Network side-channel attacks have recently been highlighted due to their severity and elusive nature. For example, SADDNS attacks allow an off-path attacker to launch cache poisoning attacks leveraging network side channels. Due to the subtle nature of network side channels, it is challenging to identify such side channels. To this date, few automated bug discovery techniques are tailored for such vulnerabilities. Unfortunately, none of them is general and automated enough, making their impact and longer-term use limited. In this paper, we describe the first solution that aims to fill this gap. Specifically, we develop SCAD, aiming at identifying violations of the non-interference property, which are commonly understood as the root cause of network side channels. As non-interference property is a hyperproperty, it necessitates reasoning across multiple execution traces. This motivated us to develop our solution based on under-constrained and dynamic symbolic execution. The state-of-the-art solution, SCENT, applies model checking, which requires extra effort in modeling or simplifying certain parts of a network protocol, in order to scale. Unfortunately, such modeling and simplification is time-consuming, error prone, and can overlook important details, leading to missed vulnerabilities. For example, it was reported that 2.5 person-week was required to construct a self-contained using SCENT. In comparison, SCAD requires only a single person-day to perform labeling of secrets and attacker-observables, and decide the analysis scope. By applying SCAD to multiple TCP and UDP implementations, including Linux, FreeBSD, and lwIp,we find 14 network side-channels, 7 of which were previously unknown, with a false positive rate of only 17.6%. The results reveal serious vulnerabilities, including those that can be used to compromise the previously patched Linux and FreeBSD kernels, making them susceptible to SADDNS attacks or off-path TCP exploits. Our analysis concludes that the majority of the side channels cannot be found by existing solutions due to the aforementioned limitations.

Online advertisers have been quite successful in circumventing traditional adblockers that rely on manually curated rules to detect ads. As a result, adblockers have started to use machine learning (ML) classifiers for more robust detection and blocking of ads. Among these, AdGraph which leverages rich contextual information to classify ads, is arguably, the state of the art ML-based adblocker. In this paper, we present a4, a tool that intelligently crafts adversarial ads to evade AdGraph. Unlike traditional adversarial examples in the computer vision domain that can perturb any pixels (i.e., unconstrained), adversarial ads generated by a4 are actionable in the sense that they preserve the application semantics of the web page. Through a series of experiments we show that a4 can bypass AdGraph about 81% of the time, which surpasses the state-of-the-art attack by a significant margin of 145.5%, with an overhead of <20% and perturbations that are visually imperceptible in the rendered webpage. We envision that a4’s framework can be used to potentially launch adversarial attacks against other ML-based web applications.

Network intrusion detection systems (NIDS) can be evaded by carefully crafted packets that exploit implementation-level discrepancies between how they are processed on the NIDS and at the endhosts. These discrepancies arise due to the plethora of endhost implementations and evolutions thereof. It is prohibitive to proactively employ a large set of implementations at the NIDS and check incoming packets against all of those. Hence, NIDS typically choose simplified implementations that attempt to approximate and generalize across the different endhost implementations. Unfortunately, this solution is fundamentally flawed since such approximations are bound to have discrepancies with some endhost implementations. In this paper, we develop a lightweight system Themis, which empowers the NIDS in identifying these discrepancies and reactively forking its connection states when any packets with "ambiguities" are encountered. Specifically, Themis incorporates an offline phase in which it extracts models from various popular implementations using symbolic execution. During runtime, it maintains a nondeterministic finite automaton to keep track of the states for each possible implementation. Our extensive evaluations show that Themis is extremely effective and can detect all evasion attacks known to date, while consuming extremely low overhead. En route, we also discovered multiple previously unknown discrepancies that can be exploited to bypass current NIDS.

In this paper, we report a series of flaws in the software stack that leads to a strong revival of DNS cache poisoning --- a classic attack which is mitigated in practice with simple and effective randomization-based defenses such as randomized source port. To successfully poison a DNS cache on a typical server, an off-path adversary would need to send an impractical number of $2^32 $ spoofed responses simultaneously guessing the correct source port (16-bit) and transaction ID (16-bit). Surprisingly, we discover weaknesses that allow an adversary to "divide and conquer'' the space by guessing the source port first and then the transaction ID (leading to only $2^16 +2^16 $ spoofed responses). Even worse, we demonstrate a number of ways an adversary can extend the attack window which drastically improves the odds of success. The attack affects all layers of caches in the DNS infrastructure, such as DNS forwarder and resolver caches, and a wide range of DNS software stacks, including the most popular BIND, Unbound, and dnsmasq, running on top of Linux and potentially other operating systems. The major condition for a victim being vulnerable is that an OS and its network is configured to allow ICMP error replies. From our measurement, we find over 34% of the open resolver population on the Internet are vulnerable (and in particular 85% of the popular DNS services including Google's 8.8.8.8). Furthermore, we comprehensively validate the proposed attack with positive results against a variety of server configurations and network conditions that can affect the success of the attack, in both controlled experiments and a production DNS resolver (with authorization).

