Both cloud providers and users wish to manage, monitor, and secure virtualized guest systems. This is typically accomplished with custom agent programs that run inside a guest or complex virtual machine introspection (VMI) systems that operate outside a guest. Agents are limited by the need to install and maintain them in each guest, while VMI systems are limited by the need to understand guest kernel internals. We introduce Hypervisor Dissociative Execution, or HyDE, a new approach that operates between these extremes to avoid their limitations and provide a robust and flexible mechanism to examine and modify a guest from the outside. In the HyDE model, developers assemble programs that mix out-of-guest logic with in-guest system calls. These programs are launched from outside a guest where they are able to co-opt the execution of guest processes. We present an open-source prototype HyDE implementation paired with 10 HyDE programs that address a wide range of user needs from password resets and guest process enumeration to dynamically generating a software bill of materials. We evaluate the utility, robustness, and performance of HyDE by executing the example programs while concurrently running standard benchmarks within multiple guest systems. Our results show that HyDE maintains system stability and incurs negligible overhead for one-off analyses or modifications. In persistent operation, HyDE incurs overhead as low as 7% in a multi-node cloud application benchmark.

Inter-process isolation has been deployed in operating systems for decades, but secure intra-process isolation remains an active research topic. Achieving secure intra-process isolation within an operating system process is notoriously difficult. However, viable solutions that securely consolidate workloads into the same process have the potential to be extremely valuable.

Fuzz testing is often automated, but also frequently augmented by experts who insert themselves into the workflow in a greedy search for bugs. In this paper, we propose Homo in Machina, or HM-fuzzing, in which analyses guide the manual efforts, maximizing benefit. As one example of this paradigm, we introduce compartment analysis. Compartment analysis uses a whole-program dominator analysis to estimate the utility of reaching new code, and combines this with a dynamic analysis indicating drastically under-covered edges guarding that code. This results in a prioritized list of compartments, i.e., large, uncovered parts of the pro-gram semantically partitioned and largely unreachable given the current corpus of inputs under consideration. A human can use this categorization and ranking of compartments directly to focus manual effort, finding or fashioning inputs that make the compartments available for future fuzzing. We evaluate the effect of compartment analysis on seven projects within the OSS-Fuzz corpus where we see coverage improvements over AFL++ as high as 94%, with a median of 13%. We further observe that the determination of compartments is highly stable and thus can be done early in a fuzzing campaign, maximizing the potential for impact.

Binary analyses based on deep neural networks (DNNs), or neural binary analyses (NBAs), have become a hotly researched topic in recent years. DNNs have been wildly successful at pushing the performance and accuracy envelopes in the natural language and image processing domains. Thus, DNNs are highly promising for solving binary analysis problems that are hard due to a lack of complete information resulting from the lossy compilation process. Despite this promise, it is unclear that the prevailing strategy of repurposing embeddings and model architectures originally developed for other problem domains is sound given the adversarial contexts under which binary analysis often operates.

Fuzz testing repeatedly assails software with random inputs in order to trigger unexpected program behaviors, such as crashes or timeouts, and has historically revealed serious security vulnerabilities. In this article, we present HotFuzz, a framework for automatically discovering Algorithmic Complexity (AC) time and space vulnerabilities in Java libraries. HotFuzz uses micro-fuzzing, a genetic algorithm that evolves arbitrary Java objects in order to trigger the worst-case performance for a method under test. We define Small Recursive Instantiation (SRI) as a technique to derive seed inputs represented as Java objects to micro-fuzzing. After micro-fuzzing, HotFuzz synthesizes test cases that triggered AC vulnerabilities into Java programs and monitors their execution in order to reproduce vulnerabilities outside the fuzzing framework. HotFuzz outputs those programs that exhibit high resource utilization as witnesses for AC vulnerabilities in a Java library. We evaluate HotFuzz over the Java Runtime Environment (JRE), the 100 most popular Java libraries on Maven, and challenges contained in the DARPA Space and Time Analysis for Cybersecurity (STAC) program. We evaluate SRI’s effectiveness by comparing the performance of micro-fuzzing with SRI, measured by the number of AC vulnerabilities detected, to simply using empty values as seed inputs. In this evaluation, we verified known AC vulnerabilities, discovered previously unknown AC vulnerabilities that we responsibly reported to vendors, and received confirmation from both IBM and Oracle. Our results demonstrate that micro-fuzzing finds AC vulnerabilities in real-world software, and that micro-fuzzing with SRI-derived seed inputs outperforms using empty values in both the temporal and spatial domains.

