Instructions to use Hironabe333/gguf-tensor-type-enum-abort-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hironabe333/gguf-tensor-type-enum-abort-poc with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Hironabe333/gguf-tensor-type-enum-abort-poc",
	filename="baseline.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Hironabe333/gguf-tensor-type-enum-abort-poc with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Hironabe333/gguf-tensor-type-enum-abort-poc
# Run inference directly in the terminal:
llama cli -hf Hironabe333/gguf-tensor-type-enum-abort-poc

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Hironabe333/gguf-tensor-type-enum-abort-poc
# Run inference directly in the terminal:
llama cli -hf Hironabe333/gguf-tensor-type-enum-abort-poc

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Hironabe333/gguf-tensor-type-enum-abort-poc
# Run inference directly in the terminal:
./llama-cli -hf Hironabe333/gguf-tensor-type-enum-abort-poc

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Hironabe333/gguf-tensor-type-enum-abort-poc
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Hironabe333/gguf-tensor-type-enum-abort-poc

Use Docker

docker model run hf.co/Hironabe333/gguf-tensor-type-enum-abort-poc

LM Studio
Jan
Ollama
How to use Hironabe333/gguf-tensor-type-enum-abort-poc with Ollama:
```
ollama run hf.co/Hironabe333/gguf-tensor-type-enum-abort-poc
```

Unsloth Studio

How to use Hironabe333/gguf-tensor-type-enum-abort-poc with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Hironabe333/gguf-tensor-type-enum-abort-poc to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Hironabe333/gguf-tensor-type-enum-abort-poc to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Hironabe333/gguf-tensor-type-enum-abort-poc to start chatting

Atomic Chat new
Docker Model Runner
How to use Hironabe333/gguf-tensor-type-enum-abort-poc with Docker Model Runner:
```
docker model run hf.co/Hironabe333/gguf-tensor-type-enum-abort-poc
```

Lemonade

How to use Hironabe333/gguf-tensor-type-enum-abort-poc with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Hironabe333/gguf-tensor-type-enum-abort-poc

Run and chat with the model

lemonade run user.gguf-tensor-type-enum-abort-poc-{{QUANT_TAG}}

List all available models

lemonade list

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Check out the documentation for more information.

GGUF tensor info type field out-of-range SIGABRT — PoC

Finding: GGUF tensor info per-tensor type field (ggml_type enum) out-of-range causes C runtime SIGABRT while Python reader rejects safely
Round: ROUND_AI378/AI379
Target: GGUF (.gguf) — llama-cpp-python / llama.cpp (huntr MFV)
Affected version: llama-cpp-python 0.2.90 (llama.cpp commit 1d1ccce67613674c75c9c7e3fa4c1e24e428ba48)

1. Summary

A GGUF model file contains a tensor info section where each tensor entry includes a type field encoding a ggml_type enum value. In llama-cpp-python 0.2.90, the C runtime (gguf_init_from_file) reads and dispatches on this per-tensor type field without first validating that it falls within the valid enum range. Placing an out-of-range value (such as 255, 99, or 50) in the tensor type field causes the process to abort with SIGABRT (exit -6). The Python gguf library (v0.19.0) rejects the same files safely with a ValueError.

This finding is distinct from the previously submitted GGUF KV entry type enum issue (ROUND_AI377). That issue mutated the per-KV type field in the KV metadata body and crashed at gguf_init_from_file+0x3d8. This issue mutates the per-tensor type field in the tensor info section and crashes at gguf_init_from_file+0xfac.

2. Environment

Component	Version
OS	Linux aarch64 (Ubuntu 22.04)
Python	3.10.12
llama-cpp-python	0.2.90
llama.cpp commit	`1d1ccce67613674c75c9c7e3fa4c1e24e428ba48`
gguf (Python library)	0.19.0

3. Root Cause

The GGUF format's tensor info section contains one entry per tensor. Each entry has the structure:

name_len   (uint64_le)   — byte length of tensor name
name       (uint8[])     — tensor name bytes
n_dims     (uint32_le)   — number of dimensions
dims       (uint64_le × n_dims) — dimension sizes
type       (uint32_le)   — ggml_type enum value  ← VULNERABLE FIELD
offset     (uint64_le)   — data offset

The type field encodes the tensor's quantization format as a ggml_type enum. Valid values are defined by GGMLQuantizationType in the Python library. In llama-cpp-python 0.2.90, the C runtime reads this field and uses it in a dispatch operation without checking whether the value is within the valid enum range. Out-of-range values cause abort() to be called, producing SIGABRT (exit -6).

