Software developers face risks of leaking their software secrets, such as API keys or passwords, which can result in significant harm. Secret management tools (SMTs), such as HashiCorp Vault Secrets or Infisical, are highly recommended by industry, academia, and security guidelines to manage secrets securely. SMTs are designed to help developers secure their secrets in a central location, yet secrets leaks are still commonplace, and developers report difficulty in learning how to setup and use SMTs. While SMTs typically come with publicly available help resources (e.g., tool documentation and interfaces), it is unclear if these actually help developers learn to effectively use SMTs. Without usable help resources that onboards developers, quick adoption and effective use of SMTs may be unrealistic.

GitGuardian monitored secrets exposure in public GitHub repositories and reported that developers leaked over 12 million secrets (database and other credentials) in 2023, indicating a 113% surge from 2021. Despite the availability of secret detection tools, developers ignore the tools' reported warnings because of false positives ($\mathbf{2 5 \% - 9 9 \%}$). However, each secret protects assets of different values accessible through asset identifiers (a DNS name and a public or private IP address). The asset information for a secret can aid developers in filtering false positives and prioritizing secret removal from the source code. However, existing secret detection tools do not provide the asset information, thus presenting difficulty to developers in filtering secrets only by looking at the secret value or finding the assets manually for each reported secret. The goal of our study is to aid software practitioners in prioritizing secrets removal by providing the assets information protected by the secrets through our novel static analysis tool. We present AssetHarvester, a static analysis tool to detect secret-asset pairs in a repository. Since the location of the asset can be distant from where the secret is defined, we investigated secret-asset co-location patterns and found four patterns. To identify the secret-asset pairs of the four patterns, we utilized three approaches (pattern matching, data flow analysis, and fast-approximation heuristics). We curated a benchmark of 1,791 secret-asset pairs of four database types extracted from 188 public GitHub repositories to evaluate the performance of AssetHarvester. AssetHarvester demonstrates precision of (97%), recall (90 %), and F1-score (94 %) in detecting secret-asset pairs. Our findings indicate that data flow analysis employed in AssetHarvester detects secret-asset pairs with 0 % false positives and aids in improving the recall of secret detection tools. Additionally, AssetHarvester shows 43 % increase in precision for database secret detection compared to existing detection tools through the detection of assets, thus reducing developer's alert fatigue.

Telephone spam has been among the highest network security concerns for users for many years. In response, industry and government have deployed new technologies and regulations to curb the problem, and academic and industry researchers have provided methods and measurements to characterize robocalls. Have these efforts borne fruit? Are the research characterizations reliable, and have the prevention and deterrence mechanisms succeeded? In this paper, we address these questions through analysis of data from several independently-operated vantage points, ranging from industry and academic voice honeypots to public enforcement and consumer complaints, some with over 5 years of historic data. We first describe how we address the non-trivial methodological challenges of comparing disparate data sources, including comparing audio and transcripts from about 3 Million voice calls. We also detail the substantial coherency of these diverse perspectives, which dramatically strengthens the evidence for the conclusions we draw about robocall characterization and mitigation while highlighting advantages of each approach. Among our many findings, we find that unsolicited calls are in slow decline, though complaints and call volumes remain high. We also find that robocallers have managed to adapt to STIR/SHAKEN, a mandatory call authentication scheme. In total, our findings highlight the most promising directions for future efforts to characterize and stop telephone spam.

Security advisories are the primary channel of communication for discovered vulnerabilities in open-source software, but they often lack crucial information. Specifically, 63% of vulnerability database reports are missing their patch links, also referred to as vulnerability fixing commits (VFCs). This paper introduces VFCFinder, a tool that generates the top-five ranked set of VFCs for a given security advisory using Natural Language Programming Language (NL-PL) models. VFCFinder achieves a 96.6% recall for finding the correct VFC within the Top-5 commits, and an 80.0% recall for the Top-1 ranked commit. VFCFinder generalizes to nine different programming languages and outperforms state-of-the-art approaches by 36 percentage points in terms of Top-1 recall. As a practical contribution, we used VFCFinder to backfill over 300 missing VFCs in the GitHub Security Advisory (GHSA) database. All of the VFCs were accepted and merged into the GHSA database. In addition to demonstrating a practical pairing of security advisories to VFCs, our general open-source implementation will allow vulnerability database maintainers to drastically improve data quality, supporting efforts to secure the software supply chain.