Fuzz testing has been used to find bugs in programs since the 1990s, but despite decades of dedicated research, there is still no consensus on which fuzzing techniques work best. One reason for this is the paucity of ground truth: bugs in real programs with known root causes and triggering inputs are difficult to collect at a meaningful scale. Bug injection technologies that add synthetic bugs into real programs seem to offer a solution, but the differences in finding these synthetic bugs versus organic bugs have not previously been explored at a large scale. Using over 80 years of CPU time, we ran eight fuzzers across 20 targets from the Rode0day bug-finding competition and the LAVA-M corpus. Experiments were standardized with respect to compute resources and metrics gathered. These experiments show differences in fuzzer performance as well as the impact of various configuration options. For instance, it is clear that integrating symbolic execution with mutational fuzzing is very effective and that using dictionaries improves performance. Other conclusions are less clear-cut; for example, no one fuzzer beat all others on all tests. It is noteworthy that no fuzzer found any organic bugs (i.e., one reported in a CVE), despite 50 such bugs being available for discovery in the fuzzing corpus. A close analysis of results revealed a possible explanation: a dramatic difference between where synthetic and organic bugs live with respect to the "main path" discovered by fuzzers. We find that recent updates to bug injection systems have made synthetic bugs more difficult to discover, but they are still significantly easier to find than organic bugs in our target programs. Finally, this study identifies flaws in bug injection techniques and suggests a number of axes along which synthetic bugs should be improved.

Closely monitoring the behavior of a software system during its execution enables developers and analysts to observe, and ultimately understand, how it works. This kind of dynamic analysis can be instrumental to reverse engineering, vulnerability discovery, exploit development, and debugging. While these analyses are typically well-supported for homogeneous desktop platforms (e.g., x86 desktop PCs), they can rarely be applied in the heterogeneous world of embedded systems. One approach to enable dynamic analyses of embedded systems is to move software stacks from physical systems into virtual environments that sufficiently model hardware behavior. This process which we call "rehosting" poses a significant research challenge with major implications for security analyses. Although rehosting has traditionally been an unscientific and ad-hoc endeavor undertaken by domain experts with varying time and resources at their disposal, researchers are beginning to address rehosting challenges systematically and in earnest. In this paper, we establish that emulation is insufficient to conduct large-scale dynamic analysis of real-world hardware systems and present rehosting as a firmware-centric alternative. Furthermore, we taxonomize preliminary rehosting efforts, identify the fundamental components of the rehosting process, and propose directions for future research.

The prevalence of memory corruption bugs in the past decades resulted in numerous defenses, such as stack canaries, control flow integrity (CFI), and memory-safe languages. These defenses can prevent entire classes of vulnerabilities, and help increase the security posture of a program. In this paper, we show that memory corruption defenses can be bypassed using speculative execution attacks. We study the cases of stack protectors, CFI, and bounds checks in Go, demonstrating under which conditions they can be bypassed by a form of speculative control flow hijack, relying on speculative or architectural overwrites of control flow data. Information is leaked by redirecting the speculative control flow of the victim to a gadget accessing secret data and acting as a side channel send. We also demonstrate, for the first time, that this can be achieved by stitching together multiple gadgets, in a speculative return-oriented programming attack. We discuss and implement software mitigations, showing moderate performance impact.

Speculative execution attacks exploit vulnerabilities at a CPU's microarchitectural level, which, until recently, remained hidden below the instruction set architecture, largely undocumented by CPU vendors. New speculative execution attacks are released on a monthly basis, showing how aspects of the so-far unexplored microarchitectural attack surface can be exploited. In this paper, we introduce, Speculator, a new tool to investigate these new microarchitectural attacks and their mitigations, which aims to be the GDB of speculative execution. Using speculative execution markers, set of instructions that we found are observable through performance counters during CPU speculation, Speculator can study microarchitectural behavior of single snippets of code, or more complex attacker and victim scenarios (e.g. Branch Target Injection (BTI) attacks). We also present our findings on multiple CPU platforms showing the precision and the flexibility offered by Speculator and its templates.

