Proof-of-concept files for a huntr Model Format Vulnerability report against hank-ai/darknet (.cfg convolutional-layer parser).

evil.cfg is a 93-byte darknet config file declaring filters=700000000 in a [convolutional] section.

parse_convolutional_section() (src-lib/darknet_cfg.cpp:1415-1417) reads filters/size/groups as plain int with no upper bound. These flow directly into convolutional_layer.cpp:764: l.nweights = (c / groups) * n * size * size; (plain int32, no overflow guard), then convolutional_layer.cpp:781: l.weights = (float*)xcalloc(l.nweights, sizeof(float)); - no sanity-check before allocating. .cfg is parsed strictly before the .weights file is even opened (src-python/darknet.py -> load_network_custom -> parse_network_cfg_custom then load_weights), so a crafted .cfg file alone (no weights file needed) reaches this code path.

repro.cpp reproduces the exact arithmetic and allocation call in isolation (full libdarknet needs OpenCV/CUDA, not built here) - verified live on this host:

c=3, n(filters)=700000000, size=3, groups=1
l.nweights (int32, overflowed) = 1720130816   (true value 18,900,000,000 wraps)
calloc(1720130816, 4) -> NULL on this host -> darknet's calloc_error() ends in
an uncatchable std::abort()

Impact: Denial of Service - an attacker who can supply or influence a .cfg file (the standard first input, parsed before any weight data) can crash the loading process via abort().