Best file fuzzer

Broadly speaking, fuzzers can be split into two categories based on how they create input to programs — mutation-based and generation-based. This section details those categories as well as offering a brief description of a more advanced technique called Evolutionary Fuzzing. Mutation-based fuzzers are arguably one of the easier types to create. This technique suites dumb fuzzing but can be used with more intelligent fuzzers as well.

With mutation, samples of valid input are mutated randomly to produce malformed input. A dumb mutation fuzzer can simply select a valid sample input and alter parts of it randomly. For many programs, this can provide a surprising amount of mileage, as inputs are still often significantly similar enough to a valid input. This means good code coverage can be achieved without the need for further intelligence.

A fuzzer can take saved sample inputs and replay them after mutating them. This works well for file format fuzzing where a number of sample files can be saved and fuzzed to provide to the target program. For a more complex protocol, replay may be more difficult.

This is because the fuzzer may need to respond in a dynamic way to the program to allow processing to continue deep into the protocol, or the protocol may simply be inherently non-replayable. You may have heard of Man-in-the-Middle MITM as a technique used by penetration testers and hackers, but it can also be used for mutation-based network protocol fuzzing.

With MITM, you place yourself in the middle of a client and server or two clients in the case of peer-to-peer networking , intercepting and possibly modifying messages passed between them. Again, the fuzzer could have no intelligence about the protocol and randomly alter some requests and not others.

Proxy-based fuzzing can allow you to take an existing deployment of a networked program and quickly insert a fuzzing layer into it, without needing to make your fuzzer act like a client or server itself. Generation-based fuzzers actually generate input from scratch rather than mutating existing input.

They usually require some level of intelligence to construct input that makes at least some sense to the program, although generating completely random data would also technically be generation. Generation fuzzers often split a protocol or file format into chunks, which they can build up in a valid order, and randomly fuzz some of those chunks independently.

This can create inputs that preserve their overall structure, but contain inconsistent data within it. Generation fuzzing can also get deeper into a protocol more easily, as it can construct valid sequences of inputs applying fuzzing to specific parts of that communication. It allows the fuzzer to use feedback from each test case to learn the format of the input over time. For example, by measuring the code coverage of each test case, the fuzzer can work out which properties of the test case exercise a given area of code, and gradually evolve a set of test cases that cover the majority of the program code.

Evolutionary fuzzing often relies on other techniques similar to genetic algorithms and may require some form of binary instrumentation to operate correctly. Or if you were testing an OCR program that parsed images of text into real text, but you were mutating the whole of an image file, you could end up testing its image parsing code more often than the actual OCR code.

If you wanted to target that OCR processing specifically, you might wish to keep the headers of the image file valid. Fuzzers often split many of these tasks into separate modules. For example, having one library that can mutate data or generate it based on a definition and another to provide test cases to the target program and so on. Below are some notes on each of these tasks. Generating test cases will vary depending on whether mutation-based or generation-based fuzzing is being employed.

As such, you may wish to favor such cases and include values such as:. For example:. The simplest way to reproduce a test case is to record the exact input used when a crash is detected.

However, there are other ways to ensure reproducibility that can be more convenient in certain circumstances. One way to do this is storing the initial seed used for the random component of test case generation, and ensuring all subsequent random behavior follows a path that can be traced back to that seed. By re-running the fuzzer with the same seed, the behavior should be reproducible. For example, you may only record the test case number and the initial seed and then quickly re-execute generation with that seed until you reach the given test case.

This technique can be useful when the target program may accumulate dependencies based on past inputs. Previous inputs may have caused the program to initialize various items in its memory that are required to be present to trigger the bug. Interfacing with the target program to provide the fuzzed input is often straightforward.

For network protocols, it may involve sending the test case over the network, or responding to a client request. For file formats, it could mean executing the program with a command line argument pointing to the test case. Creative thinking in these cases can reveal ways to exercise the relevant piece of code with the right data.

For example, this may be performed by instrumenting a program in memory artificially to execute a parsing function with the input provided as an argument entirely in memory.

This can remove the need for the program to go through a lengthy loading procedure before each test case. And further speed increases could be obtained by having test cases generated and provided completely in memory rather than going via the hard drive.

The data-generation part is made of generators, and vulnerability identification relies on debugging tools. Generators usually use combinations of static fuzzing vectors known-to-be-dangerous values , or totally random data. New generation fuzzers use genetic algorithms to link injected data and observed impact. Such tools are not public yet. The number of possible tryable solutions is the explorable solutions space. The aim of cryptanalysis is to reduce this space, which means finding a way of having less keys to try than pure bruteforce to decrypt something.

In this regard, Fuzzers try to reduce the number of unuseful tests, i. You can verify if the uncovered files and directories have proper permissions configured and if they leak any sensitive information.

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You can use the URL Fuzzer to find hidden files and directories on a web server by fuzzing. This is a discovery activity which allows you to discover resources that were not meant to be publicly accessible e.

Since "security by obscurity" is not a good practice, you can often find sensitive information in the hidden locations the URL Fuzzer identifies. The URL Fuzzer uses a custom-built wordlist for discovering hidden files and directories. The wordlist contains more than common names of known files and directories. The files and directories the tool finds are returned along with their HTTP response code and the page size. Sample Report. Use Cases. Technical Details. Scan type Light scan Full scan Target.

I am authorized to scan this target and I agree with the Terms of Service. Scan target. Reporting Sample Report Here is a URL Fuzzer sample report that gives you a taste of how our tools save you time and reduce repetitive manual work.

Download sample report. Discover Sensitive Information It is highly likely that you will find sensitive information in the hidden files and directories hosted on the target web server. Extend the Attack Surface You can also use this tool to uncover hidden functionality in web applications hidden paths that you can further explore to find more vulnerabilities.

Check for Configuration Errors You can verify if the uncovered files and directories have proper permissions configured and if they leak any sensitive information. Better vulnerability discovery. Faster pentest reporting. Create your account. You can optionally insert your payload in a custom location specified by a 'FUZZ' marker.