encoder-model.int8.onnx
Format: LLM-SCAN  ·  Size: 622.0 MB  ·  Scanned: 2026-08-05  ·  3549 ms
SHA256: 6139d2fa7e1b086097b277c7149725edbab89cc7…

Security grade

B
Score: 91/100
3 LOW 5 INFO
Artifact Security
B / 91
Repository Trust
N/A
local upload
Deployment Confidence
Medium
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
LOW No license field in ONNX model metadata -3
LOW No provenance metadata in ONNX model -3
LOW Fully dynamic input shapes (1 input(s)) -3
Penalty subtotal 91/100
Final B / 91
ONNX audio-speech model | opset 17 | 5654 nodes | 608,855,720 params | 3 warnings / 5 informational

Runtime compatibility

Runtime Status Quant Architecture Context
ONNX Runtime ✓
Direct
 ? ✓ opset 17 (requires ORT ≥ 1.14)
TensorRT
Verify arch support
 ⚡
Via conversion
verify required
 ? ?
verify
 opset 17; dynamic batch: needs profiles
OpenVINO
Verify arch support
 ⚡
Via conversion
verify required
 ? ?
verify
 opset 17; conversion required
CoreML (Apple)
Verify arch support
 ✗
Not possible
 ? ?
verify
 opset 17; conversion via coremltools; opset out of range
llama.cpp / Ollama ✗ N/A ONNX not supported — requires GGUF format

Model info

Parameters ~
Estimated total number of weight values; determines VRAM needed at inference
608,855,720
Tensors
Total number of weight tensors stored in the file
1229
File size
Size on disk in megabytes
621.97 MB
Graph nodes
Total number of computational nodes in the ONNX graph
5654
Opsets
ONNX operator set versions declared by the model
{"ai.onnx": 17}
Producer
Tool or framework that exported this ONNX model
onnx.quantize

Security findings (8)

LOW Supply Chain
No license field in ONNX model metadata
Models without license information cannot be safely redistributed or deployed commercially. ONNX metadata supports a license property in model.metadata_props.
→ Add a license field during export (e.g. apache-2.0, mit, cc-by-4.0)
LOW Supply Chain
No provenance metadata in ONNX model
Neither author nor domain (producer namespace) are set. Without origin information the model cannot be traced to a trusted source.
→ Set author and domain fields during model export
LOW Runtime Safety
Fully dynamic input shapes (1 input(s))
Inputs with all-dynamic dimensions: [('length', "['length_dynamic_axes_1']")]. Without static bounds, a malicious caller can supply arbitrarily large tensors causing OOM. The model itself is not malicious, but deployments must enforce input size limits.
→ Enforce maximum input shape at the serving layer (e.g. ORT session options, preprocessing validation). Document accepted input bounds in model card.
INFO Shape Risk
Dynamic tensor dimensions detected
Inputs with dynamic dims: ['audio_signal', 'length']. Outputs: ['outputs', 'encoded_lengths'] (symbolic: Transposeoutputs_dim_0, Transposeoutputs_dim_2, audio_signal_dynamic_axes_1, audio_signal_dynamic_axes_2). Dynamic dims allow flexible batch sizes and sequence lengths, but without enforced bounds a caller can supply arbitrarily large tensors causing OOM. This is expected for most inference models.
→ Enforce maximum input shape at the serving layer (e.g. ORT SessionOptions.add_session_config_entry, preprocessing validation). Document accepted input bounds in model card or deployment README.
INFO Resources
Large ONNX graph — 5,654 graph nodes / ~609M parameters
This model has 5,654 nodes, 1229 initializers, 608,855,720 parameters, and 609.8 MB of declared weights. Larger graphs have higher memory allocation overhead at load time and may hit per-request limits in serving frameworks.
→ Validate memory and latency under representative maximum input length. Set request concurrency and input-size limits before exposing the model as a service.
INFO Provenance
Model producer: onnx.quantize 0.1.0
Producer metadata helps identify the tool that exported this model
→ Verify producer matches expected export pipeline
INFO Classification
Inferred model type: audio-speech
Based on op inventory (5654 total nodes, 34 unique types)
→ Verify this matches the model's intended use case
INFO Runtime
Default opset: 17
Opset governs which ops and behaviors are available at runtime
→ Use ONNX Runtime (CPU/CUDA/DirectML/OpenVINO backends); requires ORT ≥ 1.14

Integrity & Structure

SHA-2566139d2fa7e1b086097b277c7149725edbab89cc7c7ae64b23c741be4055aff09
SHA-51290d6b7163914efd49ad006071282404b788911b2b26f8a5fe70407f193bbce2194a90a92c9f63cec…
File size652,183,999 bytes (621.97 MB)
Declared weight bytes609,822,622 (581.57 MB)
Non-weight overhead40.4 MB
Initializers (weights)1229
Graph nodes5654