Unsolicited telephone calls that facilitate fraud or unlawful telemarketing continue to overwhelm network users and the regulators who prosecute them. The first step in prosecuting phone abuse is traceback --- identifying the call originator. This fundamental investigative task currently requires hours of manual effort per call. In this paper, we introduce Jäger, a distributed secure call traceback system. Jäger can trace a call in a few seconds, even with partial deployment, while cryptographically preserving the privacy of call parties, carrier trade secrets like peers and call volume, and limiting the threat of bulk analysis. We establish definitions and requirements of secure traceback, then develop a suite of protocols that meet these requirements using witness encryption, oblivious pseudorandom functions, and group signatures. We prove these protocols secure in the universal composibility framework. We then demonstrate that Jäger has low compute and bandwidth costs per call, and these costs scale linearly with call volume. Jäger provides an efficient, secure, privacy-preserving system to revolutionize telephone abuse investigation with minimal costs to operators.

Cellular networks are essential everywhere, and securing them is increasingly important as attacks against them become more prevalent and powerful. All cellular network generations bootstrap new radio connections with unauthenticated System Information Blocks (SIBs), which provide critical parameters needed to identify and connect to the network. Many cellular network attacks require exploiting SIBs. Authenticating these messages would eliminate whole classes of attack, from spoofed emergency alerts to fake base stations.

In 2022, the Anti-Phishing Working Group reported a 70% increase in SMS and voice phishing attacks. Hard data on SMS phishing is hard to come by, as are insights into how SMS phishers operate. Lack of visibility prevents law enforcement, regulators, providers, and researchers from understanding and confronting this growing problem. In this paper, we present the results of extracting phishing messages from over 200 million SMS messages posted over several years on 11 public SMS gateways on the web. From this dataset we identify 67,991 phishing messages, link them together into 35,128 campaigns based on sharing near-identical content, then identify related campaigns that share infrastructure to identify over 600 distinct SMS phishing operations. This expansive vantage point enables us to determine that SMS phishers use commodity cloud and web infrastructure in addition to self-hosted URL shorteners, their infrastructure is often visible days or weeks on certificate transparency logs earlier than their messages, and they reuse existing phishing kits from other phishing modalities. We are also the first to examine in-place network defenses and identify the public forums where abuse facilitators advertise openly. These methods and findings provide industry and researchers new directions to explore to combat the growing problem of SMS phishing.

Background: According to GitGuardian's monitoring of public GitHub repositories, secrets sprawl continued accelerating in 2022 by 67% compared to 2021, exposing over 10 million secrets (API keys and other credentials). Though many open-source and proprietary secret detection tools are available, these tools output many false positives, making it difficult for developers to take action and teams to choose one tool out of many. To our knowledge, the secret detection tools are not yet compared and evaluated. Aims: The goal of our study is to aid developers in choosing a secret detection tool to reduce the exposure of secrets through an empirical investigation of existing secret detection tools. Method: We present an evaluation of five open-source and four proprietary tools against a benchmark dataset. Results: The top three tools based on precision are: GitHub Secret Scanner (75%), Gitleaks (46%), and Commercial X (25%), and based on recall are: Gitleaks (88%), SpectralOps (67%) and TruffleHog (52%). Our manual analysis of reported secrets reveals that false positives are due to employing generic regular expressions and ineffective entropy calculation. In contrast, false negatives are due to faulty regular expressions, skipping specific file types, and insufficient rulesets. Conclusions: We recommend developers choose tools based on secret types present in their projects to prevent missing secrets. In addition, we recommend tool vendors update detection rules periodically and correctly employ secret verification mechanisms by collaborating with API vendors to improve accuracy.

