ModelAudit

Secure your AI models before deployment. Static scanner that detects malicious code, potential backdoor indicators, and security vulnerabilities in ML model files — without ever loading or executing them.

Full Documentation | Usage Examples | Supported Formats

Quick Start

Requires Python 3.10-3.13

pip install "modelaudit[all]"

# Scan a file or directory
modelaudit model.pkl
modelaudit ./models/

# Export results for CI/CD
modelaudit model.pkl --format json --output results.json

$ modelaudit suspicious_model.pkl

Files scanned: 1 | Issues found: 2 critical, 1 warning

1. suspicious_model.pkl (pos 28): [CRITICAL] Malicious code execution attempt
   Why: Contains os.system() call that could run arbitrary commands

2. suspicious_model.pkl (pos 52): [WARNING] Dangerous pickle deserialization
   Why: Could execute code when the model loads

What It Detects

• Code execution attacks in Pickle, PyTorch, NumPy, and Joblib files
• Potential backdoor indicators — suspicious weight patterns, anomalous tensors, or hidden-code signals
• Embedded secrets — API keys, tokens, and credentials in model weights or metadata
• Network indicators — URLs, IPs, and socket usage that could enable data exfiltration
• Archive exploits — path traversal, symlink attacks in ZIP/TAR/7z files
• Unsafe ML operations — Lambda layers, custom ops, TorchScript/JIT, template injection
• Supply chain risks — tampering, license violations, suspicious configurations

Supported Formats

ModelAudit includes 44 registered scanners covering model, archive, and configuration formats:

Format	Extensions	Risk
Pickle	.pkl, .pickle, .dill	HIGH
PyTorch	.pt, .pth, .ckpt, .bin	HIGH
Joblib	.joblib	HIGH
NumPy	.npy, .npz	HIGH
R Serialized	.rds, .rda, .rdata	HIGH
TensorFlow	.pb, .meta, SavedModel dirs	MEDIUM
Keras	.h5, .hdf5, .keras	MEDIUM
ONNX	.onnx	MEDIUM
CoreML	.mlmodel	LOW
MXNet	*-symbol.json, *-NNNN.params	LOW
NeMo	.nemo	MEDIUM
CNTK	.dnn, .cmf	MEDIUM
RKNN	.rknn	MEDIUM
Torch7	.t7, .th, .net	HIGH
CatBoost	.cbm	MEDIUM
XGBoost	.bst, .model, .json, .ubj	MEDIUM
LightGBM	.lgb, .lightgbm, .model	MEDIUM
Llamafile	.llamafile, extensionless, .exe	MEDIUM
TorchServe	.mar	HIGH
SafeTensors	.safetensors	LOW
GGUF/GGML	.gguf, .ggml, .ggmf, .ggjt, .ggla, .ggsa	LOW
JAX/Flax	.msgpack, .flax, .orbax, .jax, .checkpoint, .orbax-checkpoint	LOW
TFLite	.tflite	LOW
ExecuTorch	.ptl, .pte	LOW
TensorRT	.engine, .plan, .trt	LOW
PaddlePaddle	.pdmodel, .pdiparams	LOW
OpenVINO	.xml	LOW
Skops	.skops	HIGH
PMML	.pmml	LOW
Compressed Wrappers	.gz, .bz2, .xz, .lz4, .zlib	MEDIUM

Plus scanners for ZIP, TAR, 7-Zip, OCI layers, Jinja2 templates, JSON/YAML metadata, manifests, model cards, text files, and RAR recognition. RAR archives are reported as unsupported/fail-closed instead of being skipped.

View complete format documentation

Remote Sources

Scan models directly from remote registries and cloud storage:

# Hugging Face
modelaudit https://huggingface.co/gpt2
modelaudit hf://microsoft/DialoGPT-medium

# Cloud storage
modelaudit s3://bucket/model.pt
modelaudit gs://bucket/models/

# MLflow registry
modelaudit models:/MyModel/Production

# JFrog Artifactory (files and folders)
# Auth: export JFROG_API_TOKEN=...
modelaudit https://company.jfrog.io/artifactory/repo/model.pt
modelaudit https://company.jfrog.io/artifactory/repo/models/

# DVC-tracked models
modelaudit model.dvc

Authentication Environment Variables

• HF_TOKEN for private Hugging Face repositories
• AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY (and optional AWS_SESSION_TOKEN) for S3
• GOOGLE_APPLICATION_CREDENTIALS for GCS
• MLFLOW_TRACKING_URI for MLflow registry access
• JFROG_API_TOKEN or JFROG_ACCESS_TOKEN for JFrog Artifactory
• Store credentials in environment variables or a secrets manager, and never commit tokens/keys.

Installation

# Broad scanner coverage (recommended; excludes the TensorFlow runtime and platform-specific TensorRT)
pip install "modelaudit[all]"

# Core only (static scanners, pickle, NumPy, archives, manifests, metadata)
pip install modelaudit

# Specific frameworks (TensorFlow installs on Python 3.11-3.12; ONNX installs on Python 3.10-3.12)
pip install "modelaudit[tensorflow,pytorch,h5,onnx,safetensors]"

# CI/CD environments
pip install "modelaudit[all-ci]"

# On Python 3.11-3.12, add TensorFlow only when you need runtime-dependent checkpoint or weight analysis
pip install "modelaudit[all,tensorflow]"

# Docker
docker run --rm -v "$(pwd)":/app ghcr.io/promptfoo/modelaudit:latest model.pkl

The ONNX extra, including the ONNX portion of modelaudit[all], is packaged for Python 3.10-3.12.

