This article proposes LATTE , the first static binary taint analysis that is powered by a large language model (LLM). LATTE is superior to the state of the art (e.g., Emtaint, Arbiter, Karonte) in three aspects. First, LATTE is fully automated while prior static binary taint analyzers need rely on human expertise to manually customize taint propagation rules and vulnerability inspection rules. Second, LATTE is significantly effective in vulnerability detection, demonstrated by our comprehensive evaluations. For example, LATTE has found 37 new bugs in real-world firmware, which the baselines failed to find. Moreover, 10 of them have been assigned CVE numbers. Lastly, LATTE incurs remarkably low engineering cost, making it a cost-efficient and scalable solution for security researchers and practitioners. We strongly believe that LATTE opens up a new direction to harness the recent advance in LLMs to improve vulnerability analysis for binary programs.

Mobile apps include privacy settings that allow their users to configure how their data should be shared. These settings, however, are often hard to locate and hard to understand by the users, even in popular apps, such as Facebook. More seriously, they are often set to share user data by default, exposing her privacy without proper consent. In this paper, we report the first systematic study on the problem, which is made possible through an in-depth analysis of user perception of the privacy settings. More specifically, we first conduct two user studies (involving nearly one thousand users) to understand privacy settings from the user's perspective, and identify these hard-to-find settings. Then we select 14 features that uniquely characterize such hidden privacy settings and utilize a novel technique called semantics- based UI tracing to extract them from a given app. On top of these features, a classifier is trained to automatically discover the hidden privacy settings, which together with other innovations, has been implemented into a tool called Hound. Over our labeled data set, the tool achieves an accuracy of 93.54%. Further running it on 100,000 latest apps from both Google Play and third-party markets, we find that over a third (36.29%) of the privacy settings identified from these apps are “hidden”. Looking into these settings, we observe that they become hard to discover and hard to understand primarily due to the problematic categorization on the apps' user interfaces and/or confusing descriptions. Further importantly, though more privacy options have been offered to the user over time, also discovered is the persistence of their usability issue, which becomes even more serious, e.g., originally easy-to-find settings now harder to locate. And among all such hidden privacy settings, 82.16% are set to leak user privacy by default. We provide suggestions for improving the usability of these privacy settings at the end of our study.

An emerging Internet business is residential proxy (RESIP) as a service, in which a provider utilizes the hosts within residential networks (in contrast to those running in a datacenter) to relay their customers' traffic, in an attempt to avoid server- side blocking and detection. With the prominent roles the services could play in the underground business world, little has been done to understand whether they are indeed involved in Cybercrimes and how they operate, due to the challenges in identifying their RESIPs, not to mention any in-depth analysis on them. In this paper, we report the first study on RESIPs, which sheds light on the behaviors and the ecosystem of these elusive gray services. Our research employed an infiltration framework, including our clients for RESIP services and the servers they visited, to detect 6 million RESIP IPs across 230+ countries and 52K+ ISPs. The observed addresses were analyzed and the hosts behind them were further fingerprinted using a new profiling system. Our effort led to several surprising findings about the RESIP services unknown before. Surprisingly, despite the providers' claim that the proxy hosts are willingly joined, many proxies run on likely compromised hosts including IoT devices. Through cross-matching the hosts we discovered and labeled PUP (potentially unwanted programs) logs provided by a leading IT company, we uncovered various illicit operations RESIP hosts performed, including illegal promotion, Fast fluxing, phishing, malware hosting, and others. We also reverse engi- neered RESIP services' internal infrastructures, uncovered their potential rebranding and reselling behaviors. Our research takes the first step toward understanding this new Internet service, contributing to the effective control of their security risks.

Recent years have witnessed the rapid progress in deep learning (DP), which also brings their potential weaknesses to the spotlights of security and machine learning studies. With important discoveries made by adversarial learning research, surprisingly little attention, however, has been paid to the real-world adversarial techniques deployed by the cybercriminal to evade image-based detection. Unlike the adversarial examples that induce misclassification using nearly imperceivable perturbation, real-world adversarial images tend to be less optimal yet equally effective. As a first step to understand the threat, we report in the paper a study on adversarial promotional porn images (APPIs) that are extensively used in underground advertising. We show that the adversary today's strategically constructs the APPIs to evade explicit content detection while still preserving their sexual appeal, even though the distortions and noise introduced are clearly observable to humans. To understand such real-world adversarial images and the underground business behind them, we develop a novel DP-based methodology called Male`na, which focuses on the regions of an image where sexual content is least obfuscated and therefore visible to the target audience of a promotion. Using this technique, we have discovered over 4,000 APPIs from 4,042,690 images crawled from popular social media, and further brought to light the unique techniques they use to evade popular explicit content detectors (e.g., Google Cloud Vision API, Yahoo Open NSFW model), and the reason that these techniques work. Also studied are the ecosystem of such illicit promotions, including the obfuscated contacts advertised through those images, compromised accounts used to disseminate them, and large APPI campaigns involving thousands of images. Another interesting finding is the apparent attempt made by cybercriminals to steal others' images for their advertising. The study highlights the importance of the research on real-world adversarial learning and makes the first step towards mitigating the threats it poses.

