Security analysis toolkit for machine learning models and infrastructure. Covers the full ML lifecycle — from adversarial robustness testing through training-time poisoning detection to deployment hardening.

pip install mlsec          # core (torch only)
pip install mlsec[all]     # all optional dependencies

Most ML security tools focus on a single attack surface. mlsec covers six critical areas in one toolkit, designed to integrate into existing MLOps pipelines:

Track robustness drift across model versions with baseline regression:

mlsec adversarial \
  --model-script models.py \
  --factory create_resnet \
  --input-shape 1,3,224,224 \
  --num-classes 1000 \
  --attacks fgsm pgd cw \
  --epsilon 0.01 \
  --baseline-file baselines.json \
  --update-baseline

Scan checkpoints for anomalies, generate fingerprints, convert to safe formats:

mlsec checkpoint /path/to/checkpoints/ \
  --convert-safetensors \
  --write-fingerprint fingerprints/ \
  --reference-fingerprint reference.json \
  --json

Monitor gradient divergence during distributed training:

# Generate demo data
mlsec poison simulate --log-dir logs/ --workers 8 --steps 500

# Analyze for poisoning signals
mlsec poison monitor --log-dir logs/ --threshold 3.0

# Live monitoring via UDP broadcast
mlsec poison listen --port 5454 --expected-workers 8

Validate the full export pipeline with provenance tracking:

mlsec export-guard \
  --model-script models.py \
  --input-shape 1,3,224,224 \
  --enable-onnxruntime \
  --build-engine \
  --hash-record attestation.json

Audit inference server configs for security gaps:

mlsec triton models/**/config.pbtxt --summary

Quick security triage of HuggingFace models:

mlsec inspect --allow-downloads

mlsec/
├── adversarial    — FGSM, PGD, CW attacks with baseline regression tracking
├── inspect        — Weight anomaly detection + activation monitoring hooks
├── poison         — Gradient snapshotter + CUSUM change-point detection
├── export-guard   — ONNX lint + SHA-256 provenance chain + trtexec integration
├── checkpoint     — KL-divergence fingerprinting + safetensors conversion
└── triton         — Heuristic config.pbtxt parser + security rule engine

Each tool is standalone (no cross-dependencies) and works both as a CLI command and as a Python library:

from mlsec.tools.checkpoint_triage import inspect_state_dict, compute_fingerprint

anomalies = inspect_state_dict(model.state_dict(), threshold=100.0)
fingerprint = compute_fingerprint(model.state_dict())

# Minimal (PyTorch only)
pip install mlsec

# With specific extras
pip install mlsec[vision]        # + torchvision
pip install mlsec[transformers]  # + HuggingFace transformers
pip install mlsec[onnx]          # + onnx + onnxruntime
pip install mlsec[safetensors]   # + safetensors format support
pip install mlsec[all]           # everything

# Development
pip install -e ".[dev,all]"
pytest tests/ -v

mlsec is designed to run in automated pipelines. Every tool returns structured exit codes:

Example GitHub Actions step:

- name: Adversarial regression gate
  run: |
    mlsec adversarial \
      --model-script models.py \
      --factory create_model \
      --input-shape 1,3,224,224 \
      --num-classes 1000 \
      --attacks fgsm pgd \
      --baseline-file baselines.json

JSON output is available for checkpoint triage (--json flag) for machine-readable results.

• FGSM — Fast Gradient Sign Method (single-step)
• PGD — Projected Gradient Descent (multi-step with random restarts)
• CW — Carlini-Wagner L-inf (optimization-based)
• Mixed-precision testing (float32, float16, bfloat16)
• Baseline tracking with configurable regression thresholds

• Per-worker gradient L2/L-inf norm monitoring
• Cross-worker divergence ratio computation
• CUSUM change-point detection for slow-burn poisoning
• Live UDP broadcast aggregation for real-time alerts

• SHA-256 provenance hashing at every pipeline stage
• ONNX graph linting (custom domains, control-flow ops, large constants, embedded subgraphs)
• KL-divergence fingerprinting for checkpoint drift detection
• Automatic safetensors conversion for pickle-free distribution

• Triton config auditing without protobuf dependency
• Checks for: rate limiting, dynamic batching, auth controls, input bounds, logging redaction
• ONNX Runtime numerical drift comparison

These tools are for authorized security testing only. See SECURITY.md for the full responsible use policy.

git clone https://github.com/scthornton/ml-security-tools.git
cd ml-security-tools
pip install -e ".[dev,all]"
pytest tests/ -v
ruff check .

MIT — see LICENSE.

• Email: [email protected]
• Security issues: See SECURITY.md