Black-box web application vulnerability scanners attempt to automatically identify vulnerabilities in web applications without access to the source code. However, they do so by using a manually curated list of vulnerability-inducing inputs, which significantly reduces the ability of a black-box scanner to explore the web application’s input space and which can cause false negatives. In addition, black-box scanners must attempt to infer that a vulnerability was triggered, which causes false positives.To overcome these limitations, we propose Witcher, a novel web vulnerability discovery framework that is inspired by grey-box coverage-guided fuzzing. Witcher implements the concept of fault escalation to detect both SQL and command injection vulnerabilities. Additionally, Witcher captures coverage information and creates output-derived input guidance to focus the input generation and, therefore, to increase the state-space exploration of the web application. On a dataset of 18 web applications written in PHP, Python, Node.js, Java, Ruby, and C, 13 of which had known vulnerabilities, Witcher was able to find 23 of the 36 known vulnerabilities (64%), and additionally found 67 previously unknown vulnerabilities, 4 of which received CVE numbers. In our experiments, Witcher outperformed state of the art scanners both in terms of number of vulnerabilities found, but also in terms of coverage of web applications.

In this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN). Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. Features indicative of malware are automatically learned by the network from the raw opcode sequence thus removing the need for hand-engineered malware features. The training pipeline of our proposed system is much simpler than existing n-gram based malware detection methods, as the network is trained end-to-end to jointly learn appropriate features and to perform classification, thus removing the need to explicitly enumerate millions of n-grams during training. The network design also allows the use of long n-gram like features, not computationally feasible with existing methods. Once trained, the network can be efficiently executed on a GPU, allowing a very large number of files to be scanned quickly.