Custom Operators & Domains

✓
Standard ops only
All operators use the standard ai.onnx or ai.onnx.ml domain. No custom kernels, no vendor-specific ops.
DomainTypeOpsRuntime requirement
ai.onnx Standard — None — supported by all ONNX runtimes

Initializer Integrity

✓ All initializer integrity checks passed.
CheckCountStatus
Inline tensor byte-size mismatches
raw_data length vs declared shape × dtype_bytes
 0 ✓ PASS
Empty initializers
Non-zero shape declared but no inline or external data
 0 ✓ PASS
Duplicate initializer names
ONNX spec requires unique names; duplicates cause non-deterministic behaviour
 0 ✓ PASS
Initializers not used by graph
Present in graph.initializer but not referenced by any node input
 0 ✓ PASS
Graph inputs shadowing initializers
Per ONNX spec these become optional overridable weights; runtime behaviour varies
 0 ✓ PASS
NaN / Inf numeric scan
Full scan — 321,792 values across 318 / 318 float tensor(s)
0 ✓ PASS

Operator Risk Profile

⚠ Elevated-risk operator categories detected — review summary below before deployment.
Risk categoryOperators in this model
▪ Control-flow ops
Loop, If, Scan — conditional/recursive execution
 none
⚠ Memory amplification
ConstantOfShape, Expand, Tile — size from runtime shapes
 ConstantOfShape ×51   Expand ×2   Tile ×1
▪ Provider compat
NMS, RoiAlign, GridSample — not in all runtimes
 none
⚠ Runtime-sensitive ops
Scatter, Gather, Resize — edge-case differences
 Gather ×149
⚠ Quantized ops
QuantizeLinear, QLinearConv — quantized path required
 MatMulInteger ×217   ConvInteger ×77
▪ Non-deterministic ops
Random*, Multinomial — output varies between runs
 none
▪ String processing ops
Tokenizer, TfIdfVectorizer — unusual in weight models
 none
▪ Custom ops / domains
Non-standard op domains — may execute arbitrary native code
 none
All operators (34 unique types)
OpCountRisk
ConstantOfShape 51 Memory amplification — allocates tensor of declared shape
Expand 2 Memory amplification — broadcasts to declared shape
Tile 1 Memory amplification — replicates input N times
Gather 149 Runtime-sensitive — index-based gather
MatMulInteger 217 Quantized model — integer matmul
ConvInteger 77 Quantized model — integer convolution
Constant 1661 —
Mul 733 —
Cast 426 —
Unsqueeze 328 —
Reshape 295 —
Transpose 244 —
DynamicQuantizeLinear 223 —
Concat 221 —
Add 205 —
Shape 175 —
Slice 123 —
LayerNormalization 120 —
Sigmoid 96 —
Where 73 —
MatMul 72 —
Pad 48 —
Div 27 —
Squeeze 24 —
Softmax 24 —
Split 24 —
Floor 3 —
Relu 3 —
Sub 2 —
Not 2 —
And 2 —
Equal 1 —
Range 1 —
Less 1 —

Shape Risk Analysis

ⓘ Dynamic dims present — standard for flexible inference models. Enforce bounds at serving layer.
Inputs
NameDtypeShapeEst. elements
audio_signal FLOAT [audio_signal_dynamic_axes_1, 128, audio_signal_dynamic_axes_2] 128
length INT64 [length_dynamic_axes_1] 1
Outputs
NameDtypeShapeEst. elements
outputs FLOAT [Transposeoutputs_dim_0, 1024, Transposeoutputs_dim_2] 1,024
encoded_lengths INT64 [length_dynamic_axes_1] 1
Dynamic dims
Yes
Transposeoutputs_dim_0, Transposeoutputs_dim_2, audio_signal_dynamic_axes_1, audio_signal_dynamic_axes_2
Output/Input ratio
7.95×
(dynamic dims assumed = 1)
Shape anomalies
None

External Data Security Checks

No external data — all weight tensors are stored inline in the .onnx file. Checks below are N/A.
CheckResult
External data present
Whether any weight tensors are stored outside the .onnx file
 ✓
PASS
Absolute paths
Paths starting with / or C:\ escape the model directory
 N/A
Path traversal (../)
.. sequences that could read files outside the model directory
 N/A
Windows drive paths (C:\)
Drive-letter paths are always absolute and OS-specific
 N/A
Remote / URL paths
http://, ftp:// etc. would trigger network requests at load time
 N/A
Null bytes in paths
Null-byte injection truncates paths in C/C++ runtimes
 N/A
Duplicate file references
Same external file referenced by multiple tensors
 N/A
Missing external files
Referenced files not present on disk at scan time
 N/A
Out-of-bounds reads
offset + length exceeds the external file size
 N/A
Offset/length range validated
Verified that each tensor's offset+length fits within the external file
N/A