Programmatic advertising provides digital ad buyers with the convenience of purchasing ad impressions through Real Time Bidding (RTB) auctions. However, programmatic advertising has also given rise to a novel form of ad fraud known as domain spoofing, in which attackers sell counterfeit impressions that claim to be from high-value publishers. To mitigate domain spoofing, the Interactive Advertising Bureau (IAB) Tech Lab introduced the ads.txt standard in May 2017 to help ad buyers verify authorized digital ad sellers, as well as to promote overall transparency in programmatic advertising.

Control flow integrity (CFI) has received significant attention in the community to combat control hijacking attacks in the presence of memory corruption vulnerabilities. The challenges in creating a practical CFI has resulted in the development of a new type of CFI based on runtime type checking (RTC). RTC-based CFI has been implemented in a number of recent practical efforts such as GRSecurity Reuse Attack Protector (RAP) and LLVM-CFI. While there has been a number of previous efforts that studied the strengths and limitations of other types of CFI techniques, little has been done to evaluate the RTC-based CFI. In this work, we study the effectiveness of RTC from the security and practicality aspects. From the security perspective, we observe that type collisions are abundant in sufficiently large code bases but exploiting them to build a functional attack is not straightforward. Then we show how an attacker can successfully bypass RTC techniques using a variant of ROP attacks that respect type checking (called TROP) and also built two proof-of-concept exploits, one against Nginx web server and the other against Exim mail server. We also discuss practical challenges of implementing RTC. Our findings suggest that while RTC is more practical for applying CFI to large code bases, its policy is not strong enough when facing a motivated attacker.

Secure deletion of data from non-volatile storage is a well-recognized problem. While numerous solutions have been proposed, advances in storage technologies have stymied efforts to solve the problem. For instance, SSDs make use of techniques such as wear leveling that involve replication of data; this is in direct opposition to efforts to securely delete sensitive data from storage. We present a technique to provide secure deletion guarantees at file granularity, independent of the characteristics of the underlying storage medium. The approach builds on prior seminal work on cryptographic erasure, encrypting every file on an insecure medium with a unique key that can later be discarded to cryptographically render the data irrecoverable. To make the approach scalable and, therefore, usable on commodity systems, keys are organized in an efficient tree structure where a single master key is confined to a secure store. We describe an implementation of this scheme as a file-aware stackable block device, deployed as a standalone Linux kernel module that does not require modifications to the operating system. Our prototype demonstrates that secure deletion independent of the underlying storage medium can be achieved with comparable overhead to existing full disk encryption implementations.

Online surveys are a popular mechanism for performing market research in exchange for monetary compensation. Unfortunately, fraudulent survey websites are similarly rising in popularity among cyber-criminals as a means for executing social engineering attacks. In addition to the sizable population of users that participate in online surveys as a secondary revenue stream, unsuspecting users who search the web for free content or access codes to commercial software can also be exposed to survey scams. This occurs through redirection to websites that ask the user to complete a survey in order to receive the promised content or a reward. In this paper, we present SURVEYLANCE, the first system that automatically identifies survey scams using machine learning techniques. Our evaluation demonstrates that SURVEYLANCE works well in practice by identifying 8,623 unique websites involved in online survey attacks. We show that SURVEYLANCE is suitable for assisting human analysts in survey scam detection at scale. Our work also provides the first systematic analysis of the survey scam ecosystem by investigating the capabilities of these services, mapping all the parties involved in the ecosystem, and quantifying the consequences to users that are exposed to these services. Our analysis reveals that a large number of survey scams are easily reachable through the Alexa top 30K websites, and expose users to a wide range of security issues including identity fraud, deceptive advertisements, potentially unwanted programs (PUPs), malicious extensions, and malware.

