CWE-125 Detail

The product reads data past the end, or before the beginning, of the intended buffer.

Modes Of Introduction

Implementation

Applicable Platforms

Language

Name: C (Undetermined)
Name: C++ (Undetermined)

Technologies

Class: ICS/OT (Often)

Common Consequences

Observed Examples

Potential Mitigations

Phases : Implementation

Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.

When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue."

Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.

To reduce the likelihood of introducing an out-of-bounds read, ensure that you validate and ensure correct calculations for any length argument, buffer size calculation, or offset. Be especially careful of relying on a sentinel (i.e. special character such as NUL) in untrusted inputs.

Phases : Architecture and Design
Use a language that provides appropriate memory abstractions.

Detection Methods

Fuzzing

Fuzz testing (fuzzing) is a powerful technique for generating large numbers of diverse inputs - either randomly or algorithmically - and dynamically invoking the code with those inputs. Even with random inputs, it is often capable of generating unexpected results such as crashes, memory corruption, or resource consumption. Fuzzing effectively produces repeatable test cases that clearly indicate bugs, which helps developers to diagnose the issues.
Effectiveness : High

Automated Static Analysis

Automated static analysis, commonly referred to as Static Application Security Testing (SAST), can find some instances of this weakness by analyzing source code (or binary/compiled code) without having to execute it. Typically, this is done by building a model of data flow and control flow, then searching for potentially-vulnerable patterns that connect "sources" (origins of input) with "sinks" (destinations where the data interacts with external components, a lower layer such as the OS, etc.)
Effectiveness : High

Vulnerability Mapping Notes

Justification : This CWE entry is at the Base level of abstraction, which is a preferred level of abstraction for mapping to the root causes of vulnerabilities.
Comment : Carefully read both the name and description to ensure that this mapping is an appropriate fit. Do not try to 'force' a mapping to a lower-level Base/Variant simply to comply with this preferred level of abstraction.

Related Attack Patterns

References

REF-1034

Breaking the memory secrecy assumption
Raoul Strackx, Yves Younan, Pieter Philippaerts, Frank Piessens, Sven Lachmund, Thomas Walter.
https://dl.acm.org/doi/10.1145/1519144.1519145

REF-1035

The info leak era on software exploitation
Fermin J. Serna.
https://media.blackhat.com/bh-us-12/Briefings/Serna/BH_US_12_Serna_Leak_Era_Slides.pdf

REF-44

24 Deadly Sins of Software Security
Michael Howard, David LeBlanc, John Viega.

Submission

Modifications