Fuzzing

[aamedina]

Fuzzing

Fuzzing is an automated software testing technique that systematically provides invalid, unexpected, or random data inputs to software applications to detect coding errors, security vulnerabilities, and unintended behaviors.

Digital Artifacts

• Executable Binary (d3f:ExecutableBinary): The software applications being tested.
• Input Function (d3f:InputFunction): Interfaces through which the software accepts inputs (e.g., APIs, network sockets, file parsers).

Definition

Fuzzing is an automated method of testing software by injecting malformed, unexpected, or random data into a system's input functions to identify defects such as crashes, exceptions, or security vulnerabilities. The technique aims to uncover errors and weaknesses in software that may not be detected through conventional testing, enhancing the overall robustness and security of the application.

How it works

Fuzzing tools, known as fuzzers, generate a large volume of test cases by mutating valid input data or creating random inputs within the expected input format. These inputs are systematically fed into the software's input functions. The software is monitored for anomalies such as crashes, hangs, memory leaks, or incorrect outputs. Instrumentation may be used to trace execution paths and improve code coverage, allowing for precise identification of vulnerabilities like buffer overflows, input validation errors, and race conditions. By exploring a wide range of input scenarios, fuzzing exposes defects that could be exploited by attackers or cause unintended behavior.

Considerations

Pros:

• Proactive Vulnerability Detection: Identifies security flaws and coding errors before software deployment.
• Automation: Efficiently tests a vast number of input combinations without manual intervention.
• Improves Software Quality: Enhances robustness and reliability by uncovering hidden bugs.

Cons:

• Resource Intensive: Requires substantial computational resources and time.
• Expert Analysis Required: Interpreting results may need specialized knowledge to differentiate between exploitable vulnerabilities and non-critical issues.
• Limited Scope: May not detect logical errors or vulnerabilities that do not cause observable failures.

Pitfalls:

• False Positives: Not all detected anomalies represent exploitable security vulnerabilities.
• Incomplete Coverage: May miss code paths that require specific conditions or sequences of inputs.
• Dependency on Proper Configuration: Effectiveness relies on correct setup, including appropriate instrumentation and monitoring tools.

References

• American Fuzzy Lop (AFL): A powerful open-source fuzzer for discovering vulnerabilities. http://lcamtuf.coredump.cx/afl/
• OSS-Fuzz: Google's continuous fuzzing service for open-source software. https://github.com/google/oss-fuzz
• Fuzzing: Detailed overview on Wikipedia. https://en.wikipedia.org/wiki/Fuzzing
• "Fuzzing: Brute Force Vulnerability Discovery", Michael Sutton, Adam Greene, Pedram Amini.
• CAPEC-28: Also used an Offensive Technique https://capec.mitre.org/data/definitions/28.html

Related to #193

[ryantxu1]

Agree this should be added