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extreme-dim-tensor.gguf
Format: GGUF  ·  Size: 5.2 KB  ·  Scanned: 2026-08-10  ·  2 ms
SHA256: f5ac9723401ca961624fd671b51fd31c39bbd62c…

Security grade

C
Score: 64/100
2 HIGH 2 LOW
Artifact Security
C / 64
Repository Trust
N/A
local upload
Deployment Confidence
Low
Artifact Security grades the file. Repository Trust is unavailable for local uploads — no external provenance to verify. Deployment Confidence reflects artifact grade only.
▶ Score breakdown
Base score 100
HIGH Tensors with extreme dimension values (1 tensors) -15
HIGH Implausible compression ratio: 0.000 bits/param -15
LOW No license field in GGUF metadata (general.license) -3
LOW No embedded provenance metadata -3
Penalty subtotal 64/100
Final C / 64
GGUF v3 model | architecture: llama | quantization: F32 | 9 tensors | 2 blocking issues / 2 warnings

Runtime compatibility

Runtime Status Quant Architecture Context
llama.cpp ✓ ✓ ✓ —
Ollama ✓ ✓ ✓ —
LM Studio ✓ ✓ ✓ —
vLLM ✗ N/A GGUF not supported — requires safetensors format
HuggingFace Transformers ✗ N/A GGUF not directly supported — use safetensors or PyTorch checkpoint

Model info

Architecture
Neural network family — determines which runtime can load this model
llama
Namellmscan-labs-toy
Quantization
Weight storage format derived from actual tensor dtypes; lower bits = smaller file and faster inference at the cost of accuracy
 F32
Parameters ~
Estimated total number of weight values; determines VRAM needed at inference
16,000,000,840
Context length
Maximum tokens the model can process in a single prompt+response; affects KV-cache memory
128 tokens
Embedding dim
Hidden state dimension (d_model); larger = more expressive but more compute per token
8
FFN dim
Feed-forward network intermediate size; typically 2.7–4× embedding_length
16
Layers
Number of transformer blocks (depth); more layers = more reasoning capacity
1
Attention heads (Q)
Number of query heads; GQA/MQA models use fewer KV heads than Q heads
2
KV heads
Key/Value heads per layer; fewer than Q heads = GQA; list = per-layer (hybrid architecture)
 2
Vocab size
Number of unique tokens the tokenizer knows; affects embedding table size
32
Tokenizer
Tokenizer algorithm family (e.g. gpt2 = BPE, llama = SentencePiece)
llama
Chat template
Jinja2 prompt template embedded in model; controls how messages are formatted for inference
✗ No
Tensors
Total number of weight tensors stored in the file
9
File size
Size on disk in megabytes
0.01 MB

Security findings (4)

HIGH Tensor Integrity
Tensors with extreme dimension values (1 tensors)
Examples: [('blk.0.attn_k.weight', 2000000000)]
→ Extreme dimension values can cause integer overflow in element-count calculations, enabling heap OOB reads
HIGH Resource Limits
Implausible compression ratio: 0.000 bits/param
File size 5,312 B for ~16,000,000,840 parameters implies <0.5 bpp, which is physically impossible for neural weights
→ Tensor count or dimensions are likely inflated; this model may be designed to exhaust RAM when loaded
LOW Supply Chain
No license field in GGUF metadata (general.license)
Absence of license information makes it impossible to determine usage rights
→ Add general.license to model metadata before distributing; use an SPDX identifier
LOW Supply Chain
No embedded provenance metadata
None of general.author, general.source.url, general.base_model.0, or general.source.huggingface.repository are embedded in the GGUF file. Without any origin signal the model cannot be traced to a trusted source or compared against a reference checkpoint for tampering detection. For files uploaded directly, provenance is especially critical because the scanner cannot infer repository origin from the file alone.
→ Add at least one of: general.author, general.source.huggingface.repository, or general.source.url

