Antifuzzing

Antifuzzing refers to techniques and strategies employed to detect and defend against fuzzing attacks. Fuzzing is a software testing method that involves providing invalid, unexpected, or random data as input to a computer program to discover programming errors, such as security vulnerabilities, crashes, or memory leaks. Antifuzzing aims to make the target software more resilient to these automated testing approaches.

One common antifuzzing technique is input validation. By rigorously checking and sanitizing all incoming data to ensure it conforms to expected formats and ranges, programs can reject malformed inputs that might otherwise trigger unintended behavior. This can involve checks for data types, lengths, character sets, and adherence to specific protocols.

Another approach is instrumentation. This involves embedding code within the target application to monitor its execution flow and detect anomalous behavior that is characteristic of fuzzing. For example, instrumentation can track the number of states visited, the frequency of certain operations, or the detection of unexpected control flow transfers, all of which can be indicative of a fuzzer at work.

Some antifuzzing methods focus on making the program's execution path more complex or unpredictable. Techniques like control-flow flattening or opaque predicates can make it harder for fuzzers to generate inputs that will effectively explore the program's state space and uncover vulnerabilities.

Finally, signature-based detection can also be employed. This involves maintaining a database of known fuzzing tools and their characteristic input patterns. By analyzing incoming data for these signatures, antifuzzing systems can identify and block traffic originating from known fuzzers. The goal of antifuzzing is to improve software robustness and security by making it more difficult for attackers to discover exploitable flaws through automated means.