Relative Path Overwrite (RPO) is a recent technique to inject style directives into sites even when no style sink or markup injection vulnerability is present. It exploits differences in how browsers and web servers interpret relative paths (i.e., path confusion) to make a HTML page reference itself as a stylesheet; a simple text injection vulnerability along with browsers» leniency in parsing CSS resources results in an attacker»s ability to inject style directives that will be interpreted by the browser. Even though style injection may appear less serious a threat than script injection, it has been shown that it enables a range of attacks, including secret exfiltration. In this paper, we present the first large-scale study of the Web to measure the prevalence and significance of style injection using RPO. Our work shows that around 9% of the sites in the Alexa Top 10,000 contain at least one vulnerable page, out of which more than one third can be exploited. We analyze in detail various impediments to successful exploitation, and make recommendations for remediation. In contrast to script injection, relatively simple countermeasures exist to mitigate style injection. However, there appears to be little awareness of this attack vector as evidenced by a range of popular Content Management Systems (CMSes) that we found to be exploitable.

Web browsers have become the predominant means for developing and deploying applications, and thus they often handle sensitive data such as social interactions or financial credentials and information. As a consequence, defensive measures such as TLS, the Same-Origin Policy (SOP), and Content Security Policy (CSP) are critical for ensuring that sensitive data remains in trusted hands.

Modern applications are often split into separate client and server tiers that communicate via message passing over the network. One well-understood threat to privacy for such applications is the leakage of sensitive user information either in transit or at the server. In response, an array of defensive techniques have been developed to identify or block unintended or malicious information leakage. However, prior work has primarily considered privacy leaks originating at the client directed at the server, while leakage in the reverse direction -- from the server to the client -- is comparatively under-studied. The question of whether and to what degree this leakage constitutes a threat remains an open question. We answer this question in the affirmative with Hush, a technique for semi-automatically identifying Server-based InFormation OvershariNg (SIFON) vulnerabilities in multi-tier applications. In particular, the technique detects SIFON vulnerabilities using a heuristic that overshared sensitive information from server-side APIs will not be displayed by the application's user interface. The technique first performs a scalable static program analysis to screen applications for potential vulnerabilities, and then attempts to confirm these candidates as true vulnerabilities with a partially-automated dynamic analysis. Our evaluation over a large corpus of Android applications demonstrates the effectiveness of the technique by discovering several previously-unknown SIFON vulnerabilities in eight applications.

Spearphishing is a prominent targeted attack vector in today's Internet. By impersonating trusted email senders through carefully crafted messages and spoofed metadata, adversaries can trick victims into launching attachments containing malicious code or into clicking on malicious links that grant attackers a foothold into otherwise well-protected networks. Spearphishing is effective because it is fundamentally difficult for users to distinguish legitimate emails from spearphishing emails without additional defensive mechanisms. However, such mechanisms, such as cryptographic signatures, have found limited use in practice due to their perceived difficulty of use for normal users. In this paper, we present a novel automated approach to defending users against spearphishing attacks. The approach first builds probabilistic models of both email metadata and stylometric features of email content. Then, subsequent emails are compared to these models to detect characteristic indicators of spearphishing attacks. Several instantiations of this approach are possible, including performing model learning and evaluation solely on the receiving side, or senders publishing models that can be checked remotely by the receiver. Our evaluation of a real data set drawn from 20 email users demonstrates that the approach effectively discriminates spearphishing attacks from legitimate email while providing significant ease-of-use benefits over traditional defenses.

The prevailing security model for OSes focuses on isolating users from each other, however, the changing computing landscape has led to the extension of traditional access control models for single-user devices. Modern OSes for mobile devices such as iOS and Android have taken the opportunity provided by these new platforms to introduce permission systems in which users can manage access to sensitive resources during application installation or runtime. One drawback of similar efforts on desktop environments is that applications must be rewritten with this security model in mind, which hinders traditional OSes from enjoying the benefits of user-driven access control. We present a novel architecture for retrofitting a dynamic, input-driven access control model into traditional OSes. In this model, access to privacy-sensitive resources is mediated based on the temporal proximity of user interactions to access requests, and requests are communicated back to the user via visual alerts. We present a prototype implementation and demonstrate how input-driven access control can be realized for resources such as the microphone, camera, clipboard, and screen contents. Our approach is transparent to applications and users, and incurs no discernible performance overhead.

