Finding sands in the eyes: vulnerabilities discovery in IoT with EUFuzzer on human machine interface

In supervisory control and data acquisition (SCADA) systems or the Internet of Things (IoT), human machine interface (HMI) performs the function of data acquisition and control, providing the operators with a view of the whole plant and access to monitoring and interacting with the system. The compromise of HMI will result in lost of view (LoV), which means the state of the whole system is invisible to operators. The worst case is that adversaries can manipulate control commands through HMI to damage the physical plant. HMI often relies on poorly understood proprietary protocols, which are time-sensitive, and usually keeps a persistent connection for hours even days. All these factors together make the vulnerability mining of HMI a tough job. In this paper, we present EUFuzzer, a novel fuzzing tool to assist testers in HMI vulnerability discovery. EUFuzzer first identifies packet fields of the specific protocol and classifies all fields into four types, then using a relatively high efficiency fuzzing method to test HMI. The experimental results show that EUFuzzer is capable of identifying packet fields and revealing bugs. EUFuzzer also successfully triggers flaws of actual proprietary SCADA protocol implementation on HMI, which the SCADA software vendor has confirmed that four were zero-day vulnerabilities and has taken measures to patch up

Prospex:ProtocolSpecificationExtraction

Protocol reverse engineering is the process of extracting application-level specifications for network protocols. Such specificationsare very useful in a numberof security-related contexts, forexample, to perform deep packet inspectionand black-box fuzzing, or to quickly understand custom botnet command and control (C&C) channels. Since manual reverse engineering is a time-consuming and tedious process, a number of systems have been proposed that aim to automate this task. These systems either analyze network traffic directly or monitor the execution of the application that receivestheprotocolmessages.While previoussystemsshow thatprecise message formatscanbe extractedautomatically, they do not provide a protocol specification. The reason is that they do not reverse engineerthe protocol state machine. In this paper, we focus on closing this gap by presenting a system that is capable of automatically inferring state machines. This greatly enhances the results of automatic protocol reverse engineering, while further reducing the need for human interaction. We extend previous work that focuses on behavior-based message format extraction, and introduce techniques for identifying and clustering different types of messages not only based on their structure, but also accordingto the impact of each message on server behavior. Moreover, we present an algorithm for extracting the state machine. We have applied our techniques to a number of real-world protocols, including the command and control protocol used by a malicious bot. Our results demonstrate that we are able to extract format specifications for different types of messages and meaningful protocol state machines. We use these protocol specifications to automatically generate input for a stateful fuzzer, allowing us to discover security vulnerabilities in real-world applications. 1

Using grid computing for large scale fuzzing

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2010Neste projeto, o nosso objetivo é usar a técnica de teste de fuzzing, que fornece dados inválidos, inesperados ou aleatórios para a entrada de um programa para nele tentar encontrar vulnerabilidades. Os resultados do teste fornecem ao programador informações para melhorar o programa, nomedamente para torná-lo mais seguro. Um ambiente de computação em grade é usado para suportar o fuzzing das aplicações usando simultaneamente os recursos de vários computadores em uma rede, a fim de paralelizar o processo e permitir tentar muitas entradas diferentes. Um trabalho de fuzzing é dividido em várias tarefas de fuzzing e distribuído aos recursos de rede que se encontrem livres para que a operação seja realizada. Um broker recebe as solicitações de fuzzing de clientes, e insere a divisão de tarefas num servidor Web, como o Apache. Quando os recursos da rede estão disponíveis, as tarefas de difusão são descarregadas a partir do servidor web e automaticamente executadas e os resultados retornados ao serviço de coordenação. O serviço de coordenação Zookeeper é usado para sincronizar o broker, o servidor web e dos recursos.In this project, our goal is to use a testing technique called fuzzing that provides invalid, unexpected or random data to the input fields of an application to find vulnerabilities in the same application. The testing results provide a programmer with information to improve the program, making it more secure. A Grid computing environment was designed to support the fuzzing of applications, by using simultaneously the resources of many computers in a network, in order to parallelize the process and allow trying many different inputs. One fuzzing job is divided into many fuzzing tasks and distributed to the free network resources for fuzzing. A broker gets the fuzzing requests from clients, and then inserts the split fuzzing tasks into a Web server, like Apache. When resources in the network are available, fuzzing tasks will be downloaded from the web server and resources will automatically execute these tasks and return the results to ZooKeeper. The ZooKeeper coordination service is used for synchronizing the broker, the web server and the resources

Qualitative Analysis for Validating IEC 62443-4-2 Requirements in DevSecOps

Validation of conformance to cybersecurity standards for industrial automation and control systems is an expensive and time consuming process which can delay the time to market. It is therefore crucial to introduce conformance validation stages into the continuous integration/continuous delivery pipeline of products. However, designing such conformance validation in an automated fashion is a highly non-trivial task that requires expert knowledge and depends upon the available security tools, ease of integration into the DevOps pipeline, as well as support for IT and OT interfaces and protocols. This paper addresses the aforementioned problem focusing on the automated validation of ISA/IEC 62443-4-2 standard component requirements. We present an extensive qualitative analysis of the standard requirements and the current tooling landscape to perform validation. Our analysis demonstrates the coverage established by the currently available tools and sheds light on current gaps to achieve full automation and coverage. Furthermore, we showcase for every component requirement where in the CI/CD pipeline stage it is recommended to test it and the tools to do so