Software depends on upstream projects that regularly fix vulnerabilities, but the documentation of those vulnerabilities is often unreliable or unavailable. Automating the collection of existing vulnerability fixes is essential for downstream projects to reliably update their dependencies due to the sheer number of dependencies in modern software. Prior efforts rely solely on incomplete databases or imprecise or inaccurate statistical analysis of upstream repositories. In this paper, we introduce Differential Alert Analysis (DAA) to discover vulnerability fixes in software projects. In contrast to statistical analysis, DAA leverages static analysis security testing (SAST) tools, which reason over code context and semantics. We provide a language-independent implementation of DAA and show that for Python and Java based projects, DAA has high precision for a ground-truth dataset of vulnerability fixes — even with noisy and low-precision SAST tools. We then use DAA in two large-scale empirical studies covering several prominent ecosystems, finding hundreds of resolved alerts, including many never publicly disclosed. DAA thus provides a powerful, accurate primitive for software projects, code analysis tools, vulnerability databases, and researchers to characterize and enhance the security of software supply chains.

Throughout 2021, GitGuardian's monitoring of public GitHub repositories revealed a two-fold increase in the number of secrets (database credentials, API keys, and other credentials) exposed compared to 2020, accumulating more than six million secrets. To our knowledge, the challenges developers face to avoid checked-in secrets are not yet characterized. The goal of our paper is to aid researchers and tool developers in understanding and prioritizing opportunities for future research and tool automation for mitigating checked-in secrets through an empirical investigation of challenges and solutions related to checked-in secrets. We extract 779 questions related to checked-in secrets on Stack Exchange and apply qualitative analysis to determine the challenges and the solutions posed by others for each of the challenges. We identify 27 challenges and 13 solutions. The four most common challenges, in ranked order, are: (i) store/version of secrets during deployment; (ii) store/version of secrets in source code; (iii) ignore/hide of secrets in source code; and (iv) sanitize VCS history. The three most common solutions, in ranked order, are: (i) move secrets out of source code/version control and use template config file; (ii) secret management in deployment; and (iii) use local environment variables. Our findings indicate that the same solution has been mentioned to mitigate multiple challenges. However, our findings also identify an increasing trend in questions lacking accepted solutions substantiating the need for future research and tool automation on managing secrets.

According to GitGuardian’s monitoring of public GitHub repositories, the exposure of secrets (API keys and other credentials) increased two-fold in 2021 compared to 2020, totaling more than six million secrets. However, no benchmark dataset is publicly available for researchers and tool developers to evaluate secret detection tools that produce many false positive warnings. The goal of our paper is to aid researchers and tool developers in evaluating and improving secret detection tools by curating a benchmark dataset of secrets through a systematic collection of secrets from open-source repositories. We present a labeled dataset of source codes containing 97,479 secrets (of which 15,084 are true secrets) of various secret types extracted from 818 public GitHub repositories. The dataset covers 49 programming languages and 311 file types.

Commercially-available software defined networking (SDN) technologies will play an important role in protecting the on-premises resources that remain as enterprises transition to zero trust architectures. However, existing solutions assume the entire network resides in a single geographic location, requiring organizations with multiple sites to manually ensure consistency of security policy across all sites. In this paper, we present MSNetViews, which extends a single, globally-defined and managed, enterprise network security policy to many geographically distributed sites. Each site operates independently and enforces a site-specific policy slice that is dynamically parameterized with user location as employees roam between sites. We build a prototype of MSNetViews and show that for an enterprise with globally distributed sites, the average time for policy state to settle after a user roams to a new site is well below two seconds. As such, we demonstrate that multisite organizations can efficiently protect their on-premises network-attached devices via a single global perspective.

