—and subsequently cause real-world damage—with the help of evasion techniques. Sophisticated client-side evasion techniques, known as cloaking, leverage JavaScript to enable complex interactions between potential victims and the phishing website, and can thus be particularly effective in slowing or entirely preventing automated mitigations. Yet, neither the prevalence nor the impact of client-side cloaking has been studied.In this paper, we present CrawlPhish, a framework for automatically detecting and categorizing client-side cloaking used by known phishing websites. We deploy CrawlPhish over 14 months between 2018 and 2019 to collect and thoroughly analyze a dataset of 112,005 phishing websites in the wild. By adapting state-of-the-art static and dynamic code analysis, we find that 35,067 of these websites have 1,128 distinct implementations of client-side cloaking techniques. Moreover, we find that attackers’ use of cloaking grew from 23.32% initially to 33.70% by the end of our data collection period. Detection of cloaking by our framework exhibited low false-positive and false-negative rates of 1.45% and 1.75%, respectively. We analyze the semantics of the techniques we detected and propose a taxonomy of eight types of evasion across three high-level categories: User Interaction, Fingerprinting, and Bot Behavior.Using 150 artificial phishing websites, we empirically show that each category of evasion technique is effective in avoiding browser-based phishing detection (a key ecosystem defense). Additionally, through a user study, we verify that the techniques generally do not discourage victim visits. Therefore, we propose ways in which our methodology can be used to not only improve the ecosystem’s ability to mitigate phishing websites with client-side cloaking, but also continuously identify emerging cloaking techniques as they are launched by attackers.

Industrial Control Systems (ICS) provide management and control capabilities for mission-critical utilities such as the nuclear, power, water, and transportation grids. Within ICS, Programmable Logic Controllers (PLCs) play a key role as they serve as a convenient bridge between the cyber and the physical worlds, e.g., controlling centrifuge machines in nuclear power plants. The critical roles that ICS and PLCs play have made them the target of sophisticated cyberattacks that are designed to disrupt their operation, which creates both social unrest and financial losses. In this context, honeypots have been shown to be highly valuable tools for collecting real data, e.g., malware payload, to better understand the many different methods and strategies that attackers use. However, existing state-of-the-art honeypots for PLCs lack sophisticated service simulations that are required to obtain valuable data. Worse, they cannot adapt while ICS malware keeps evolving, and attack patterns become more sophisticated. To overcome these shortcomings, we present HoneyPLC, a high-interaction, extensible, and malware collecting honeypot supporting a broad spectrum of PLCs models and vendors. Results from our experiments show that HoneyPLC exhibits a high level of camouflaging: it is identified as real devices by multiple widely used reconnaissance tools, including Nmap, Shodan's Honeyscore, the Siemens Step7 Manager, PLCinject, and PLCScan, with a high level of confidence. We deployed HoneyPLC on Amazon AWS and recorded a large amount of interesting interactions over the Internet, showing not only that attackers are in fact targeting ICS systems, but also that HoneyPLC can effectively engage and deceive them while collecting data samples for future analysis.

Organizations, such as companies and governments, created Security Operations Centers (SOCs) to defend against computer security attacks. SOCs are central defense groups that focus on security incident management with capabilities such as monitoring, preventing, responding, and reporting. They are one of the most critical defense components of a modern organization's defense. Despite their critical importance to organizations, and the high frequency of reported security incidents, only a few research studies focus on problems specific to SOCs. In this study, to understand and identify the issues of SOCs, we conducted 18 semi-structured interviews with SOC analysts and managers who work for organizations from different industry sectors. Through our analysis of the interview data, we identified technical and non-technical issues that exist in SOC. Moreover, we found inherent disagreements between SOC managers and their analysts that, if not addressed, could entail a risk to SOC efficiency and effectiveness. We distill these issues into takeaways that apply both to future academic research and to SOC management. We believe that research should focus on improving the efficiency and effectiveness of SOCs.

The studies on underground forums and marketplaces have significantly advanced our understandings of cybercrime workflows and underground economies. Researchers of underground economies have conducted comprehensive studies on public interactions. However, little research focuses on private interactions. The lack of the investigation on private interactions may cause misunderstandings on underground economies, as users in underground forums and marketplaces tend to share the minimal amount of information in public interactions and resort to private messages for follow-up conversations. In this paper, we propose methods to investigate the underground private interactions and we analyze a recently leaked dataset from Nulled.io. We present analyses on the contents and purposes of private messages. In addition, we design machine learning-based models that only use the publicly available information to detect if two underground users privately communicate with each other. Finally, we perform adversarial analysis to evaluate the robustness of the detector to different types of attacks.

Automated techniques and tools for finding, exploiting and patching vulnerabilities are maturing. In order to achieve an end goal such as winning a cyber-battle, these techniques and tools must be wielded strategically. Currently, strategy development in cyber - even with automated tools - is done manually, and is a bottleneck in practice. In this paper, we apply game theory toward the augmentation of the human decision-making process.,,Our work makes two novel contributions. First, previous work is limited by strong assumptions regarding the number of actors, actions, and choices in cyber-warfare. We develop a novel model of cyber-warfare that is more comprehensive than previous work, removing these limitations in the process. Second, we present an algorithm for calculating the optimal strategy of the players in our model. We show that our model is capable of finding better solutions than previous work within seconds, making computer-time strategic reasoning a reality. We also provide new insights, compared to previous models, on the impact of optimal strategies.

Developing a remote exploit is not easy. It requires a comprehensive understanding of a vulnerability and delicate techniques to bypass defense mechanisms. As a result, attackers may prefer to reuse an existing exploit and make necessary changes over developing a new exploit from scratch. One such adaptation is the replacement of the original shellcode (i.e., the attacker-injected code that is executed as the final step of the exploit) in the original exploit with a replacement shellcode, resulting in a modified exploit that carries out the actions desired by the attacker as opposed to the original exploit author. We call this a shellcode transplant. Current automated shellcode placement methods are insufficient because they over-constrain the replacement shellcode, and so cannot be used to achieve shellcode transplant. For example, these systems consider the shellcode as an integrated memory chunk and require that the execution path of the modified exploit must be same as the original one. To resolve these issues, we present ShellSwap, a system that uses symbolic tracing, with a combination of shellcode layout remediation and path kneading to achieve shellcode transplant. We evaluated the ShellSwap system on a combination of 20 exploits and 5 pieces of shellcode that are independently developed and different from the original exploit. Among the 100 test cases, our system successfully generated 88% of the exploits.