CWE-805: Buffer Access with Incorrect Length Value
low-riskThe product uses a sequential operation to read or write a buffer, but it uses an incorrect length value that causes it to access memory that is outside of the bounds of the buffer.
Common Consequences
Detection Methods
This weakness can often be detected using automated static analysis tools. Many modern tools use data flow analysis or constraint-based techniques to minimize the number of false positives. Automated static analysis generally does not account for environmental considerations when reporting out-of-bounds memory operations. This can make it difficult for users to determine which warnings should be investigated first. For example, an analysis tool might report buffer overflows that originate from command line arguments in a program that is not expected to run with setuid or other special privileges.
This weakness can be detected using dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
Manual analysis can be useful for finding this weakness, but it might not achieve desired code coverage within limited time constraints. This becomes difficult for weaknesses that must be considered for all inputs, since the attack surface can be too large.
Use tools that are integrated during compilation to insert runtime error-checking mechanisms related to memory safety errors, such as AddressSanitizer (ASan) for C/C++ [REF-1518].
Real-World Examples (10)
| CVE | CVSS | EPSS | KEV |
|---|---|---|---|
| CVE-2023-20049 | 8.6 | 1.1% | — |
| CVE-2025-23319 | 8.1 | 0.9% | — |
| CVE-2023-5396 | 7.4 | 0.9% | — |
| CVE-2025-20169 | 7.7 | 0.5% | — |
| CVE-2025-20170 | 7.7 | 0.5% | — |
| CVE-2025-20175 | 7.7 | 0.5% | — |
| CVE-2025-20174 | 7.7 | 0.5% | — |
| CVE-2024-24851 | 7.5 | 0.5% | — |
| CVE-2025-30651 | 7.5 | 0.4% | — |
| CVE-2020-16101 | 7.5 | 0.4% | — |