Traditional enterprise security relies on network perimeters to define and enforce network security policies. Emerging application-focused Zero Trust architectures attempt to address this long-standing challenge by moving business applications to the cloud and performing enhanced identity and access control checks within a web gateway. However, these solutions ignore the security needs of workstations, development servers, and device management interfaces. In this work, we propose Network Views (abbrev. NetViews) for least-privilege network access control where each host has a different, limited view of the other hosts and services within a network. We present an SDN-based design and demonstrate that our implementation has network latency and throughput comparable to baseline reactive forwarding. We further provide an optimization for multi-connection flows that significantly reduces both redundant access control checks and forwarding state storage in switches. As such, NetViews provides a practical primitive for removing the reliance on security perimeters within enterprise networks.

Desktop operating systems, including macOS, Windows 10, and Linux, are adopting the application-based security model pervasive in mobile platforms. In Linux, this transition is part of the movement towards two distribution-independent application platforms: Flatpak and Snap. This paper provides the first analysis of sandbox policies defined for Flatpak and Snap applications, covering 283 applications contained in both platforms. First, we find that 90.1% of Snaps and 58.3% of Flatpak applications studied are contained by tamperproof sandboxes. Further, we find evidence that package maintainers actively attempt to define least-privilege application policies. However, defining policy is difficult and error-prone. When studying the set of matching applications that appear in both Flatpak and Snap app stores, we frequently found policy mismatches: e.g., the Flatpak version has a broad privilege (e.g., file access) that the Snap version does not, or vice versa. This work provides confidence that Flatpak and Snap improve Linux platform security while highlighting opportunities for improvement.

Cellular networks connect nearly every human on the planet; they consequently have visibility into location data and voice, SMS, and data contacts and communications. Such near-universal visibility represents a significant threat to the privacy of mobile subscribers. In 5G networks, end-user mobile device manufacturers assign a Permanent Equipment Identifier (PEI) to every new device. Mobile operators legitimately use the PEI to blocklist stolen devices from the network to discourage device theft, but the static PEI also provides a mechanism to uniquely identify and track subscribers. Advertisers and data brokers have also historically abused the PEI for data fusion of location and analytics data, including private data sold by cellular providers.

The long-awaited smart home revolution has arrived, and with it comes the challenge of managing dozens of potentially vulnerable network devices by average users. While research has developed techniques to fingerprint these devices, and even provide for sophisticated network access control models, such techniques are too complex for end users to manage, require sophisticated systems or unavailable public device descriptions, and proposed network policies have not been tested against real device behaviors. As a result, none of these solutions are available to users today.

The always-on, always-connected nature of smart home devices complicates Internet-of-Things (IoT) security and privacy. Unlike traditional hosts, IoT devices constantly send sensor, state, and heartbeat data to cloud-based servers. These data channels require reliable, routine communication, which is often at odds with an IoT device's storage and power constraints. Although recent efforts such as pervasive encryption have addressed protecting data intransit, there remains little insight into designing mechanisms for protecting integrity and availability for always-connected devices. This paper seeks to better understand smart home device security by studying the vendor design decisions surrounding IoT telemetry messaging protocols, specifically, the behaviors taken when an IoT device loses connectivity. To understand this, we hypothesize and evaluate sensor blinding and state confusion attacks, measuring their effectiveness against an array of smart home IoT device types. Our analysis uncovers pervasive failure in designing telemetry that reports data to the cloud, and buffering that fails to properly cache undelivered data. We uncover that 22 of 24 studied devices suffer from critical design flaws that (1) enable attacks to transparently disrupt the reporting of device status alerts or (2) prevent the uploading of content integral to the device's core functionality. We conclude by considering the implications of these findings and offer directions for future defense. While the state of the art is rife with implementation flaws, there are several countermeasures IoT vendors could take to reduce their exposure to attacks of this nature.