As Deep Packet Inspection (DPI) middleboxes become increasingly popular, a spectrum of adversarial attacks have emerged with the goal of evading such middleboxes. Many of these attacks exploit discrepancies between the middlebox network protocol implementations, and the more rigorous/complete versions implemented at end hosts. These evasion attacks largely involve subtle manipulations of packets to cause different behaviours at DPI and end hosts, to cloak malicious network traffic that is otherwise detectable. With recent automated discovery, it has become prohibitively challenging to manually curate rules for detecting these manipulations. In this work, we propose CLAP, the first fully-automated, unsupervised ML solution to accurately detect and localize DPI evasion attacks. By learning what we call the packet context, which essentially captures inter-relationships across both (1) different packets in a connection; and (2) different header fields within each packet, from benign traffic traces only, CLAP can detect and pinpoint packets that violate the benign packet contexts (which are the ones that are specially crafted for evasion purposes). Our evaluations with 73 state-of-the-art DPI evasion attacks show that CLAP achieves an Area Under the Receiver Operating Characteristic Curve (AUCROC) of 0.963, an Equal Error Rate (EER) of only 0.061 in detection, and an accuracy of 94.6% in localization. These results suggest that CLAP can be a promising tool for thwarting DPI evasion attacks.

Recent work has showcased the presence of subtle TCP side channels in modern operating systems, that can be exploited by off-path adversaries to launch pernicious attacks such as hijacking a connection. Unfortunately, most work to date is on the manual discovery of such side-channels, and patching them subsequently. In this work we ask "Can we develop a principled approach that can lead to the automated discovery of such hard-to-find TCP side-channels?" We identify that the crux of why such side-channels exist is the violation of the non-interference property between simultaneous TCP connections i.e., there exist cases wherein a change in state of one connection implicitly leaks some information to a different connection (controlled possibly by an attacker). To find such non-interference property violations, we argue that model-checking is a natural fit. However, because of limitations with regards to its scalability, there exist many challenges in using model checking. Specifically, these challenges relate to (a) making the TCP code base self-contained and amenable to model checking and (b) limiting the search space of model checking and yet achieving reasonable levels of code coverage. We develop a tool that we call SCENT (for Side Channel Excavation Tool) that addresses these challenges in a mostly automated way. At the heart of SCENT is an automated downscaling component that transforms the TCP code base in a consistent way to achieve both a reduction in the state space complexity encountered by the model checker and the number and types of inputs needed for verification. Our extensive evaluations show that SCENT leads to the discovery of 12 new side channel vulnerabilities in the Linux and FreeBSD kernels. In particular, a real world validation with one class of vulnerabilities shows that an off-path attacker is able to infer whether two arbitrary hosts are communicating with each other, within slightly more than 1 minute, on average.

As the popularity of adblocking has soared over the last few years, publishers are increasingly deploying anti-adblocking paywalls that ask users to either disable their adblockers or pay to access content. In this work we propose ShadowBlock, a new Chromium-based adblocking browser that can hide traces of adblocking activities from anti-adblockers as it removes ads from web pages. To bypass anti-adblocking paywalls, ShadowBlock takes advantage of existing filter lists used by adblockers and hides all ad elements stealthily in such a way that anti-adblocking scripts cannot detect any tampering of the ads (e.g., absence of ad elements). Specifically, ShadowBlock introduces lightweight hooks in Chromium to ensure that DOM states queried by anti-adblocking scripts are exactly as if adblocking is not employed. We implement a fully working prototype by modifying Chromium which shows great promise in terms of adblocking effectiveness and anti-adblocking circumvention but also more efficient than the state-of-the-art adblocking browser extensions. Our evaluation on Alexa top-1K websites shows that ShadowBlock successfully blocks 98.3% of all visible ads while only causing minor breakage on less than 0.6% of the websites. Most importantly, ShadowBlock is able to bypass anti-adblocking paywalls on more than 200 websites that deploy visible anti-adblocking paywalls with a 100% success rate. Our performance evaluation further shows that ShadowBlock loads pages as fast as the state-of-the-art adblocking browser extension on average.

Understanding the behaviors of, and evading state-level Internet-scale censorship systems such as the Great Firewall (GFW) of China, has emerged as a research problem of great interest. One line of evasion is the development of techniques that leverage the possibility that the TCP state maintained on the GFW may not represent the state at end-hosts. In this paper we undertake, arguably, the most extensive measurement study on TCP-level GFW evasion techniques, with several vantage points within and outside China, and with clients subscribed to multiple ISPs. We find that the state-of-the art evasion techniques are no longer very effective on the GFW. Our study further reveals that the primary reason that causes these failures is the evolution of GFW over time. In addition, other factors such as the presence of middleboxes on the route from the client to the server also contribute to previously unexpected behaviors.