Raw vector
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:HSummary
CVE-2023-52497 is a high-severity Out-of-bounds Write (CWE-787) vulnerability in Linux Linux Kernel. Its CVSS base score is 7.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Privilege Escalation (T1068); ranked at the 20th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SA-15 (Development Process, Standards, and Tools) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-57122
Vulnerability Data
In the Linux kernel, the following vulnerability has been resolved: erofs: fix lz4 inplace decompression Currently EROFS can map another compressed buffer for inplace decompression, that was used to handle the cases that some pages of compressed data are actually…
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not in-place I/O. However, like most simple LZ77 algorithms, LZ4 expects the compressed data is arranged at the end of the decompressed buffer and it explicitly uses memmove() to handle overlapping: __________________________________________________________ |_ direction of decompression --> ____ |_ compressed data _| Although EROFS arranges compressed data like this, it typically maps two individual virtual buffers so the relative order is uncertain. Previously, it was hardly observed since LZ4 only uses memmove() for short overlapped literals and x86/arm64 memmove implementations seem to completely cover it up and they don't have this issue. Juhyung reported that EROFS data corruption can be found on a new Intel x86 processor. After some analysis, it seems that recent x86 processors with the new FSRM feature expose this issue with "rep movsb". Let's strictly use the decompressed buffer for lz4 inplace decompression for now. Later, as an useful improvement, we could try to tie up these two buffers together in the correct order.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation (including fuzzing and bounds checks) finds out-of-bounds write flaws before deployment.
Requiring documented secure-development standards and tools can mandate bounds-checked coding practices that avoid the weakness.
Input validation can structurally reject or sanitize data that would otherwise trigger an out-of-bounds write.
Memory-protection mechanisms limit the exploitability and blast radius of a successful out-of-bounds write.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure-development practices (static analysis, bounds checking, code review) are the primary means of preventing out-of-bounds writes.
Vulnerability scanning and recording can discover out-of-bounds write flaws so they can be remediated.
Patching or replacing vulnerable software directly eliminates known instances of this coding weakness.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing in development and acceptance can detect and prevent out-of-bounds write defects.
Secure development life cycle mandates practices that prevent out-of-bounds writes.
Application security requirements can specify bounds-checking and safe memory handling.
Secure architecture and engineering principles reduce the likelihood of buffer overflows.
Secure coding directly addresses out-of-bounds writes through language choice and coding standards.
Change management can enforce review gates that catch unsafe memory operations before deployment.