The widespread adoption of smart home IoT devices has led to a broad and heterogeneous market with flawed security designs and privacy concerns. While the quality of IoT device software is unlikely to be fixed soon, there is great potential for a network-based solution that helps protect and inform consumers. Unfortunately, the encrypted and proprietary protocols used by devices limit the value of traditional network-based monitoring techniques. In this paper, we present HomeSnitch, a building block for enhancing smart home transparency and control by classifying IoT device communication by semantic behavior (e.g., heartbeat, firmware check, motion detection). HomeSnitch ignores payload content (which is often encrypted) and instead identifies behaviors using features of connection-oriented application data unit exchanges, which represent application-layer dialog between clients and servers. We evaluate HomeSnitch against an independent labeled corpus of IoT device network flows and correctly detect over 99% of behaviors. We further deployed HomeSnitch in a home environment and empirically evaluated its ability to correctly classify known behaviors as well as discover new behaviors. Through these efforts, we demonstrate the utility of network-level services to classify behaviors of and enforce control on smart home devices.

Recent years have seen the Short Message Service (SMS) become a critical component of the security infrastructure, assisting with tasks including identity verification and second-factor authentication. At the same time, this messaging infrastructure has become dramatically more open and connected to public networks than ever before. However, the implications of this openness, the security practices of benign services, and the malicious misuse of this ecosystem are not well understood. In this article, we provide a comprehensive longitudinal study to answer these questions, analyzing over 900,000 text messages sent to public online SMS gateways over the course of 28 months. From this data, we uncover the geographical distribution of spam messages, study SMS as a transmission medium of malicious content, and find that changes in benign and malicious behaviors in the SMS ecosystem have been minimal during our collection period. The key takeaways of this research show many services sending sensitive security-based messages through an unencrypted medium, implementing low entropy solutions for one-use codes, and behaviors indicating that public gateways are primarily used for evading account creation policies that require verified phone numbers. This latter finding has significant implications for combating phone-verified account fraud and demonstrates that such evasion will continue to be difficult to detect and prevent.

Android applications are frequently plagiarized or repackaged, and software obfuscation is a recommended protection against these practices. However, there is very little data on the overall rates of app obfuscation, the techniques used, or factors that lead to developers to choose to obfuscate their apps. In this paper, we present the first comprehensive analysis of the use of and challenges to software obfuscation in Android applications. We analyzed 1.7 million free Android apps from Google Play to detect various obfuscation techniques, finding that only 24.92% of apps are obfuscated by the developer. To better understand this rate of obfuscation, we surveyed 308 Google Play developers about their experiences and attitudes about obfuscation. We found that while developers feel that apps in general are at risk of plagiarism, they do not fear theft of their own apps. Developers also report difficulties obfuscating their own apps. To better understand, we conducted a follow-up study where the vast majority of 70 participants failed to obfuscate a realistic sample app even while many mistakenly believed they had been successful. These findings have broad implications both for improving the security of Android apps and for all tools that aim to help developers write more secure software.

The global telephone network is relied upon by billions every day. Central to its operation is the Signaling System 7 (SS7) protocol, which is used for setting up calls, managing mobility, and facilitating many other network services. This protocol was originally built on the assumption that only a small number of trusted parties would be able to directly communicate with its core infrastructure. As a result, SS7 - as a feature - allows all parties with core access to redirect and intercept calls for any subscriber anywhere in the world. Unfortunately, increased interconnectivity with the SS7 network has led to a growing number of illicit call redirection attacks. We address such attacks with Sonar, a system that detects the presence of SS7 redirection attacks by securely measuring call audio round-trip times between telephony devices. This approach works because redirection attacks force calls to travel longer physical distances than usual, thereby creating longer end-to-end delay. We design and implement a distance bounding-inspired protocol that allows us to securely characterize the round-trip time between the two endpoints. We then use custom hardware deployed in 10 locations across the United States and a redirection testbed to characterize how distance affects round trip time in phone networks. We develop a model using this testbed and show Sonar is able to detect 70.9% of redirected calls between call endpoints of varying attacker proximity (300-7100 miles) with low false positive rates (0.3%). Finally, we ethically perform actual SS7 redirection attacks on our own devices with the help of an industry partner to demonstrate that Sonar detects 100% of such redirections in a real network (with no false positives). As such, we demonstrate that telephone users can reliably detect SS7 redirection attacks and protect the integrity of their calls.

