Fuzzing is a technique of finding bugs by executing a target program recurrently with a large number of abnormal inputs. Most of the coverage-based fuzzers consider all parts of a program equally and pay too much attention to how to improve the code coverage. It is inefficient as the vulnerable code only takes a tiny fraction of the entire code. In this article, we design and implement an evolutionary fuzzing framework called V-Fuzz, which aims to find bugs efficiently and quickly in limited time for binary programs. V-Fuzz consists of two main components: 1) a vulnerability prediction model and 2) a vulnerability-oriented evolutionary fuzzer. Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of a program are more likely to be vulnerable. Then, the fuzzer leverages an evolutionary algorithm to generate inputs which are more likely to arrive at the vulnerable locations, guided by the vulnerability prediction result. The experimental results demonstrate that V-Fuzz can find bugs efficiently with the assistance of vulnerability prediction. Moreover, V-Fuzz has discovered ten common vulnerabilities and exposures (CVEs), and three of them are newly discovered.

Internet users have suffered from phishing attacks for a long time. Attackers deceive users through malicious constructed phishing websites to steal sensitive information, such as bank account numbers, website usernames, and passwords. In recent years, many phishing detection solutions have been proposed, which mainly leverage whitelists or blacklists, website content, or side channel-based techniques. However, with the continuous improvement of phishing technology, current methods have difficulty in achieving effective detection. Hence, in this paper, we propose an effective phishing website detection approach, which we call HinPhish. HinPhish extracts various link relationships from webpages and uses domains and resource objects to construct a heterogeneous information network. HinPhish applies a modified algorithm to leverage the characteristics of different link types in order to calculate the phish-score of the target domain on the webpage. Moreover, HinPhish not only improves the accuracy of detection, but also can increase the phishing cost for attackers. Extensive experimental results demonstrate that HinPhish can achieve an accuracy of 0.9856 and F1-score of 0.9858.

With the rising demand for website data storage, leveraging cloud storage services for vast user file storage has become prevalent. Nowadays, a new file upload scenario has been introduced, allowing web users to upload files directly to the cloud storage service. This new scenario offers convenience but involves more roles (i.e., web users, web servers, and cloud storage services) and their interactions, bringing new security threats. In this paper, we perform the first systematic security study in this scenario. With in-depth analysis, we identify six new types of vulnerabilities and conduct real-world measurements on the top 500 Alexa Rank websites. Among these websites, 182 (36.4%) use cloud storage services, illustrating the widespread use of the cloud. Then, we perform a detailed analysis of 28 popular websites that allow user uploading. Surprisingly, they all have at least one of the six types of vulnerabilities. In total, we discover 79 new vulnerabilities and responsibly report them to the websites. Many popular websites respond positively, including Google, Reddit, and CSDN. etc. We discuss the root causes of these vulnerabilities and propose possible mitigation methods. In summary, our work offers significant value in understanding the security risks of cloud storage services for websites and facilitating future research.

gVisor is a Google-published application-level kernel for containers. As gVisor is lightweight and has sound isolation, it has been widely used in many IT enterprises [1],[2],[3]. When a new vulnerability of the upstream gVisor is found, it is important for the downstream developers to test the corresponding code to maintain the security. To achieve this aim, directed fuzzing is promising. Nevertheless, there are many challenges in applying existing directed fuzzing methods for gVisor. The core reason is that existing directed fuzzers are mainly for general C/C++ applications, while gVisor is an OS kernel written in the Go language. To address the above challenges, we propose G-Fuzz, a directed fuzzing framework for gVisor. There are three core methods in G-Fuzz, including lightweight and fine-grained distance calculation, target related syscall inference and utilization, and exploration and exploitation dynamic switch. Note that the methods of G-Fuzz are general and can be transferred to other OS kernels. We conduct extensive experiments to evaluate the performance of G-Fuzz. Compared to Syzkaller, the state-of-the-art kernel fuzzer, G-Fuzz outperforms it significantly. Furthermore, we have rigorously evaluated the importance for each core method of G-Fuzz. G-Fuzz has been deployed in industry and has detected multiple serious vulnerabilities.

The centralization of the global DNS ecosystem may accelerate the creation of an oligopoly market, thereby, increasing the risk of a single point of failure and network traffic manipulation. Earlier studies have revealed the level of centralization in terms of the market share of public DNS services and DNS traffic seen by major CDN providers. However, the level of centralization in the infrastructure of the DNS Ecosystem is not well understood. In this paper, we present a novel and lightweight measurement approach that effectively discovers resolver pools from a single probing point. We conduct an Internet-wide active measurement on the client-side as well as the server-side DNS infrastructure to assess the level of DNS centralization in terms of the supporting infrastructure. Our measurement results show that the DNS infrastructure is much more centralized than previously believed. Over 90% of forwarding resolvers are backed by less than 5% (4071) of indirect resolvers. Merely 0.45% (12,679) of all name servers across 1138 gTLDs, operated by just 10 DNS providers, provide authoritative domain resolution service for 48.5% (more than 100 million) of domain names. We also investigated several leading DNS providers in IP infrastructure, load distribution, and service geo-distribution. The findings of our measurements provide novel insights into the centrality of the DNS infrastructure, which will help the Internet community promote the understanding of the DNS ecosystem.