AI fuzzing is a cybersecurity practice used by organizations to spot vulnerabilities or flaws within any application or system. Sadly, hackers have now begun using AI fuzzing for committing crimes.
The term ‘data security’ for data is on everyone’s lips now, given the skyrocketing data breaches and other cyber attacks. Every day, a new report on cybercrimes pops up as media headlines. While there are numerous statistics on the current cybersecurity landscape, but the fact that cybercrime will damage 6 trillion dollars annually by 2021, which was 3 trillion dollars in the year 2015, is alarming. This indicates that the traditional security methods of finding out vulnerabilities are failing. Organizations across the world are striving to strengthen their security practices and find a flawless, robust security solution. More and more cybersecurity professionals are joining the ranks to achieve the goal of meeting 100 percent data security. But still, organizations see the same disturbing reports on data breaches. Realizing that something beyond the conventional security method should be done, cybersecurity specialists jumped onto integrating AI and ML with traditional fuzzing techniques. And that’s what AI fuzzing all about. Since ‘An I fuzzing’ is not a common term, most of us aren’t aware of its definition and the concepts associated with it. So, let’s dig forward to comprehend what AI fuzzing is.
What AI Fuzzing Exactly is
Before getting into the concepts of AI fuzzing, let’s understand what exactly fuzzing is. We know that with evolving technologies hackers are getting smarter. Hackers have swiftly moved onto using automated cyberattack techniques to exploit the system and execute malicious activities. Organizations have to follow the same approach of using automated methods to find exploitable bugs or flaws that are prone to hacker attacks. Fuzzing is an automated vulnerability management system in which a random combination of data inputs is entered to detect system errors and loopholes. The inputs entered will determine the system for its robustness, accuracy, and efficiency. Apart from these, the system or the application can also be checked for security bugs. The ultimate goal is to spot vulnerabilities, which can be considered and fixed while developing the system or application. The system is actually tested and checked for weakness at touchpoints, which are indeed the entry points for hackers. When these touchpoints are assessed and examined with several inputs, organizations get a great opportunity to figure out whether the system can be exploited or not.
Now let’s understand how AI fits in here. Fussing is a testing process where random data is intentionally fed into the system. Random attempts mean the outcomes may or may not be accurate. The need is to feed data inputs that cover the entire code of an application or system. But as the technique comprises of massive inputs that are random, the coverage of the code might be incomplete. Hence, there is a need for a system that not only understands a given set of inputs but also learns to create new inputs that will increase the probability of code coverage. One such technology that can comprehend and analyze the previous input, learn from it, and create new inputs is AI. Realizing the problem, organizations are now increasingly integrating AI with the old fuzzing techniques for preparing quality test use cases, which in turn, will improve the vulnerability detection process.
How Hackers are Using AI Fuzzing
We often discuss how AI empowers organizations to mitigate cybersecurity risks, but very little thought is given to the fact that AI can be helpful to cybercriminals too. With the help of AI, hackers can develop systems that allow them to carry out illicit activities easily. Similar is the case with AI fuzzing.
Before understanding how AI fuzzing helps hackers, let’s first comprehend what zero-day vulnerability is. A zero-day vulnerability refers to a situation where the software engineer newly learned about the vulnerability in a program and hackers have perhaps attacked the system. Vulnerabilities, when discovered, have to be fixed at the earliest. A zero-day attack occurs when a developer detects a system flaw and hackers exploiting the system before she releases a patch. Hackers leveraging AI fuzzing will be able to speed up their discovery of the zero-day vulnerabilities easily.
Hackers can use AI for two phases - exploration and exploitation. First, bad actors can analyze and understand the functionality of the targeted system. After grasping patterns of the target system, hackers then intentionally bombard the program with data, view and assess the result, use AI to understand the vulnerabilities, improve the attack to wreck the target. By repeatedly trying out combinations using AI, hackers can discover more zero-day vulnerabilities and also break them.
How Adverse is the Impact of AI Fuzzing
“Software vendors were already struggling to keep up with patches for software bugs; the use of Fuzzing tools by hackers and the flood of newly discovered vulnerabilities may overwhelm software vendors’ ability to respond with patches,” said Paul Henry, Vice President of a global leader in enterprise security solutions - Secure Computing. Besides, findings of the security vendor Secure Computing states that hackers use AI fuzzing and share with their coworkers through online chat rooms and newsgroups, thereby intensifying the cybersecurity threat issue. Furthermore, according to security researchers, attackers can get access to computers of users via the Internet connection even if they don't have an active Wifi connection. Using the AI fuzzing method, the bad actors then check for vulnerabilities in the system to break their system.
An Australian tech support and services company named AI fuzzing as 8 out of 10 top security threats of 2019. Now that says how adverse the impact of AI fuzzing is. Another cyber attack form that is performed by hackers in parallel to AI fuzzing is ML poisoning. Like AI, ML is another advanced, cutting-edge technology that is helping organizations streamline and optimize their workflow execution. Besides, ML is leveraged to fight against cybercrimes. But unfortunately, hackers also use ML for their benefit. If malicious actors happen to compromise and enter an ML system, they can implant an ML code that directs the system to execute specific instructions after learning the given situation. The instructions can range from not reacting to patches to not calculating certain traffic data so that the injected malicious ML code cannot be discovered. In a nutshell, hackers try to poison ML systems to carry out their evil activities.
Obviously, these newest cybersecurity threats cannot be fixed using traditional security approaches. Along with rethinking the security technologies and developing strategies to curb these issues, organizations should try to impact the hacker enterprise’s economic models. Leveraging the potential of automation and new-age technologies will help organizations to not only predict the criminal intent but also disrupt the economic strategies. Furthermore, test the systems and programs for bugs using the advanced technologies for enhanced results. Ensure that the touchpoints are not vulnerable to attacks. With everything well considered, organizations can save their digital assets from hackers, for sure.