Detecting and explaining the nature of attacks in distributed web services is often difficult -- determining the nature of suspicious activity requires following the trail of an attacker through a chain of heterogeneous software components including load balancers, proxies, worker nodes, and storage services. Unfortunately, existing forensic solutions cannot provide the necessary context to link events across complex workflows, particularly in instances where application layer semantics (e.g., SQL queries, RPCs) are needed to understand the attack. In this work, we present a transparent provenance-based approach for auditing web services through the introduction of Network Provenance Functions (NPFs). NPFs are a distributed architecture for capturing detailed data provenance for web service components, leveraging the key insight that mediation of an application's protocols can be used to infer its activities without requiring invasive instrumentation or developer cooperation. We design and implement NPF with consideration for the complexity of modern cloud-based web services, and evaluate our architecture against a variety of applications including DVDStore, RUBiS, and WikiBench to show that our system imposes as little as 9.3% average end-to-end overhead on connections for realistic workloads. Finally, we consider several scenarios in which our system can be used to concisely explain attacks. NPF thus enables the hassle-free deployment of semantically rich provenance-based auditing for complex applications workflows in the Cloud.

Abstract The majority of people across the globe rely on telephony networks as their primary means of communication. As such, many of the most sensitive personal, corporate and government related communications pass through these systems every day. Unsurprisingly, such connections are subject to a wide range of attacks. Of increasing concern is the use of metadata contained in Call Detail Records (CDRs), which contain source, destination, start time and duration of a call. This information is potentially dangerous as the very act of two parties communicating can reveal significant details about their relationship and put them in the focus of targeted observation or surveillance, which is highly critical especially for journalists and activists. To address this problem, we develop the Phonion architecture to frustrate such attacks by separating call setup functions from call delivery. Specifically, Phonion allows users to preemptively establish call circuits across multiple providers and technologies before dialing into the circuit and does not require constant Internet connectivity. Since no single carrier can determine the ultimate destination of the call, it provides unlinkability for its users and helps them to avoid passive surveillance. We define and discuss a range of adversary classes and analyze why current obfuscation technologies fail to protect users against such metadata attacks. In our extensive evaluation we further analyze advanced anonymity technologies (e.g., VoIP over Tor), which do not preserve our functional requirements for high voice quality in the absence of constant broadband Internet connectivity and compatibility with landline and feature phones. Phonion is the first practical system to provide guarantees of unlinkable communication against a range of practical adversaries in telephony systems.

Mobile money, also known as branchless banking, leverages ubiquitous cellular networks to bring much-needed financial services to the unbanked in the developing world. These services are often deployed as smartphone apps, and although marketed as secure, these applications are often not regulated as strictly as traditional banks, leaving doubt about the truth of such claims. In this article, we evaluate these claims and perform the first in-depth measurement analysis of branchless banking applications. We first perform an automated analysis of all 46 known Android mobile money apps across the 246 known mobile money providers from 2015. We then perform a comprehensive manual teardown of the registration, login, and transaction procedures of a diverse 15% of these apps. We uncover pervasive vulnerabilities spanning botched certification validation, do-it-yourself cryptography, and other forms of information leakage that allow an attacker to impersonate legitimate users, modify transactions, and steal financial records. These findings show that the majority of these apps fail to provide the protections needed by financial services. In an expanded re-evaluation one year later, we find that these systems have only marginally improved their security. Additionally, we document our experiences working in this sector for future researchers and provide recommendations to improve the security of this critical ecosystem. Finally, through inspection of providers’ terms of service, we also discover that liability for these problems unfairly rests on the shoulders of the customer, threatening to erode trust in branchless banking and hinder efforts for global financial inclusion.

