Continuous Search for the UnknownForAllSecure on the Next-Generation fuzzing
In the field of software security, the aim is always to deploy code that is free of vulnerabilities. Mainly since malicious attackers often target such program and system failures.
A company’s software can be entirely trustworthy because of the security methods that are employed in that company. That is something Google can be proud of, for example, with their Chrome browser.
According to Brumley, Google and Microsoft are among the organizations that practice a new security testing methodology. They act within a new generation scheme, following a process similar to that which a real hacker would follow.
But before we make an overview of this process, let's put it in context.
Security tests tend to be thought of as being carried out at specific times. For example, as soon as a program has been developed and before it is available to users. What the use of testing is intended to find is the presence of vulnerabilities.
Vulnerabilities may correspond to mistakes that developers made in their work with code. These vulnerabilities can be classified as known, unknown, and zero-day.
Known vulnerabilities are those that have already been disclosed to the security communities and software vendors. Unknown vulnerabilities are those that have not been discovered by anyone. And zero-day vulnerabilities have already been found by some subjects, but have not been disclosed to vendors or communities.
If we take the first one, we’re referring to Static Analysis Security Testing. This form of white-box testing uncovers vulnerabilities through an analysis of the source code when the software is in a non-running state.
The second technique is the Dynamic Analysis Security Testing, which corresponds to a form of black-box testing that uncovers vulnerabilities by analyzing software in a running state without accessing the source code.
Almost all static and dynamic analysis tools search for known vulnerabilities or test for known attack patterns. For the discovery of unknown or zero-day vulnerabilities, it is necessary to perform negative testing.
Negative testing is a form of verification and validation testing (process to guarantee that apps operate as expected). Contrary to positive testing, in this process, an invalid set of inputs is sent to the application. In this instance, it must be verified that the app remains stable in the face of unexpected use. Combinations of invalid inputs can be almost infinite, so testing them all before deploying the application may be unlikely.
It is here that the use of continuous hacking appears as recommended, a process that goes beyond one-pass checks. While people use the software and hackers try to find the vulnerabilities, continuous hacking is conducted as a security testing, and it is performed as the software evolves.
Concerning the negative testing, we can then mention continuous fuzzing. As Ispoglou et al. (2019), from Google, share: "Fuzzing is a testing technique to discover unknown vulnerabilities in software."
Microsoft and Google are using a "Next-Generation fuzzing," a dynamic analysis technique (really under grey-box testing category) that can look for unknown vulnerabilities, and that following Brumley is located in the last quadrant, we can see here in Figure 1:
Figure 1. taken from the PDF of the webinar
Fuzzing can also detect known and zero-day vulnerabilities. With the use of invalid inputs, fuzzers are intended to provoke anomalous behaviors in the software, such as memory leaks, infinite loops, and crashes. These behaviors may be linked in some way to underlying vulnerabilities.
There are four categories in which we can divide fuzzing:
In Random fuzzing, random inputs are sent to an application. Many of the inputs fail to penetrate the app, so there is low code coverage.
In Template fuzzing, customized inputs are employed, and they are modified to include the irregularities. Within this category, compared to the previous one, the penetration to an application is more probable. However, the fuzzing process is limited to the scope of the template.
For the Generational fuzzing, engineers develop lots of test cases written to resemble a valid input. Some fuzzers of this type can prioritize test cases based on feedback from the target, which can increase the chances of penetration and of triggering anomalous behavior.
In Evolutionary fuzzing, the fuzzers are intelligent. They have the ability to monitor and leverage the behavior of the target for the automatic generation of new custom test cases during the process. Besides, they don’t get test cases to begin with.
More in line with this last category, the Next-Generation fuzzing works with random guessing, and the fuzzer learns as it automatically runs the program. The fuzzer tries to explore all paths, and never repeat any of them twice, always moving on to a new one. The automatic generation of new test cases, thanks to feedback on the target’s reactions, can help in the discovery of more defects and new code edges.
That process can be quite useful in finding the vulnerabilities before the attackers do. And that is something that many organizations are currently requesting.
For the sake of improving their cybersecurity, companies should understand that simple tests and scans, with predetermined paths, and at specific times, may limit the findings of vulnerabilities. Companies must be aware that: whether through automatic systems or manual work, continuous hacking will always mean more depth and thoroughness in the search for vulnerabilities.
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