Integrity & Structure

SHA-256f5ac9723401ca961624fd671b51fd31c39bbd62c43929e7061f06bad9fc82791
SHA-51271dfeb9aabc856e08d94c9089f2ecea33b2756337b9d48ad31e636cfbe6346a348dca27e449ce336…
File size (bytes)5,312
Header + KV + tensor directory1,696 bytes (0.00 MB)
Tensor data region3,616 bytes (0.00 MB)
Trailing bytes0
Unaccounted bytes0 ✓
KV metadata keys14
Tensors parsed9
Offsets monotone✓ OK
Max tensor offset3,104

DType Mix

DTypeCountTotal bytesTotal MBAvg bits/elem
F32 9 3,616 0.0 2e-06

Tensor Preview (first 9)

#NameDimsDTypeOffsetBytesbits/elem
0 token_embd.weight [8, 32] F32 0 1,024 32.0
1 output_norm.weight [8] F32 1,024 32 32.0
2 blk.0.attn_q.weight [8, 8] F32 1,056 256 32.0
3 blk.0.attn_k.weight [2000000000, 8] F32 1,312 256 0.0
4 blk.0.attn_v.weight [8, 8] F32 1,568 256 32.0
5 blk.0.attn_output.weight [8, 8] F32 1,824 256 32.0
6 blk.0.ffn_gate.weight [8, 16] F32 2,080 512 32.0
7 blk.0.ffn_down.weight [16, 8] F32 2,592 512 32.0
8 blk.0.ffn_up.weight [8, 16] F32 3,104 512 32.0

