Vulnerabilidades do Hank-ai Darknet
🔔 Criar alerta RSS Verificador de versão
CVEs publicadas que afetam Hank-ai Darknet: 2 registro(s), com versões afetadas e correções.
CVE-2026-81334
Média · 6.1
CWE-125 27/08/2026
darknet through 6.0 Out-of-Bounds Read and Write via Unchecked Layer Index in .cfg Parser
darknet subscripts its layer array with an index taken from a configuration file without checking it against the array's length. The array is allocated in src-lib/darknet_network.cpp as xcalloc(net.n, sizeof(Darknet::Layer)), sized to exactly the number of layer sections the file declares. The shortcut, scale_channels and sam sections supply that index through their from field and the route section through its layers field, and parse_shortcut_section in src-lib/darknet_cfg.cpp reads net.layers[index].outputs with no bounds check, which reads past the allocation. The dispatch loop in create_network then reuses the same index to assign net.layers[l.index].use_bin_output and net.layers[l.index].keep_delta_gpu, writing past the allocation at an offset the file controls, with a fixed one-byte value. Parsing a crafted configuration file is sufficient: the parse runs before any weights file is opened and needs no non-default option, so the result is a reliable crash and a write whose location, though not its value, is chosen by whoever supplied the file.
CVE-2026-72852
Alta · 7.8
CWE-190 20/08/2026
darknet Integer Overflow in Convolutional Layer Buffer Sizing Leads to Heap Buffer Overflow
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.