4. Affected Field

Property	Value
Field name	tensor info `type` (per-tensor)
Field type	uint32_le
Binary offset in PoC	457
Section	Tensor info section (after KV metadata body)
Enum class	`ggml_type` / `GGMLQuantizationType`
Crash offset	`gguf_init_from_file+0xfac`

5. Binary Layout

In the PoC GGUF file (512 bytes):

Offset  Size  Field
0       4     magic (GGUF)
4       4     version (3)
8       8     tensor_count
16      8     kv_count
24      …     KV metadata entries (not mutated)
412     …     Tensor info section begins
412     8     name_len = 17
420     17    name = "token_embd.weight"
437     4     n_dims = 2
441     16    dims
457     4     type ← MUTATED FIELD (baseline=0, mutants=255/99/50)
461     8     offset

6. Reproduction Steps

pip install llama-cpp-python==0.2.90 gguf==0.19.0
python reproduce.py

To inspect binary layout and confirm mutation scope:

python inspect_artifacts.py

7. Expected Results

baseline.gguf (type=0): C runtime exits 0 or raises non-crash Python exception; Python gguf reader returns OK
tensor_type_0xff.gguf (type=255): Python gguf reader raises ValueError: GGMLQuantizationType; C runtime exits cleanly
tensor_type_99.gguf (type=99): Python gguf reader raises ValueError; C runtime exits cleanly
tensor_type_50.gguf (type=50): Python gguf reader raises ValueError; C runtime exits cleanly

8. Observed Results

File	Python gguf 0.19.0	C runtime (3 runs)
baseline.gguf	OK (type=0)	exit 0, 0, 0
tensor_type_0xff.gguf	ValueError: GGMLQuantizationType	-6, -6, -6 (SIGABRT)
tensor_type_99.gguf	ValueError: GGMLQuantizationType	-6, -6, -6 (SIGABRT)
tensor_type_50.gguf	ValueError: GGMLQuantizationType	-6, -6, -6 (SIGABRT)

9/9 SIGABRT on mutant files.

9. Distinctness from Prior GGUF Findings

This finding is distinct from all prior GGUF submissions:

Dimension	This finding	ROUND_AI377 (KV type enum)	ROUND_AI371 (kv_count)
Mutated field	Per-tensor `type` in tensor info	Per-KV `type` in KV body	`kv_count` in header
Binary section	Tensor info section	KV metadata body	GGUF header
Enum class	`GGMLQuantizationType`	`GGUFValueType`	N/A (count field)
Crash offset	`+0xfac`	`+0x3d8`	`ggml.c:366`
Python exception	`ValueError: GGMLQuantizationType`	`ValueError: GGUFValueType`	`IndexError`
Required fix	Range-check tensor-info ggml_type	Range-check KV-entry gguf_type	Validate kv_count bounds

10. Non-Claims

This report does not claim arbitrary code execution (RCE/ACE)
This report does not claim memory corruption
This report does not claim data exfiltration
This report does not claim all GGUF consumers are affected
This report is version-scoped to llama-cpp-python 0.2.90 (tested behavior)
This report does not claim current/latest llama-cpp-python is affected
This report does not claim High, Critical, or specific CVSS scores

Impact is limited to deterministic process abort / denial of service on the confirmed version.

11. Recommendation

Add an explicit range check on the tensor info type field before dispatch:

if (info->type >= GGML_TYPE_COUNT) {
    GGML_LOG_ERROR("%s: invalid ggml_type %d for tensor '%s'\n",
                   __func__, info->type, info->name.data);
    ok = false;
    break;
}

This check is analogous to the fix recommended for the KV entry type field (ROUND_AI377) but applied to the tensor info section rather than the KV metadata body.

Files

File	Description
`baseline.gguf`	Baseline GGUF — tensor type=0 (valid)
`tensor_type_0xff.gguf`	Mutant — tensor type=255 (out-of-range)
`tensor_type_99.gguf`	Mutant — tensor type=99 (out-of-range)
`tensor_type_50.gguf`	Mutant — tensor type=50 (out-of-range)
`reproduce.py`	Demonstrates Python/C runtime differential
`inspect_artifacts.py`	Verifies mutation scope and binary offsets
`runtime_results.json`	Runtime evidence (9/9 SIGABRT)
`hash_matrix.json`	SHA256 + mutation metadata
`distinctness_matrix.json`	Distinctness vs 3 prior GGUF submissions
`route_framing.json`	Route/reward/non-claims metadata
`SHA256SUMS.txt`	File integrity checksums

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GGUF

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4 params

Architecture

llama

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