This course teaches you the foundations of fuzzing and how to apply it on real-world complex software. The course focuses on source-based fuzzing, which means that we will only fuzz software for which we have source code available. This course brings a deep and comprehensive view into fuzzing, and there is a lot of material to cover. We do not just show how to run a few commands - we go deep into the code of targets as well as fuzzers, find bugs in real-world projects and more. Following completion of the course you will be able to fuzz professionally at a high level.
Throughout the course you have several exercises and assignments. The assignments ask you to fuzz real-world software and you receive personalised feedback on your work. In addition to this, you can ask the instructor any qestion at any stage in the process, ensuring you have maximum flexbility to learn in your own manner and at your own pace.
The course is heavily focused on real-world connection, and because of this you will be fuzzing many real-world projects in the course. You will learn how to surgically target your analysis towards specific parts of the application you are analysing, and do so against some of the most complex applications in the world such as Chromium.
Who should attend?
In the course we will be reading a lot of C/C++ code, so it is expected that students are familiar with reading source code in these languages and also writing small applications.
If you are a full-time student and can prove this, then you are eligible for a 25% discount. To claim the student discount, buy the course on our website and notify us in the message field that you would like the students discount. This discount is only valid for full prices, i.e. no discounts on early-bird prices.
Why study fuzzing?
Automating the process of uncovering programming errors in software is a well-known task. Fuzzing is one of the core techniques for doing this and the basic idea behind fuzzing is to send large amounts of pseudo-random inputs to a given target application and for each input observe whether the input triggers any faulty behaviour in the target. This technique has drastically increased in popularity in recent years. For example, the oss-fuzz project run by Google has found over 16,000 bugs in 250 open source projects since its inception in January 2017 to January 2020.
Many research efforts have been put into fuzzing, so whereas fuzzing was originally introduced as sending large amounts of random inputs, the concept has now evolved into sophisticated science that relies on rigorous program analysis techniques to optimally send “interesting’’ inputs in large amounts. Some fuzzers even adapt techniques from software verification to ensure a more calculated approach to crafting inputs. Fuzzing is particularly useful to finding memory corruption bugs that often trigger sophisticated exploits and can discover a variety of types like stack-based buffer overflows, heap-based buffer overflows, memory-out-of-bounds, null-pointer dereference and use-after-free.
Fuzzing is popular in the industry. The Behemoths of the software industry like Microsoft and Google have performed fuzzing for more than a decade now. DARPA invested more than 55 million in a project called Cyber Grand Challenge where more than 100 teams developed new techniques for automated vulnerability discovery and the vast majority of top-performing teams relied on Fuzzing as the main way of finding vulnerabilities. Fuzzing has empirically proven itself as a major way of automating the bug-finding process and the technique has placed itself as one of the de facto standards for ensuring secure software.