Work on automating vulnerability discovery has long been hampered by a shortage of ground-truth corpora with which to evaluate tools and techniques. This lack of ground truth prevents authors and users of tools alike from being able to measure such fundamental quantities as miss and false alarm rates. In this paper, we present LAVA, a novel dynamic taint analysis-based technique for producing ground-truth corpora by quickly and automatically injecting large numbers of realistic bugs into program source code. Every LAVA bug is accompanied by an input that triggers it whereas normal inputs are extremely unlikely to do so. These vulnerabilities are synthetic but, we argue, still realistic, in the sense that they are embedded deep within programs and are triggered by real inputs. Using LAVA, we have injected thousands of bugs into eight real-world programs, including bash, tshark, and the GNU coreutils. In a preliminary evaluation, we found that a prominent fuzzer and a symbolic execution-based bug finder were able to locate some but not all LAVA-injected bugs, and that interesting patterns and pathologies were already apparent in their performance. Our work forms the basis of an approach for generating large ground-truth vulnerability corpora on demand, enabling rigorous tool evaluation and providing a high-quality target for tool developers.

Android is the most popular mobile platform today, and it is also the mobile operating system that is most heavily targeted by malware. Existing static analyses are effective in detecting the presence of most malicious code and unwanted information flows. However, certain types of malice are very difficult to capture explicitly by modeling permission sets, suspicious API calls, or unwanted information flows. One important type of such malice is malicious application logic, where a program (often subtly) modifies its outputs or performs actions that violate the expectations of the user. Malicious application logic is very hard to identify without a specification of the "normal," expected functionality of the application. We refer to malicious application logic that is executed, or triggered, only under certain (often narrow) circumstances as a logic bomb. This is a powerful mechanism that is commonly employed by targeted malware, often used as part of APTs and state-sponsored attacks: in fact, in this scenario, the malware is designed to target specific victims and to only activate under certain circumstances. In this paper, we make a first step towards detecting logic bombs. In particular, we propose trigger analysis, a new static analysis technique that seeks to automatically identify triggers in Android applications. Our analysis combines symbolic execution, path predicate reconstruction and minimization, and inter-procedural control-dependency analysis to enable the precise detection and characterization of triggers, and it overcomes several limitations of existing approaches. We implemented a prototype of our analysis, called TriggerScope, and we evaluated it over a large corpus of 9,582 benign apps from the Google Play Store and a set of trigger-based malware, including the recently-discovered HackingTeam's RCSAndroid advanced malware. Our system is capable of automatically identify several interesting time-, location-, and SMS-related triggers, is affected by a low false positive rate (0.38%), and it achieves 100% detection rate on the malware set. We also show how existing approaches, specifically when tasked to detect logic bombs, are affected by either a very high false positive rate or false negative rate. Finally, we discuss the logic bombs identified by our analysis, including two previously-unknown backdoors in benign apps.

The ubiquity of Internet advertising has made it a popular target for attackers. One well-known instance of these attacks is the widespread use of trick banners that use social engineering techniques to lure victims into clicking on deceptive fake links, potentially leading to a malicious domain or malware. A recent and pervasive trend by attackers is to imitate the "download" or "play" buttons in popular file sharing sites (e.g., one-click hosters, video-streaming sites, bittorrent sites) in an attempt to trick users into clicking on these fake banners instead of the genuine link.

Since its introduction, Android's in-app billing service has quickly gained popularity. The in-app billing service allows users to pay for options, services, subscriptions, and virtual goods from within mobile apps themselves. In-app billing is attractive for developers because it is easy to integrate, and has the advantage that the developer does not need to be concerned with managing financial transactions. In this paper, we present the first fully-automated attack against the in-app billing service on Android. Using our prototype, we conducted a robustness study against our attack, analyzing 85 of the most popular Android apps that make use of in-app billing. We found that 60% of these apps were easily and automatically crackable. We were able to bypass highly popular and prominent games such as Angry Birds and Temple Run, each of which have millions of users. Based on our study, we developed a defensive technique that specifically counters automated attacks against in-app billing. Our technique is lightweight and can be easily added to existing applications.

QR codes, a form of 2D barcode, allow easy interaction between mobile devices and websites or printed material by removing the burden of manually typing a URL or contact information. QR codes are increasingly popular and are likely to be adopted by malware authors and cyber-criminals as well. In fact, while a link can "look" suspicious, malicious and benign QR codes cannot be distinguished by simply looking at them. However, despite public discussions about increasing use of QR codes for malicious purposes, the prevalence of malicious QR codes and the kinds of threats they pose are still unclear. In this paper, we examine attacks on the Internet that rely on QR codes. Using a crawler, we performed a large-scale experiment by analyzing QR codes across 14 million unique web pages over a ten-month period. Our results show that QR code technology is already used by attackers, for example to distribute malware or to lead users to phishing sites. However, the relatively few malicious QR codes we found in our experiments suggest that, on a global scale, the frequency of these attacks is not alarmingly high and users are rarely exposed to the threats distributed via QR codes while surfing the web.