CLI Options

Primary commands:

modelaudit [PATHS...]                           # Default scan command
modelaudit scan [OPTIONS] PATHS...              # Explicit scan command
modelaudit scan --list-scanners                 # List scanner IDs for targeted scans
modelaudit metadata [OPTIONS] PATH              # Extract model metadata safely (no deserialization by default)
modelaudit doctor [--show-failed]               # Diagnose scanner/dependency availability
modelaudit debug [--json] [--verbose]           # Environment and configuration diagnostics
modelaudit cache [stats|clear|cleanup] [OPTIONS]

Common scan options:

--format {text,json,sarif}   Output format (default: auto-detected)
--output FILE                Write results to file
--strict                     Fail on warnings, scan all file types, strict license validation
--sbom FILE                  Generate CycloneDX SBOM
--stream                     Process files one-by-one; remote downloads are deleted after scanning
--max-size SIZE              Size limit (e.g., 10GB)
--timeout SECONDS            Override scan timeout
--dry-run                    Preview what would be scanned
--verbose / --quiet          Control output detail
--blacklist PATTERN          Additional patterns to flag
--no-cache                   Disable result caching
--cache-dir DIR              Set cache directory for downloads and scan results
--progress                   Force progress display
--scanners LIST              Only run selected scanners (IDs/classes; comma-separated or repeated)
--exclude-scanner NAME       Exclude a scanner from the active set (comma-separated or repeated)
--list-scanners              List scanner IDs, class names, extensions, and dependencies

Targeted scanner selection:

# Discover scanner IDs and class names
modelaudit scan --list-scanners
modelaudit scan --list-scanners --format json

# Run only selected scanners
modelaudit scan ./models --scanners pickle,tf_savedmodel
modelaudit scan ./model.pkl --scanners PickleScanner

# Run the default scanner set except a noisy or slow scanner
modelaudit scan ./models --exclude-scanner weight_distribution

# For container formats, include both the container scanner and nested scanner
modelaudit scan ./archive.zip --scanners zip,pickle

--scanners starts from an explicit allowlist. --exclude-scanner subtracts scanners from either that allowlist or the default scanner set. Scanner selection is reflected in JSON output under scanner_selection.

For remote folders, ModelAudit narrows downloads by selected scanner extensions when safe, and keeps filtering conservative for container or header-routed scanners to avoid dropping extension-spoofed artifacts before scanning.

Metadata Extraction

# Human-readable summary (safe default: no model deserialization)
modelaudit metadata model.safetensors

# Machine-readable output
modelaudit metadata ./models --format json --output metadata.json

# Focus only on security-relevant metadata fields
modelaudit metadata model.onnx --security-only

--trust-loaders enables scanner metadata loaders that may deserialize model content. Only use this on trusted artifacts in isolated environments.

Exit Codes

• 0: No security issues detected
• 1: Security issues detected
• 2: Scan errors

Telemetry and Privacy

ModelAudit includes telemetry for product reliability and usage analytics.

• Collected metadata can include command usage, scan timing, scanner/file-type usage, issue severity/type aggregates, sanitized model names/references, and coarse metadata like file extension/domain.
• URL telemetry strips userinfo, query strings, and fragments from model references. Avoid putting credentials in model names, file names, or artifact paths when telemetry is enabled.
• Model files are scanned locally and ModelAudit does not upload model binary contents as telemetry events.
• Telemetry is disabled automatically in CI/test environments and in editable development installs by default.

Opt out explicitly with either environment variable:

export PROMPTFOO_DISABLE_TELEMETRY=1
# or
export NO_ANALYTICS=1

To opt in during editable/development installs:

export MODELAUDIT_TELEMETRY_DEV=1

Output Examples

# JSON for CI/CD pipelines
modelaudit model.pkl --format json --output results.json

# SARIF for code scanning platforms
modelaudit model.pkl --format sarif --output results.sarif

Troubleshooting

• Run modelaudit doctor --show-failed to list unavailable scanners and missing optional deps.
• Run modelaudit debug --json to collect environment/config diagnostics for bug reports.
• Use modelaudit cache cleanup --max-age 30 to remove stale cache entries safely.
• If pip installs an older release, verify Python is supported (python --version; ModelAudit supports Python 3.10-3.13).
• For additional troubleshooting and cloud auth guidance, see:
  • https://www.promptfoo.dev/docs/model-audit/
  • https://www.promptfoo.dev/docs/model-audit/usage/

Documentation

• Full docs — setup, configuration, and advanced usage
• Usage examples — CI/CD integration, remote scanning, SBOM generation
• Supported formats — detailed scanner documentation
• Support policy — supported Python/OS versions and maintenance policy
• Security model and limitations — what ModelAudit does and does not guarantee
• Compatibility matrix — file formats vs optional dependencies
• Scanner selection — targeted scanner allowlists and exclusions
• Metadata extraction guide — safe metadata workflows and --trust-loaders guidance
• Offline/air-gapped guide — secure operation without internet access
• Troubleshooting — run modelaudit doctor --show-failed to check scanner availability

License

MIT License — see LICENSE for details.