LFM2-350M-ENJP-MT-F16.gguf
Format: LLM-SCAN  ·  Size: 678.5 MB  ·  Scanned: 2026-07-09  ·  2467 ms
SHA256: 0ed20b653006d4e97dd2b4a22872971e45fa71ad…

Security grade

N/A
Score: 0/100
Artifact Security
N/A / 0
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.
GGUF file detected.

Runtime compatibility

Runtime Status Quant Architecture Context
llama.cpp ? ? ?
verify
 —
Ollama ? ? ?
verify
 —
LM Studio ? ? ?
verify
 —
vLLM ? ? ?
verify
 —

Model info

Scan coverage

Checks performed
  • ✓Magic bytes & 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 & 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": [
    {
      "notes": "GGUF v3 container supported; architecture compatibility depends on your runtime build",
      "runtime": "llama.cpp",
      "supported": true
    },
    {
      "notes": "GGUF supported via llama.cpp backend; verify architecture support for non-mainstream models",
      "runtime": "Ollama",
      "supported": true
    },
    {
      "notes": "GGUF supported; architecture compatibility depends on bundled llama.cpp version",
      "runtime": "LM Studio",
      "supported": true
    },
    {
      "notes": "Requires safetensors conversion",
      "runtime": "vLLM",
      "supported": false
    }
  ],
  "format": "LLM-SCAN",
  "format_version": "1.0",
  "generator": {
    "name": "llmscan-engine",
    "version": "1.1.0"
  },
  "model": {
    "arch": null,
    "format": "gguf",
    "gguf_version": 3,
    "kv_meta": {},
    "params_estimate": null,
    "quantization": null
  },
  "report": {
    "bullets": [
      "Size: 0.66 GB",
      "SHA256: 0ed20b653006d4e9\u2026"
    ],
    "recommendations": [],
    "summary": "GGUF file detected."
  },
  "scan_context": {
    "duration_ms": 2467,
    "ended_at": "2026-07-09T10:25:39+00:00Z",
    "limits": {
      "sandbox": true,
      "timeout_ms": 60000
    },
    "mode": "static-lite",
    "started_at": "2026-07-09T10:25:39+00:00Z"
  },
  "security": {
    "findings": [],
    "grade": "N/A"
  },
  "source": {
    "filename": "LFM2-350M-ENJP-MT-F16.gguf",
    "hashes": {
      "sha256": "0ed20b653006d4e97dd2b4a22872971e45fa71ad6ccb3cd21705747ee08d3b30"
    },
    "input_type": "file",
    "size_bytes": 711483168
  },
  "tensors": {},
  "tokenizer": {}
}