Virtual personal assistants (VPA) (e.g., Amazon Alexa and Google Assistant) today mostly rely on the voice channel to communicate with their users, which however is known to be vulnerable, lacking proper authentication (from the user to the VPA). A new authentication challenge, from the VPA service to the user, has emerged with the rapid growth of the VPA ecosystem, which allows a third party to publish a function (called skill) for the service and therefore can be exploited to spread malicious skills to a large audience during their interactions with smart speakers like Amazon Echo and Google Home. In this paper, we report a study that concludes such remote, large-scale attacks are indeed realistic. We discovered two new attacks: voice squatting in which the adversary exploits the way a skill is invoked (e.g., ``open capital one''), using a malicious skill with a similarly pronounced name (e.g., ``capital won'') or a paraphrased name (e.g., ``capital one please'') to hijack the voice command meant for a legitimate skill (e.g., ``capital one''), and voice masquerading in which a malicious skill impersonates the VPA service or a legitimate skill during the user's conversation with the service to steal her personal information. These attacks aim at the way VPAs work or the user's misconceptions about their functionalities, and are found to pose a realistic threat by our experiments (including user studies and real-world deployments) on Amazon Echo and Google Home. The significance of our findings has already been acknowledged by Amazon and Google, and further evidenced by the risky skills found on Alexa and Google markets by the new squatting detector we built. We further developed a technique that automatically captures an ongoing masquerading attack and demonstrated its efficacy.

Effectively identifying IoT devices in cyberspace is significant for grasping the security posture of cyberspace. However, there are still some IoT devices without vendor or product keywords in response data that cannot be identified by existing device identification engines. In this paper, we propose a new engine (IoT Tracker)for identifying IoT devices by leveraging the highest similarity of response data between IoT devices of the same vendor or product. Based on the protocol features, IoT Tracker divides application-layer protocols into semistructured data protocols and unstructured data protocols. For each category, IoT Tracker extracts structure structure, style structure or simhash feature. Then, IoTTracker utilizes features extracted from the response data to identify IoT devices. We implement a prototype of our proposed engine and evaluate its effectiveness through real-world experiments. The experimental results show that IoTTracker yield very high accuracy with 95.54 % precision and 93.08 % recall at vendor-level. Compared with existing methods, IoT Tracker adds 40.76% of identifiable devices after de-duplicating the identified dataset.

An increasing number of embedded devices are connecting to the Internet at a surprising rate. Those devices usually run firmware and are exposed to the public by device search engines. Firmware in embedded devices comes from different manufacturers and product versions. More importantly, many embedded devices are still using outdated versions of firmware due to compatibility and release-time issues, raising serious security concerns. In this paper, we propose generating fine-grained fingerprints based on the subtle differences between the filesystems of various firmware images. We leverage the natural language processing technique to process the file content and the document object model to obtain the firmware fingerprint. To validate the fingerprints, we have crawled 9,716 firmware images from official websites of device vendors and conducted real-world experiments for performance evaluation. The results show that the recall and precision of the firmware fingerprints exceed 90%. Furthermore, we have deployed the prototype system on Amazon EC2 and collected firmware in online embedded devices across the IPv4 space. Our findings indicate that thousands of devices are still using vulnerable firmware on the Internet.

Industrial control system (ICS) devices play a crucial role in critical infrastructures, such as power grid. In recent years, numerous ICS devices are accessible on the Internet, resulting in potential security issues. However, there is a lack of deep understanding of these devices' characteristics in the cyberspace. In this paper, we take the first step in this direction by investigating these visible ICS devices on the Internet. Because of the critical nature of ICSs, the detection of online ICS devices should be done in a nonintrusive and timely manner. We first analyze 17 industrial protocols widely used in ICSs and train a probability model through the learning algorithm to improve detection accuracy. Then, we discover online ICS devices in the IPv4 space while reducing the negative effects caused by industrial honeypots and dynamic IP addresses. To observe the dynamics of ICS devices in a relatively long run, we have deployed our discovery system on Amazon EC2 and detected online ICS devices in the whole IPv4 space for eight times from August 2015 to March 2016. Based on the ICS device data collection, we conduct a comprehensive data analysis to characterize the usage of ICS devices, especially in answer to the following three questions: 1) what are the distribution features of ICS devices; 2) who use these ICS devices; and 3) what are the functions of these ICS devices.