Text messages sent via the Short Message Service (SMS) have revolutionized interpersonal communication. Recent years have also seen this service become a critical component of the security infrastructure, assisting with tasks including identity verification and second-factor authentication. At the same time, this messaging infrastructure has become dramatically more open and connected to public networks than ever before. However, the implications of this openness, the security practices of benign services, and the malicious misuse of this ecosystem are not well understood. In this paper, we provide the first longitudinal study to answer these questions, analyzing nearly 400,000 text messages sent to public online SMS gateways over the course of 14 months. From this data, we are able to identify not only a range of services sending extremely sensitive plaintext data and implementing low entropy solutions for one-use codes, but also offer insights into the prevalence of SMS spam and behaviors indicating that public gateways are primarily used for evading account creation policies that require verified phone numbers. This latter finding has significant implications for research combatting phone-verified account fraud and demonstrates that such evasion will continue to be difficult to detect and prevent.

Text messaging is used by more people around the world than any other communications technology. As such, it presents a desirable medium for spammers. While this problem has been studied by many researchers over the years, the recent increase in legitimate bulk traffic (e.g., account verification, 2FA, etc.) has dramatically changed the mix of traffic seen in this space, reducing the effectiveness of previous spam classification efforts. This paper demonstrates the performance degradation of those detectors when used on a large-scale corpus of text messages containing both bulk and spam messages. Against our labeled dataset of text messages collected over 14 months, the precision and recall of past classifiers fall to 23.8% and 61.3% respectively. However, using our classification techniques and labeled clusters, precision and recall rise to 100% and 96.8%. We not only show that our collected dataset helps to correct many of the overtraining errors seen in previous studies, but also present insights into a number of current SMS spam campaigns.

Use-after-free conditions occur when an execution path of a process accesses an incorrectly deal located object. Such access is problematic because it may potentially allow for the execution of arbitrary code by an adversary. However, while increasingly common, such flaws are rarely detected by compilers in even the most obvious instances. In this paper, we design and implement a static analysis method for the detection of use-after-free conditions in binary code. Our new analysis is similar to available expression analysis and traverses all code paths to ensure that every object is defined before each use. Failure to achieve this property indicates that an object is improperly freed and potentially vulnerable to compromise. After discussing the details of our algorithm, we implement a tool and run it against a set of enterprise-grade, publicly available binaries. We show that our tool can not only catch textbook and recently released in-situ examples of this flaw, but that it has also identified 127 additional use-after-free conditions in a search of 652 compiled binaries in the Windows system32 directory. In so doing, we demonstrate not only the power of this approach in combating this increasingly common vulnerability, but also the ability to identify such problems in software for which the source code is not necessarily publicly available.

Malware is a pressing concern for mobile application market operators. While current mitigation techniques are keeping pace with the relatively infrequent presence of malicious code, the rapidly increasing rate of application development makes manual and resource-intensive automated analysis costly at market-scale. To address this resource imbalance, we present the Mobile Application Security Triage (MAST) architecture, a tool that helps to direct scarce malware analysis resources towards the applications with the greatest potential to exhibit malicious behavior. MAST analyzes attributes extracted from just the application package using Multiple Correspondence Analysis (MCA), a statistical method that measures the correlation between multiple categorical (i.e., qualitative) data. We train MAST using over 15,000 applications from Google Play and a dataset of 732 known-malicious applications. We then use MAST to perform triage on three third-party markets of different size and malware composition---36,710 applications in total. Our experiments show that MAST is both effective and performant. Using MAST ordered ranking, malware-analysis tools can find 95% of malware at the cost of analyzing 13% of the non-malicious applications on average across multiple markets, and MAST triage processes markets in less than a quarter of the time required to perform signature detection. More importantly, we show that successful triage can dramatically reduce the costs of removing malicious applications from markets.