CAPEC-261: Fuzzing for garnering other adjacent user/sensitive data
An adversary who is authorized to send queries to a target sends variants of expected queries in the hope that these modified queries might return information (directly or indirectly through error logs) beyond what the expected set of queries should provide.
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Overview
Many client applications use specific query templates when interacting with a server and often automatically fill in specific fields or attributes. If the server does not verify that the query matches one of the expected templates, an adversary who is allowed to send normal queries could modify their query to try to return additional information. The adversary may not know the names of fields to request or how other modifications will affect the server response, but by attempting multiple plausible variants, they might eventually trigger a server response that divulges sensitive information. Other possible outcomes include server crashes and resource consumption if the unexpected queries cause the server to enter an unstable state or perform excessive computation.
How the attack works
The phases an attacker typically follows to carry out this attack.
- Step 1Explore
[Observe communication and inputs] The fuzzing adversary observes the target system looking for inputs and communications between modules, subsystems, or systems.
- Network sniffing. Using a network sniffer such as wireshark, the adversary observes communications into and out of the target system.
- Monitor API execution. Using a tool such as ktrace, strace, APISpy, or another debugging tool, the adversary observes the system calls and API calls that are made by the target system, and the nature of their parameters.
- Observe inputs using web inspection tools (OWASP's WebScarab, Paros, TamperData, TamperIE, etc.)
- Step 2Experiment
[Generate fuzzed inputs] Given a fuzzing tool, a target input or protocol, and limits on time, complexity, and input variety, generate a list of inputs to try. Although fuzzing is random, it is not exhaustive. Parameters like length, composition, and how many variations to try are important to get the most cost-effective impact from the fuzzer.