Graphical user interfaces (GUIs) are the predominant means by which users interact with modern programs. GUIs contain a number of common visual elements or widgets such as labels, text fields, buttons, and lists, and GUIs typically provide the ability to set attributes on these widgets to control their visibility, enabled status, and whether they are writable. While these attributes are extremely useful to provide visual cues to users to guide them through an application's GUI, they can also be misused for purposes they were not intended. In particular, in the context of GUI-based applications that include multiple privilege levels within the application, GUI element attributes are often misused as a mechanism for enforcing access control policies. In this work, we introduce GEMs, or instances of GUI element misuse, as a novel class of access control vulnerabilities in GUI-based applications. We present a classification of different GEMs that can arise through misuse of widget attributes, and describe a general algorithm for identifying and confirming the presence of GEMs in vulnerable applications. We then present GEM Miner, an implementation of our GEM analysis for the Windows platform. We evaluate GEM Miner over a test set of three complex, real-world GUI-based applications targeted at the small business and enterprise markets, and demonstrate the efficacy of our analysis by finding numerous previously unknown access control vulnerabilities in these applications. We have reported the vulnerabilities we discovered to the developers of each application, and in one case have received confirmation of the issue.

Android is currently the largest mobile platform with around 750 million devices worldwide. Unfortunately, more than 30% of all devices contain publicly known security vulnerabilities and, in practice, cannot be updated through normal mechanisms since they are not longer supported by the manufacturer and mobile operator. This failure of traditional patch distribution systems has resulted in the creation of a large population of vulnerable mobile devices.

As more and more Internet-based attacks arise, organizations are responding by deploying an assortment of security products that generate situational intelligence in the form of logs. These logs often contain high volumes of interesting and useful information about activities in the network, and are among the first data sources that information security specialists consult when they suspect that an attack has taken place. However, security products often come from a patchwork of vendors, and are inconsistently installed and administered. They generate logs whose formats differ widely and that are often incomplete, mutually contradictory, and very large in volume. Hence, although this collected information is useful, it is often dirty.

Privacy has become an issue of paramount importance for many users. As a result, encryption tools such as True Crypt, OS-based full-disk encryption such as File Vault, and privacy modes in all modern browsers have become popular. However, although such tools are useful, they are not perfect. For example, prior work has shown that browsers still leave many traces of user information on disk even if they are started in private browsing mode. In addition, disk encryption alone is not sufficient, as key disclosure through coercion remains possible. Clearly, it would be useful and highly desirable to have OS-level support that provides strong privacy guarantees for any application -- not only browsers. In this paper, we present the design and implementation of PrivExec, the first operating system service for private execution. PrivExec provides strong, general guarantees of private execution, allowing any application to execute in a mode where storage writes, either to the filesystem or to swap, will not be recoverable by others during or after execution. PrivExec does not require explicit application support, recompilation, or any other preconditions. We have implemented a prototype of PrivExec by extending the Linux kernel that is performant, practical, and that secures sensitive data against disclosure.

Botnets continue to be a significant problem on the Internet. Accordingly, a great deal of research has focused on methods for detecting and mitigating the effects of botnets. Two of the primary factors preventing the development of effective large-scale, wide-area botnet detection systems are seemingly contradictory. On the one hand, technical and administrative restrictions result in a general unavailability of raw network data that would facilitate botnet detection on a large scale. On the other hand, were this data available, real-time processing at that scale would be a formidable challenge. In contrast to raw network data, NetFlow data is widely available. However, NetFlow data imposes several challenges for performing accurate botnet detection.

Hard disk encryption is known to be vulnerable to a number of attacks that aim to directly extract cryptographic key material from system memory. Several approaches to preventing this class of attacks have been proposed, including Tresor [18] and LoopAmnesia [25]. The common goal of these systems is to confine the encryption key and encryption process itself to the CPU, such that sensitive key material is never released into system memory where it could be accessed by a DMA attack.