DType Mix

DTypeCountTotal bytesTotal MBAvg bits/elem
FLOAT 612 1,288,344 1.23 32.0
INT64 29 736 0.0 64.0
UINT8 588 608,533,542 580.34 8.0

Op Distribution (top 20)

Initializer Preview (first 50 of 1229)

#NameShapeDTypeBytesbits/elem
0 pre_encode.out.bias [1024] FLOAT 4,096 32.0
1 pre_encode.conv.0.bias [256] FLOAT 1,024 32.0
2 pre_encode.conv.2.bias [256] FLOAT 1,024 32.0
3 pre_encode.conv.3.bias [256] FLOAT 1,024 32.0
4 pre_encode.conv.5.bias [256] FLOAT 1,024 32.0
5 pre_encode.conv.6.bias [256] FLOAT 1,024 32.0
6 layers.0.norm_feed_forward1.weight [1024] FLOAT 4,096 32.0
7 layers.0.norm_feed_forward1.bias [1024] FLOAT 4,096 32.0
8 layers.0.norm_conv.weight [1024] FLOAT 4,096 32.0
9 layers.0.norm_conv.bias [1024] FLOAT 4,096 32.0
10 layers.0.norm_self_att.weight [1024] FLOAT 4,096 32.0
11 layers.0.norm_self_att.bias [1024] FLOAT 4,096 32.0
12 layers.0.self_attn.pos_bias_u [8, 128] FLOAT 4,096 32.0
13 layers.0.self_attn.pos_bias_v [8, 128] FLOAT 4,096 32.0
14 layers.0.norm_feed_forward2.weight [1024] FLOAT 4,096 32.0
15 layers.0.norm_feed_forward2.bias [1024] FLOAT 4,096 32.0
16 layers.0.norm_out.weight [1024] FLOAT 4,096 32.0
17 layers.0.norm_out.bias [1024] FLOAT 4,096 32.0
18 layers.1.norm_feed_forward1.weight [1024] FLOAT 4,096 32.0
19 layers.1.norm_feed_forward1.bias [1024] FLOAT 4,096 32.0
20 layers.1.norm_conv.weight [1024] FLOAT 4,096 32.0
21 layers.1.norm_conv.bias [1024] FLOAT 4,096 32.0
22 layers.1.norm_self_att.weight [1024] FLOAT 4,096 32.0
23 layers.1.norm_self_att.bias [1024] FLOAT 4,096 32.0
24 layers.1.self_attn.pos_bias_u [8, 128] FLOAT 4,096 32.0
25 layers.1.self_attn.pos_bias_v [8, 128] FLOAT 4,096 32.0
26 layers.1.norm_feed_forward2.weight [1024] FLOAT 4,096 32.0
27 layers.1.norm_feed_forward2.bias [1024] FLOAT 4,096 32.0
28 layers.1.norm_out.weight [1024] FLOAT 4,096 32.0
29 layers.1.norm_out.bias [1024] FLOAT 4,096 32.0
30 layers.2.norm_feed_forward1.weight [1024] FLOAT 4,096 32.0
31 layers.2.norm_feed_forward1.bias [1024] FLOAT 4,096 32.0
32 layers.2.norm_conv.weight [1024] FLOAT 4,096 32.0
33 layers.2.norm_conv.bias [1024] FLOAT 4,096 32.0
34 layers.2.norm_self_att.weight [1024] FLOAT 4,096 32.0
35 layers.2.norm_self_att.bias [1024] FLOAT 4,096 32.0
36 layers.2.self_attn.pos_bias_u [8, 128] FLOAT 4,096 32.0
37 layers.2.self_attn.pos_bias_v [8, 128] FLOAT 4,096 32.0
38 layers.2.norm_feed_forward2.weight [1024] FLOAT 4,096 32.0
39 layers.2.norm_feed_forward2.bias [1024] FLOAT 4,096 32.0
40 layers.2.norm_out.weight [1024] FLOAT 4,096 32.0
41 layers.2.norm_out.bias [1024] FLOAT 4,096 32.0
42 layers.3.norm_feed_forward1.weight [1024] FLOAT 4,096 32.0
43 layers.3.norm_feed_forward1.bias [1024] FLOAT 4,096 32.0
44 layers.3.norm_conv.weight [1024] FLOAT 4,096 32.0
45 layers.3.norm_conv.bias [1024] FLOAT 4,096 32.0
46 layers.3.norm_self_att.weight [1024] FLOAT 4,096 32.0
47 layers.3.norm_self_att.bias [1024] FLOAT 4,096 32.0
48 layers.3.self_attn.pos_bias_u [8, 128] FLOAT 4,096 32.0
49 layers.3.self_attn.pos_bias_v [8, 128] FLOAT 4,096 32.0
… 1179 more initializers omitted

