2023 is coming in hot. If you don't want to miss out on this year's best events, you better start planning your conference schedule early. To help you out, I created an overview of my personal top 10 favorite cybersecurity events in 2023 about application security, cloud security, IoT, and all the other topics that currently occupy the cybersecurity world.
All software has bugs, and some can be difficult to find or reproduce. However, not all approaches to bug-finding need to be difficult to use! Fuzzing is an undeniably effective approach to finding security issues and bugs in software projects, however, tools can be complex to set up and execute. CI Fuzz CLI (open-source), automates the parts that make fuzzing complex, giving its users the look and feel of a unit test.
Today, I want to show you a way how to increase the integrity and functionality of your Java applications, with fuzz testing. This awesome testing approach has done me great service for building more secure Java applications, and it's basically as simple as unit testing. In this article, I will share how you can apply fuzz testing to your own code. And the best part: all code examples and tools I will use are 100% open-source.
Mock testing, also called mocking, is an integral part of the embedded software development process as it allows you to test your code without relying on actual hardware. This can be extremely helpful when trying to debug your code or test new features. During fuzzing testing, applications are tested using unexpected or invalid inputs. Modern fuzzers generate these inputs based on feedback about the SUT’s interaction with previous test inputs.
In software testing, negative testing refers to the practice of feeding a system with unexpected or invalid inputs. Given an input field that accepts numeric values from 0-100, positive tests would assess if the application does what it's supposed to do, given input values such as "1", "2" or "99".
Due to increasing connectivity and dependencies, modern embedded applications in many industries including automotive, aviation, and even automated cow brushes (no joke) are constantly growing more complex. This complexity comes with implications for embedded testing tools and requires plenty of manual effort, depending on the toolchain. From an operational perspective, many embedded industries are tightly staffed and work in long cycles with strict deadlines.
The growing complexity of embedded systems coupled with the advent of increasingly sophisticated security attacks highlights a dire need for advanced automated vulnerability analysis tools. Fuzzing is an effective proven technique to find security-critical issues in systems, often without needing to fully understand the internals of the system under test.
Today, I would like to show you to a simplified fuzz testing approach that enables secure coding of C and C++ applications. If you read this article to the end, you will learn about an automated security testing approach for C/C++ that can protect your applications against all sorts of memory corruptions and other common C/C++ vulnerabilities.
The modern vehicle comes equipped with a variety of software systems. Especially features that connect it to the outside world, such as online updates, fleet management and communication between vehicles, offer attack surface. The security of automotive software is crucial, not only because bug-induced call-backs are costly, but also because the well-being of passengers depends on it.