Web applications have become an integral part of the daily lives of millions of users. Unfortunately, web applications are also frequently targeted by attackers, and critical vulnerabilities such as cross-site scripting and SQL injection are still common. As a consequence, much effort in the past decade has been spent on mitigating web application vulnerabilities. Current techniques focus mainly on sanitization: either on automated sanitization, the detection of missing sanitizers, the correctness of sanitizers, or the correct placement of sanitizers. However, these techniques are either not able to prevent new forms of input validation vulnerabilities such as HTTP Parameter Pollution, come with large runtime overhead, lack precision, or require significant modifications to the client and/or server infrastructure. In this paper, we present IPAAS, a novel technique for preventing the exploitation of cross-site scripting and SQL injection vulnerabilities based on automated data type detection of input parameters. IPAAS automatically and transparently augments otherwise insecure web application development environments with input validators that result in significant and tangible security improvements for real systems. We implemented IPAAS for PHP and evaluated it on five real-world web applications with known cross-site scripting and SQL injection vulnerabilities. Our evaluation demonstrates that IPAAS would have prevented 83% of SQL injection vulnerabilities and 65% of cross-site scripting vulnerabilities while incurring no developer burden.

Voting is the process through which a democratic society determines its government. Therefore, voting systems are as important as other well-known critical systems, such as air traffic control systems or nuclear plant monitors. Unfortunately, voting systems have a history of failures that seems to indicate that their quality is not up to the task. Because of the alarming frequency and impact of the malfunctions of voting systems, in recent years a number of vulnerability analysis exercises have been carried out against voting systems to determine if they can be compromised in order to control the results of an election. We have participated in two such large-scale projects, sponsored by the Secretaries of State of California and Ohio, whose goals were to perform the security testing of the electronic voting systems used in their respective states. As the result of the testing process, we identified major vulnerabilities in all of the systems analyzed. We then took advantage of a combination of these vulnerabilities to generate a series of attacks that would spread across the voting systems and would “steal” votes by combining voting record tampering with social engineering approaches. As a response to the two large-scale security evaluations, the Secretaries of State of California and Ohio recommended changes to improve the security of the voting process. In this paper, we describe the methodology that we used in testing the two real-world electronic voting systems we evaluated, the findings of our analysis, our attacks, and the lessons we learned.

Web-based applications have become a popular means of exposing functionality to large numbers of users by leveraging the services provided by web servers and databases. The wide proliferation of custom-developed web-based applications suggests that a

Electronic voting systems play a critical role in today's democratic societies, as they are responsible for recording and counting the citizens' votes. Unfortunately, there is an alarming number of reports describing the malfunctioning of these systems, suggesting that their quality is not up to the task. Recently, there has been a focus on the security testing of voting systems to determine if they can be compromised in order to control the results of an election. We have participated in two large-scale projects, sponsored by the Secretaries of State of California and Ohio, whose respective goals were to perform the security testing of the electronic voting systems used in those two states. The testing process identified major flaws in all the systems analyzed, and resulted in substantial changes in the voting procedures of both states. In this paper, we describe the testing methodology that we used in testing two real-world electronic voting systems, the findings of our analysis, and the lessons we learned.

The effectiveness and precision of network-based intrusion detection signatures can be evaluated either by direct analysis of the signatures (if they are available) or by using black-box testing (if the system is closed-source). Recently, several techniques have been proposed to generate test cases by automatically deriving variations (or mutations) of attacks. Even though these techniques have been useful in identifying "blindspots" in the signatures of closed-source, network-based intrusion detection systems, the generation of test cases is performed in a random, un- guided fashion. The reason is that there is no information available about the signatures to be tested. As a result, identifying a test case that is able to evade detection is difficult. In this paper, we propose a novel approach to drive the generation of test cases by using the information gathered by analyzing the dynamic behavior of the intrusion detection system. Our approach applies dynamic dataflow analysis techniques to the intrusion detection system to identify which parts of a network stream are used to detect an attack and how these parts are matched by a signature. The result of our analysis is a set of constraints that is used to guide the black-box testing process, so that the mutations are applied to only those parts of the attack that are relevant for detection. By doing this, we are able to perform a more focused generation of the test cases and improve the process of identifying an attack variation that evades detection.