Scan coverage

Checks performed
  • ✓ONNX protobuf parse
  • ✓Opset version check
  • ✓Node count & op histogram
  • ✓Custom domain detection
  • ✓External data reference check
  • ✓Structural validation (ONNX checker)
  • ✓Metadata key scan
  • ✓Weight size estimate vs file size
  • ✓Embedded supply-chain metadata (license, author, provenance fields)
  • ✓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) 
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      "conversion_possible": true,
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      "notes": "Conversion via Model Optimizer (mo) or OpenVINO OVC (openvino.convert_model) required | Op coverage varies; validate with mo --input_model before deployment | dynamic shapes supported but may require --input re-specification",
      "quant": null,
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      "verification_required": true
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    "bullets": [
      "ONNX audio-speech model | opset 17 | 5654 nodes | 608,855,720 params | 3 warnings / 5 informational",
      "Size: 621.97 MB",
      "SHA256: 6139d2fa7e1b0860..."
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    "recommendations": [
      "Add a license field during export (e.g. apache-2.0, mit, cc-by-4.0)",
      "Set author and domain fields during model export",
      "Enforce maximum input shape at the serving layer (e.g. ORT session options, preprocessing validation). Document accepted input bounds in model card."
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    "summary": "ONNX audio-speech model | opset 17 | 5654 nodes | 608,855,720 params | 3 warnings / 5 informational"
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  "scan_context": {
    "duration_ms": 3549,
    "ended_at": "2026-08-05T12:17:14+00:00Z",
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        "category": "Shape Risk",
        "detail": "Inputs with dynamic dims: [\u0027audio_signal\u0027, \u0027length\u0027]. Outputs: [\u0027outputs\u0027, \u0027encoded_lengths\u0027] (symbolic: Transposeoutputs_dim_0, Transposeoutputs_dim_2, audio_signal_dynamic_axes_1, audio_signal_dynamic_axes_2). Dynamic dims allow flexible batch sizes and sequence lengths, but without enforced bounds a caller can supply arbitrarily large tensors causing OOM. This is expected for most inference models.",
        "recommendation": "Enforce maximum input shape at the serving layer (e.g. ORT SessionOptions.add_session_config_entry, preprocessing validation). Document accepted input bounds in model card or deployment README.",
        "severity": "INFO",
        "title": "Dynamic tensor dimensions detected"
      },
      {
        "category": "Supply Chain",
        "detail": "Models without license information cannot be safely redistributed or deployed commercially. ONNX metadata supports a license property in model.metadata_props.",
        "recommendation": "Add a license field during export (e.g. apache-2.0, mit, cc-by-4.0)",
        "severity": "LOW",
        "title": "No license field in ONNX model metadata"
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      {
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        "detail": "Neither author nor domain (producer namespace) are set. Without origin information the model cannot be traced to a trusted source.",
        "recommendation": "Set author and domain fields during model export",
        "severity": "LOW",
        "title": "No provenance metadata in ONNX model"
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        "recommendation": "Validate memory and latency under representative maximum input length. Set request concurrency and input-size limits before exposing the model as a service.",
        "severity": "INFO",
        "title": "Large ONNX graph \u2014 5,654 graph nodes / ~609M parameters"
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        "category": "Provenance",
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        "recommendation": "Verify producer matches expected export pipeline",
        "severity": "INFO",
        "title": "Model producer: onnx.quantize 0.1.0"
      },
      {
        "category": "Classification",
        "detail": "Based on op inventory (5654 total nodes, 34 unique types)",
        "recommendation": "Verify this matches the model\u0027s intended use case",
        "severity": "INFO",
        "title": "Inferred model type: audio-speech"
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        "category": "Runtime",
        "detail": "Opset governs which ops and behaviors are available at runtime",
        "recommendation": "Use ONNX Runtime (CPU/CUDA/DirectML/OpenVINO backends); requires ORT \u2265 1.14",
        "severity": "INFO",
        "title": "Default opset: 17"
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        "category": "Runtime Safety",
        "detail": "Inputs with all-dynamic dimensions: [(\u0027length\u0027, \"[\u0027length_dynamic_axes_1\u0027]\")]. Without static bounds, a malicious caller can supply arbitrarily large tensors causing OOM. The model itself is not malicious, but deployments must enforce input size limits.",
        "recommendation": "Enforce maximum input shape at the serving layer (e.g. ORT session options, preprocessing validation). Document accepted input bounds in model card.",
        "severity": "LOW",
        "title": "Fully dynamic input shapes (1 input(s))"
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