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CAPEC-28: Fuzzing

 
Fuzzing
Definition in a New Window Definition in a New Window
Attack Pattern ID: 28
Abstraction: Standard
Status: Draft
Completeness: Complete
Presentation Filter:
+ Summary

Fuzzing is a software testing method that feeds randomly constructed input to the system and looks for an indication that a failure in response to that input has occurred. Fuzzing treats the system as a black box and is totally free from any preconceptions or assumptions about the system.

An attacker can leverage fuzzing to try to identify weaknesses in the system. For instance fuzzing can help an attacker discover certain assumptions made in the system about user input. Fuzzing gives an attacker a quick way of potentially uncovering some of these assumptions without really knowing anything about the internals of the system. These assumptions can then be turned against the system by specially crafting user input that may allow an attacker to achieve his goals.

+ Attack Execution Flow
Explore
  1. Observe communication and inputs:

    The fuzzing attacker observes the target system looking for inputs and communications between modules, subsystems, or systems.

    Attack Step Techniques

    IDAttack Step Technique DescriptionEnvironments
    1

    Network sniffing. Using a network sniffer such as wireshark, the attacker observes communications into and out of the target system.

    env-Web env-ClientServer env-Peer2Peer env-CommProtocol
    2

    Monitor API execution. Using a tool such as ktrace, strace, APISpy, or another debugging tool, the attacker observes the system calls and API calls that are made by the target system, and the nature of their parameters.

    env-Local env-Embedded
    3

    Observe inputs using web inspection tools (OWASP's WebScarab, Paros, TamperData, TamperIE, etc.)

    env-Web

    Outcomes

    IDTypeOutcome Description
    1Success
    The attacker creates a list of unique communications packets, messages, inputs, API calls or other actions the software takes.

    Security Controls

    IDTypeSecurity Control Description
    1Detective
    Alert on promiscuous mode. Some network devices (switches, hubs) or monitoring stations (e.g., IDS) can detect and alert when a station in the network is passively eavesdropping.
    2Preventative
    Some production hardware (for embedded environments) can have debugging disabled on the hardware.
    3Preventative
    Techniques exist to insert no-ops and other null branches that thwart basic attempts to execute software stepwise in a debugger.
Experiment
  1. 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.

    Attack Step Techniques

    IDAttack Step Technique DescriptionEnvironments
    1

    Boundary cases. Generate fuzz inputs that attack boundary cases of protocol fields, inputs, or other communications limits. Examples include 0xff and 0x00 for single-byte inputs. In binary situations, approach each bit of an individual field with on and off (e.g., 0x80).

    env-All
    2

    Attempt arguments to system calls or APIs. The variations include payloads that, if they were successful, could lead to a compromise on the system.

    env-Local env-Embedded

    Security Controls

    IDTypeSecurity Control Description
    1Detective
    Log unexpected parameters to API calls or system calls.
    2Preventative
    Profile the software's expected use of system calls. Use a sandboxing technique to restrict its API calls to the expected patterns.
    3Preventative
    SSL or other link-layer encryption techniques (VPN, 802.11x, etc.) can impair simple observation and require a would-be attacker to work much harder to get information about the operation of the software..
  2. Observe the outcome:

    Observe the outputs to the inputs fed into the system by fuzzers and see if anything interesting happens. If failure occurs, determine why that happened. Figure out the underlying assumption that was invalidated by the input.

    Indicators

    IDTypeIndicator DescriptionEnvironments
    1Positive

    The software produces an indicator that the attacker can see (error message, altered error state in a protocol, etc.).

    env-All
    2Positive

    The previous step led to plausible, practical fuzz inputs.

    env-All

    Outcomes

    IDTypeOutcome Description
    1Success
    If the software's indicators (error messages, etc.) vary clearly based on the attackers' input, then the attacker has a sufficient starting point for customizing his attack.
    2Failure
    The attacker is unable to induce unexpected failures or output based fuzzed inputs.
Exploit
  1. Craft exploit payloads:

    Put specially crafted input into the system that leverages the weakness identified through fuzzing and allows to achieve the goals of the attacker. Fuzzers often reveal ways to slip through the input validation filters and introduce unwanted data into the system.

    Attack Step Techniques

    IDAttack Step Technique DescriptionEnvironments
    1

    Identify and embed shell code for the target system.

    env-All
    2

    Embed higher level attack commands in the payload. (e.g., SQL, PHP, server-side includes, etc.)

    env-Web env-CommProtocol env-Peer2Peer env-ClientServer
    3

    Induce denial of service by exploiting resource leaks or bad error handling.

    env-All

    Security Controls

    IDTypeSecurity Control Description
    1Detective
    Monitor system logs and alert on unusual activity. Most shell code and unusual activity appears in logs.
+ Typical Severity

Medium

+ Typical Likelihood of Exploit

Likelihood: High

+ Methods of Attack
  • Analysis
  • Injection
  • Brute Force
+ Examples-Instances

Description

A fuzz test reveals that when data length for a particular field exceeds certain length, the input validation filter fails and lets the user data in unfiltered. This provides an attacker with an injection vector to deliver the malicious payload into the system.

+ Attacker Skills or Knowledge Required

Skill or Knowledge Level: Low

There is a wide variety of fuzzing tools available.

+ Resources Required

Fuzzing tools.

+ Indicators-Warnings of Attack

A lot of invalid data is fed to the system. Data that cannot have been generated through a legitimate transaction/request. Data is coming into the system within a short period of time and potentially from the same IP.

+ Obfuscation Techniques

Take pauses between fuzzing attempts (may not be very practical). Spoof IP addresses so that it does not look like all data is coming from the same source.

+ Solutions and Mitigations

Test to ensure that the software behaves as per specification and that there are no unintended side effects. Ensure that no assumptions about the validity of data are made.

Use fuzz testing during the software QA process to uncover any surprises, uncover any assumptions or unexpected behavior.

+ Attack Motivation-Consequences
ScopeTechnical ImpactNote
Integrity
Modify memory
Availability
Unexpected State
Confidentiality
Read application data
Confidentiality
Access_Control
Authorization
Gain privileges / assume identity
Confidentiality
Integrity
Availability
Alter execution logic
+ Purposes
  • Reconnaissance
+ CIA Impact
Confidentiality Impact: MediumIntegrity Impact: MediumAvailability Impact: Medium
+ Technical Context
Architectural Paradigms
All
Frameworks
All
Platforms
All
Languages
All
+ Content History
Submissions
SubmitterOrganizationDateSource
CAPEC Content TeamThe MITRE Corporation2014-06-23Internal_CAPEC_Team

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Page Last Updated: December 04, 2014