Clickbait PDFs, an entry point for multiple Web attacks, are distributed via SEO poisoning and rank high in search results due to being massively uploaded on abused or compromised websites. The central role of these hosts in the distribution of clickbait PDFs remains understudied, and it is unclear whether attackers differentiate the types of hosting for PDF uploads, how long they rely on hosts, and how affected parties respond to abuse. To address this, we conducted real-time analyses on hosts, collecting data on 4,648,939 clickbait PDFs served by 177,835 hosts over 17 months. Our results revealed a diverse infrastructure, with hosts falling into three main hosting types. We also identified at scale the presence of eight software components which facilitate file uploads and which are likely exploited for clickbait PDF distribution. We contact affected parties to report the misuse of their resources via a large-scale vulnerability notification. While we observed some effectiveness in terms of number of cleaned-up PDFs following the notification, long-term improvement in this infrastructure remained insignificant. This finding raises questions about the hosting providers' role in combating abuse and the actual impact of vulnerability notifications.

Clickbait PDFs are PDF documents that do not embed malware but trick victims into visiting malicious web pages leading to attacks like password theft or drive-by download. While recent reports indicate a surge of clickbait PDFs, prior works have largely neglected this new threat, considering PDFs only as accessories of email phishing campaigns.

A known problem in the security industry is that programs that deal with executable file formats, such as OS loaders, reverse-engineering tools, and antivirus software, often have little discrepancies in the way they interpret an input file. These differences can be abused by attackers to evade detection or complicate reverse engineering, and are often found by researchers through a manual, trial-and-error process.

As malware detection algorithms and methods become more sophisticated, malware authors adopt equally sophisticated evasion mechanisms to defeat them. Anecdotal evidence claims Living-Off-The-Land (LotL) techniques are one of the major evasion techniques used in many malware attacks. These techniques leverage binaries already present in the system to conduct malicious actions. We present the first large-scale systematic investigation of the use of these techniques by malware on Windows systems.In this paper, we analyse how common the use of these native system binaries is across several malware datasets, containing a total of 31,805,549 samples. We identify an average 9.41% prevalence. Our results show that the use of LotL techniques is prolific, particularly in Advanced Persistent Threat (APT) malware samples where the prevalence is 26.26%, over twice that of commodity malware.To illustrate the evasive potential of LotL techniques, we test the usage of LotL techniques against several fully patched Windows systems in a local sandboxed environment and show that there is a generalised detection gap in 10 of the most popular anti-virus products.

The number of unique malware samples is growing out of control. Over the years, security companies have designed and deployed complex infrastructures to collect and analyze this overwhelming number of samples. As a result, a security company can collect more than 1M unique files per day only from its different feeds. These are automatically stored and processed to extract actionable information derived from static and dynamic analysis. However, only a tiny amount of this data is interesting for security researchers and attracts the interest of a human expert. To the best of our knowledge, nobody has systematically dissected these datasets to precisely understand what they really contain. The security community generally discards the problem because of the alleged prevalence of uninteresting samples. In this article, we guide the reader through a step-by-step analysis of the hundreds of thousands Windows executables collected in one day from these feeds. Our goal is to show how a company can employ existing state-of-the-art techniques to automatically process these samples and then perform manual experiments to understand and document what is the real content of this gigantic dataset. We present the filtering steps, and we discuss in detail how samples can be grouped together according to their behavior to support manual verification. Finally, we use the results of this measurement experiment to provide a rough estimate of both the human and computer resources that are required to get to the bottom of the catch of the day .

For the past two decades, the security community has been fighting malicious programs for Windows-based operating systems. However, the recent surge in adoption of embedded devices and the IoT revolution are rapidly changing the malware landscape. Embedded devices are profoundly different than traditional personal computers. In fact, while personal computers run predominantly on x86-flavored architectures, embedded systems rely on a variety of different architectures. In turn, this aspect causes a large number of these systems to run some variants of the Linux operating system, pushing malicious actors to give birth to "Linux malware." To the best of our knowledge, there is currently no comprehensive study attempting to characterize, analyze, and understand Linux malware. The majority of resources on the topic are available as sparse reports often published as blog posts, while the few systematic studies focused on the analysis of specific families of malware (e.g., the Mirai botnet) mainly by looking at their network-level behavior, thus leaving the main challenges of analyzing Linux malware unaddressed. This work constitutes the first step towards filling this gap. After a systematic exploration of the challenges involved in the process, we present the design and implementation details of the first malware analysis pipeline specifically tailored for Linux malware. We then present the results of the first large-scale measurement study conducted on 10,548 malware samples (collected over a time frame of one year) documenting detailed statistics and insights that can help directing future work in the area.

Code reuse attacks based on return oriented programming (ROP) are becoming more and more prevalent every year. They started as a way to circumvent operating systems protections against injected code, but they are now also used as a technique to keep the malicious code hidden from detection and analysis systems. This means that while in the past ROP chains were short and simple (and therefore did not require any dedicated tool for their analysis), we recently started to observe very complex algorithms -- such as a complete rootkit -- implemented entirely as a sequence of ROP gadgets. In this paper, we present a set of techniques to analyze complex code reuse attacks. First, we identify and discuss the main challenges that complicate the reverse engineer of code implemented using ROP. Second, we propose an emulation-based framework to dissect, reconstruct, and simplify ROP chains. Finally, we test our tool on the most complex example available to date: a ROP rootkit containing four separate chains, two of them dynamically generated at runtime.

Spam has been largely studied in the past years from different perspectives but, unfortunately, it is still an open problem and a lucrative and active business for criminals and bot herders. While several countermeasures have been proposed and deployed in the past decade, their impact and effectiveness is not always clear. In particular, on top of the most common content-and sender-based anti-spam techniques, two minor approaches are popular among system administrators to cope with this annoying problem: greylisting and nolisting. These techniques exploit known features of the Simple Mail Transfer Protocol (SMTP) protocol that are not often respected by spambots. This assumption makes these two countermeasures really simple to adopt and, at least in theory, quite effective. In this paper we present the first comprehensive study of nolisting and greylisting, in which we analyze these spam countermeasures from different perspectives. First, we measure their world-wide deployment and provide insights from their distribution. Second, we measure their effectiveness against areal dataset of malware samples responsible to generate over 70% of the global spam traffic. Finally, we measure the impact of these two defensive mechanisms on the delivery of normal emails. Our study provides a unique and valuable perspective on two of the most innovative and atypical anti-spam systems. Our findings may guide system administrators and security experts to better assess their anti-spam infrastructure and shed some light on myths about greylisting and nolisting.

This paper focuses on the containment and control of the network interaction generated by malware samples in dynamic analysis environments. A currently unsolved problem consists in the existing dependency between the execution of a malware sample and a number of external hosts (e.g. C&C servers). This dependency affects the repeatability of the analysis, since the state of these external hosts influences the malware execution but it is outside the control of the sandbox. This problem is also important from a containment point of view, because the network traffic generated by a malware sample is potentially of malicious nature and, therefore, it should not be allowed to reach external targets.