Surveillance devices with IP addresses are accessible on the Internet and play a crucial role in monitoring physical worlds. Discovering surveillance devices is a prerequisite for ensuring high availability, reliability, and security of these devices. However, today's device search depends on keywords of packet head fields, and keyword collection is done manually, which requires enormous human efforts and induces inevitable human errors. The difficulty of keeping keywords complete and updated has severely impeded an accurate and large-scale device discovery. To address this problem, we propose to automatically generate device fingerprints based on webpages embedded in surveillance devices. We use natural language processing to extract the content of webpages and machine learning to build a classification model. We achieve real-time and non-intrusive web crawling by leveraging network scanning technology. We implement a prototype of our proposed discovery system and evaluate its effectiveness through real-world experiments. The experimental results show that those automatically generated fingerprints yield very high accuracy of 99% precision and 96% recall. We also deploy the prototype system on Amazon EC2 and search surveillance devices in the whole IPv4 space (nearly 4 billion). The number of devices we found is almost 1.6 million, about twice as many as those using commercial search engines.

Nowadays industrial control devices are crucial for infrastructure-critical systems such as factories, power plants, and water treatment facilities. Devices with IP addresses are visible on the Internet and they connect cyber space and physical world. The first step in protecting devices from attackers is a deep understanding of the devices' characteristics in the cyber space. In this paper, we take a first step in this direction by investigating physical devices running one of the two specific protocols that are widely adopted in industrial control systems. In order to detect these devices in real-time, we propose a two-stage discovery mechanism: first filtering out unqualified hosts from 4 billion remote hosts and then identifying physical devices from qualified candidates. We have conducted a real-world experiment to verify the mechanism and identified dozens of thousands of physical devices from the entire Internet. Results show that our method discovers all devices in 20 hours with 89.5% precision and 79.3% recall.

Nowadays, more and more physical devices embed computing and networking capabilities and are visible on the Internet. These devices include webcams, net-printers, and industrial control equipments, etc. Collecting information about these devices is crucial to preserve cyber-security and facilitate security auditing for system administrators. In this paper, we propose a scalable framework for physical device profiling. It leverages banner grabbing to identify device types and running services, and uses clock skew to determine a device ID. Our framework scales well. We implement a prototype system and use it to profile Webcams and industrial control device. The results show that our system can effectively profile and identify Webcams in real time. We deploy it on the cloud server and use it to detect 4 billion IP addresses to profile 1.2 million Webcams and more than 60 thousand industrial control devices in 20 hours.

Industrial control system (ICS) devices with IP addresses are accessible on the Internet and play a crucial role for critical infrastructures like power grid. However, there is a lack of deep understanding of these devices' characteristics in the cyberspace. In this paper, we take a first step in this direction by investigating these accessible industrial devices on the Internet. Because of critical nature of industrial control systems, the detection of online ICS devices should be done in a real-time and non-intrusive manner. Thus, we first analyze 17 industrial protocols widely used in industrial control systems, and train a probability model through the learning algorithm to improve detection accuracy. Then, we discover online ICS devices in the IPv4 space while reducing the noise of industrial honeypots. To observe the dynamics of ICS devices in a relatively long run, we have deployed our discovery system on Amazon EC2 and detected online ICS devices in the whole IPv4 space for eight times from August 2015 to March 2016. Based on the ICS device data collection, we conduct a comprehensive data analysis to characterize the usage of ICS devices, especially in the answer to the following three questions: (1) what are the distribution features of ICS devices, (2) who use these ICS devices, and (3) what are the functions of these ICS devices.

Industrial control system (ICS) devices with IP addresses are accessible on the Internet and play a crucial role for critical infrastructures like power grid. However, there is a lack of deep understanding on these devices' characteristics in the cyber space. In this paper, we propose ASCEND, a search engine for online industrial control devices. ASCEND analyse 17 industrial protocols and use it to discover almost all online ICS devices in the IPv4 while reducing the noise of industrial honeypots. It provides a big picture of online ICS devices: who are using ICS devices; where they are located and what functions these ICS device have. In order to demonstrate how ASCEND works, we have implemented the prototype system and verified it in the real-world experiments.

Nowadays, the number of visible physical devices exposed on the Internet is dynamically increasing and they play a crucial role for bridging between the cyber space and the physical world, such as network printer, Webcam, and industrial control devices. Discovering these devices brings about the deep understanding on these devices' characteristics and help secure device security in the cyber space. A device fingerprint is a prerequisite of device discovery in the Internet. However, today's online device search depends on keywords of packet head fields and the keyword collection is done manually. This impedes an accurate and large-scale device discovery, due to high human efforts and inevitable human errors, as well as the difficulty of keeping keywords complete and updated. To address this problem, we propose GUIDE, a framework to automatically generate device fingerprints based on webpages embedded in these devices. In order to demonstrate how GUIDE works, we also develop its prototype system and provide a case study which discover surveillance devices in the cyber space.