| Attack Pattern ID | Pattern Abstraction: Standard 28 |
| Typical Severity | Medium |
| Description | 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 occured. Fuzzing treats the system as a blackbox 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 Observe communication and inputs: The fuzzing attacker observes the target system looking for inputs and communications between modules, subsystems, or systems. |
Attack Step Techniques |
|---|
| Description | Environments |
|---|
| 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 | | 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 | | Observe inputs using web inspection tools (OWASP's WebScarab, Paros, TamperData, TamperIE, etc.) | env-Web |
|
Outcome |
|---|
| ID | Type | Description |
|---|
| c28s1o1 | Success | The attacker creates a list of unique communications packets, messages, inputs, API calls or other actions the software takes. |
|
Security Controls |
|---|
| ID | Type | Description |
|---|
| c28s1s1 | Detective | 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. | | c28s1s2 | Preventative | Some production hardware (for embedded environments) can have debugging disabled on the hardware. | | c28s1s3 | Preventative | Techniques exist to insert no-ops and other null branches that thwart basic attempts to execute software stepwise in a debugger. |
Experiment 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 |
|---|
| Description | Environments |
|---|
| 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 | | 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 |
|---|
| ID | Type | Description |
|---|
| c28s2s1 | Detective | Log unexpected parameters to API calls or system calls. | | c28s2s2 | Preventative | Profile the software's expected use of system calls. Use a sandboxing technique to restrict its API calls to the expected patterns. | | c28s2s3 | Preventative | 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.. |
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 of Susceptibility
|
|---|
| ID | Type | Description | Environments |
|---|
| c28s3i1 | Positive | The software produces an indicator that the attacker can see (error message, altered error state in a protocol, etc.). | env-All | | c28s3i2 | Positive | The previous step led to plausible, practical fuzz inputs. | env-All |
|
Outcomes |
|---|
| ID | Type | Description |
|---|
| c28s3o1 | Success | If the software's indicators (error messages, etc.) vary clearly based on the attacker's input, then the attacker has a sufficient starting point for customizing his attack. | | c28s3o2 | Failure | The attacker is unable to induce unexpected failures or output based fuzzed inputs. |
Exploit 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 |
|---|
| Description | Environments |
|---|
| Identify and embed shellcode for the target system. | env-All | | Embed higher level attack commands in the payload. (e.g., SQL, PHP, server-side includes, etc.) | env-Web env-CommProtocol env-Peer2Peer env-ClientServer | | Induce denial of service by exploiting resource leaks or bad error handling. | env-All |
|
Security Control |
|---|
| ID | Type | Description |
|---|
| c28s4s1 | Detective | Monitor system logs and alert on unusual activity. Most shellcode and unusual activity appears in logs. |
|
| Typical Likelihood of Exploit |
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.
Related Vulnerability |
| Attacker Skill or Knowledge Required |
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 | - Data Modification
- Denial of Service
- Information Leakage
- Privilege Escalation
- Run Arbitrary Code
|
| Related Weaknesses | | CWE-ID | Weakness Name | Weakness Relationship Type |
|---|
| 74 | Failure to Sanitize Data into a Different Plane (aka 'Injection') | Targeted | | 388 | Error Handling | Targeted | | 20 | Insufficient Input Validation | Targeted |
|
| Purpose | Reconnaissance |
| CIA Impact | | Confidentiality Impact | Integrity Impact | Availability Impact |
|---|
| Medium | Medium | Medium |
|
| Technical Context | | Architectural Paradigm | Framework | Platform | Language |
|---|
| All | All | All | All |
|
| Source | | Submission(s) |
|---|
| Submitter | Organization | Date | Comment |
|---|
| Eugene Lebanidze | Cigital, Inc | 2007-02-26 | |
| Modification(s) |
|---|
| Modifier | Organization | Date | Comment |
|---|
| Sean Barnum | Cigital, Inc | 2007-03-01 | Review and revision of content |
|