A rootkit is a collection of tools used by intruders to keep the legitimate users and administrators of a compromised machine unaware of their presence. Originally, root-kits mainly included modified versions of system auditing programs (e.g., ps or netstat on a Unix system). However, for operating systems that support loadable kernel modules (e.g., Linux and Solaris), a new type of rootkit has recently emerged. These rootkits are implemented as kernel modules, and they do not require modification of user-space binaries to conceal malicious activity. Instead, these rootkits operate within the kernel, modifying critical data structures such as the system call table or the list of currently-loaded kernel modules. This paper presents a technique that exploits binary analysis to ascertain, at load time, if a module's behavior resembles the behavior of a rootkit. Through this method, it is possible to provide additional protection against this type of malicious modification of the kernel. Our technique relies on an abstract model of module behavior that is not affected by small changes in the binary image of the module. Therefore, the technique is resistant to attempts to conceal the malicious nature of a kernel module.

Misuse-based intrusion detection systems rely on models of attacks to identify the manifestation of intrusive behavior. Therefore, the ability of these systems to reliably detect attacks is strongly affected by the quality of their models, which are often called "signatures." A perfect model would be able to detect all the instances of an attack without making mistakes, that is, it would produce a 100% detection rate with 0 false alarms. Unfortunately, writing good models (or good signatures) is hard. Attacks that exploit a specific vulnerability may do so in completely different ways, and writing models that take into account all possible variations is very difficult. For this reason, it would be beneficial to have testing tools that are able to evaluate the "goodness" of detection signatures. This work describes a technique to test and evaluate misuse detection models in the case of network-based intrusion detection systems. The testing technique is based on a mechanism that generates a large number of variations of an exploit by applying mutant operators to an exploit template. These mutant exploits are then run against a victim host protected by a network-based intrusion detection system. The results of the systems in detecting these variations provide a quantitative basis for the evaluation of the quality of the corresponding detection model.

Intrusion detection systems (IDSs) attempt to identify attacks by comparing collected data to predefined signatures known to be malicious (misuse-based IDSs) or to a model of legal behavior (anomaly-based IDSs). Anomaly-based approaches have the advantage of being able to detect previously unknown attacks, but they suffer from the difficulty of building robust models of acceptable behavior, which may result in a large number of false alarms. Almost all current anomaly-based intrusion detection systems classify an input event as normal or anomalous by analyzing its features, utilizing a number of different models. A decision for an input event is made by aggregating the results of all employed models. We have identified two reasons for the large number of false alarms, caused by incorrect classification of events in current systems. One is the simplistic aggregation of model outputs in the decision phase. Often, only the sum of the model results is calculated and compared to a threshold. The other reason is the lack of integration of additional information into the decision process. This additional information can be related to the models, such as the confidence in a model's output, or can be extracted from external sources. To mitigate these shortcomings, we propose an event classification scheme that is based on Bayesian networks. Bayesian networks improve the aggregation of different model outputs and allow one to seamlessly incorporate additional information. Experimental results show that the accuracy of the event classification process is significantly improved using our proposed approach.

Web servers are ubiquitous, remotely accessible, and often misconfigured. In addition, custom Web-based applications may introduce vulnerabilities that are overlooked even by the most security-conscious server administrators. Consequently, Web servers are a popular target for hackers. To mitigate the security exposure associated with Web servers, intrusion detection systems are deployed to analyze and screen incoming requests. The goal is to perform early detection of malicious activity and possibly prevent more serious damage to the protected site. Even though intrusion detection is critical for the security of Web servers, the intrusion detection systems available today only perform very simple analyses and are often vulnerable to simple evasion techniques. In addition, most systems do not provide sophisticated attack languages that allow a system administrator to specify custom, complex attack scenarios to be detected. We present WebSTAT, an intrusion detection system that analyzes Web requests looking for evidence of malicious behavior. The system is novel in several ways. First of all, it provides a sophisticated language to describe multistep attacks in terms of states and transitions. In addition, the modular nature of the system supports the integrated analysis of network traffic sent to the server host, operating system-level audit data produced by the server host, and the access logs produced by the Web server. By correlating different streams of events, it is possible to achieve more effective detection of Web-based attacks.