Scan coverage

Checks performed
  • ✓Magic bytes &amp; GGUF version validation
  • ✓Header parse (tensor count, KV count)
  • ✓KV metadata parse — all key/value pairs
  • ✓Tensor directory parse (names, dims, dtype, offsets)
  • ✓Tensor offset monotonicity
  • ✓Tensor overlap detection
  • ✓Tensor out-of-bounds detection
  • ✓Data section alignment verification
  • ✓Trailing bytes after last tensor
  • ✓Unaccounted bytes (header vs file size)
  • ✓Dtype histogram &amp; quantization mix analysis
  • ✓Embedding tensor size cross-validation
  • ✓Chat template static scan (Jinja2/SSTI patterns)
  • ✓Runtime compatibility heuristics
  • —External model card / repository context: not available for local upload
  • —Upstream repository: not checked — not available for direct file upload
Not covered
  • ✗Runtime execution or dynamic analysis
  • ✗Behavioral backdoor detection
  • ✗Weight-level semantic backdoor detection
  • ✗Full upstream repository verification
  • ✗License validation beyond embedded metadata
  • ✗Malware scanning of surrounding repo files
  • ✗Adversarial robustness or alignment audit
  • ✗Training data provenance
ⓘ Static scanning can detect structural anomalies and known malicious patterns. It cannot guarantee absence of risk. Treat results as a security signal, not a formal audit.
Raw scan data (.llmscan JSON) 
{
  "compatibility": [
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      "arch": true,
      "container": true,
      "context": null,
      "notes": "GGUF v3 container: supported | quant F32: supported",
      "quant": true,
      "runtime": "llama.cpp",
      "supported": true
    },
    {
      "arch": true,
      "container": true,
      "context": null,
      "notes": "GGUF via llama.cpp backend: container and quantization supported",
      "quant": true,
      "runtime": "Ollama",
      "supported": true
    },
    {
      "arch": true,
      "container": true,
      "context": null,
      "notes": "GGUF container and quantization supported; GPU/CPU inference",
      "quant": true,
      "runtime": "LM Studio",
      "supported": true
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    {
      "arch": null,
      "container": false,
      "context": null,
      "not_supported_msg": "GGUF not supported \u2014 requires safetensors format",
      "notes": "GGUF not supported; requires safetensors format",
      "quant": false,
      "runtime": "vLLM",
      "supported": false
    },
    {
      "arch": null,
      "container": false,
      "context": null,
      "not_supported_msg": "GGUF not directly supported \u2014 use safetensors or PyTorch checkpoint",
      "notes": "GGUF not supported; requires safetensors or PyTorch checkpoint",
      "quant": false,
      "runtime": "HuggingFace Transformers",
      "supported": false
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  "format": "LLM-SCAN",
  "format_version": "1.0",
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        "llama.block_count",
        "llama.context_length",
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      "license": null,
      "magic": "GGUF",
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    ],
    "trailing_bytes": 0,
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    "values_redacted": true,
    "vocab_size": 32
  },
  "report": {
    "bullets": [
      "GGUF v3 model | architecture: llama | quantization: F32 | 9 tensors | 2 blocking issues / 2 warnings",
      "Size: 0.01 MB",
      "SHA256: f5ac9723401ca961..."
    ],
    "recommendations": [
      "Extreme dimension values can cause integer overflow in element-count calculations, enabling heap OOB reads",
      "Tensor count or dimensions are likely inflated; this model may be designed to exhaust RAM when loaded",
      "Add general.license to model metadata before distributing; use an SPDX identifier",
      "Add at least one of: general.author, general.source.huggingface.repository, or general.source.url"
    ],
    "summary": "GGUF v3 model | architecture: llama | quantization: F32 | 9 tensors | 2 blocking issues / 2 warnings"
  },
  "scan_context": {
    "duration_ms": 2,
    "ended_at": "2026-08-10T19:43:42+00:00Z",
    "limits": {
      "sandbox": true,
      "timeout_ms": 60000
    },
    "mode": "static-deep",
    "started_at": "2026-08-10T19:43:42+00:00Z"
  },
  "security": {
    "findings": [
      {
        "category": "Tensor Integrity",
        "detail": "Examples: [(\u0027blk.0.attn_k.weight\u0027, 2000000000)]",
        "recommendation": "Extreme dimension values can cause integer overflow in element-count calculations, enabling heap OOB reads",
        "severity": "HIGH",
        "title": "Tensors with extreme dimension values (1 tensors)"
      },
      {
        "category": "Resource Limits",
        "detail": "File size 5,312 B for ~16,000,000,840 parameters implies \u003c0.5 bpp, which is physically impossible for neural weights",
        "recommendation": "Tensor count or dimensions are likely inflated; this model may be designed to exhaust RAM when loaded",
        "severity": "HIGH",
        "title": "Implausible compression ratio: 0.000 bits/param"
      },
      {
        "category": "Supply Chain",
        "detail": "Absence of license information makes it impossible to determine usage rights",
        "recommendation": "Add general.license to model metadata before distributing; use an SPDX identifier",
        "severity": "LOW",
        "title": "No license field in GGUF metadata (general.license)"
      },
      {
        "category": "Supply Chain",
        "detail": "None of general.author, general.source.url, general.base_model.0, or general.source.huggingface.repository are embedded in the GGUF file. Without any origin signal the model cannot be traced to a trusted source or compared against a reference checkpoint for tampering detection. For files uploaded directly, provenance is especially critical because the scanner cannot infer repository origin from the file alone.",
        "recommendation": "Add at least one of: general.author, general.source.huggingface.repository, or general.source.url",
        "severity": "LOW",
        "title": "No embedded provenance metadata"
      }
    ],
    "grade": "C",
    "score": 64
  },
  "source": {
    "filename": "extreme-dim-tensor.gguf",
    "hashes": {
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    },
    "input_type": "file",
    "size_bytes": 5312
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    "dtype_histogram": {
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    "dtype_stats": {
      "F32": {
        "avg_bits_per_elem": 2e-06,
        "bytes": 3616,
        "count": 9,
        "mb": 0.0
      }
    },
    "integrity": {
      "header_kv_dir_bytes": 1696,
      "misaligned_tensors": 0,
      "offsets_monotone": true,
      "oob_tensor_count": 0,
      "overlapping_tensor_pairs": 0,
      "tensor_bytes_sum": 3616,
      "tensor_delta_bytes": 1696,
      "tensor_delta_mb": 0.0,
      "trailing_bytes": 0,
      "unaccounted_bytes": 0
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    "preview": [
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        "name": "blk.0.ffn_up.weight",
        "offset": 3104
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    ]
  },
  "tokenizer": {
    "bos_token_id": null,
    "chat_template_chars": null,
    "eos_token_id": null,
    "has_chat_template": false,
    "type": "llama",
    "vocab_size": 32
  }
}