hank-ai/darknet sizes a convolutional layer's weight and output heap buffers by multiplying configuration fields taken from a .cfg file in unchecked 32-bit int arithmetic. In src-lib/convolutional_layer.cpp, l.nweights is computed as (c / groups) * n * size * size and l.outputs as l.out_h * l.out_w * l.out_c, and both feed xcalloc directly. A .cfg whose true dimension product exceeds INT_MAX wraps to a small or zero value, so the allocation is undersized; for example width and height of 256 with filters of 65536 gives 2^32, which wraps to 0. forward_convolutional_layer then re-derives the GEMM dimensions with a different operand order, computing k as l.size*l.size*l.c / l.groups where the allocation divided before multiplying, and reads and writes through the undersized buffer. Loading the crafted .cfg for inference or training is sufficient and no valid .weights file is required. The reported proof of concept observed a heap buffer overflow read in gemm_nn_fast under AddressSanitizer and glibc allocator metadata corruption in a release build of the same input, indicating an out-of-bounds write.
Metrics
Affected Vendors & Products
No data.
No data.
No data.
No data.
Advisories
No advisories yet.
Fixes
Solution
No solution given by the vendor.
Workaround
No workaround given by the vendor.
References
History
Thu, 20 Aug 2026 18:45:00 +0000
| Type | Values Removed | Values Added |
|---|---|---|
| Description | hank-ai/darknet sizes a convolutional layer's weight and output heap buffers by multiplying configuration fields taken from a .cfg file in unchecked 32-bit int arithmetic. In src-lib/convolutional_layer.cpp, l.nweights is computed as (c / groups) * n * size * size and l.outputs as l.out_h * l.out_w * l.out_c, and both feed xcalloc directly. A .cfg whose true dimension product exceeds INT_MAX wraps to a small or zero value, so the allocation is undersized; for example width and height of 256 with filters of 65536 gives 2^32, which wraps to 0. forward_convolutional_layer then re-derives the GEMM dimensions with a different operand order, computing k as l.size*l.size*l.c / l.groups where the allocation divided before multiplying, and reads and writes through the undersized buffer. Loading the crafted .cfg for inference or training is sufficient and no valid .weights file is required. The reported proof of concept observed a heap buffer overflow read in gemm_nn_fast under AddressSanitizer and glibc allocator metadata corruption in a release build of the same input, indicating an out-of-bounds write. | |
| Title | darknet Integer Overflow in Convolutional Layer Buffer Sizing Leads to Heap Buffer Overflow | |
| Weaknesses | CWE-190 CWE-787 |
|
| References |
|
|
| Metrics |
cvssV3_1
|
Projects
Sign in to view the affected projects.
MITRE
Status: PUBLISHED
Assigner: VulnCheck
Published:
Updated: 2026-08-20T18:19:23.554Z
Reserved: 2026-08-10T15:16:31.371Z
Link: CVE-2026-72852
Vulnrichment
No data.
NVD
Status : Received
Published: 2026-08-20T19:17:00.830
Modified: 2026-08-20T19:17:00.830
Link: CVE-2026-72852
Redhat
No data.
OpenCVE Enrichment
No data.