veritensor 1.4.1

Zero-Trust Security Platform for the AI Supply Chain. Scans models, datasets, notebooks, and RAG documents for threats.

🛡️ Veritensor: AI Supply Chain Security

Veritensor is an end-to-end security platform for the entire AI Life Cycle. It replaces traditional black-box scanning with deep semantic analysis, data supply chain protection, and cryptographic trust verification.

Unlike standard security tools, Veritensor provides a unified defense layer for every asset in your AI stack:

1. Models: Deep AST and Bytecode analysis of Pickle, PyTorch, Keras, Safetensors, and GGUF to block RCE, backdoors, and weight-tampering.
2. Datasets: High-speed streaming protection for Parquet, CSV, and JSONL to detect Data Poisoning, Malicious URLs, and PII.
3. Notebooks: Hardening of Jupyter (.ipynb) files by scanning code execution, markdown phishing, and identifying secrets leaked in cell outputs.
4. RAG Knowledge Base: Zero-trust extraction for PDF, DOCX, and PPTX to neutralize prompt injections before they reach your Vector Database.
5. Supply Chain Trust: Cryptographic signing of models and containers via Sigstore, ensuring that only verified assets reach your production environment.

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🚀 Features

• Deep Static Analysis: Decompiles Pickle bytecode and Keras Lambda layers to find obfuscated attacks (e.g., STACK_GLOBAL exploits). Now supports deep scanning of Zip archives (PyTorch) and Python Wheels.
• Identity Verification: Automatically verifies model hashes against the official Hugging Face registry to detect Man-in-the-Middle attacks.
• Dataset Poisoning Guard: Scans massive datasets (100GB+) using Streaming Analysis. Detects "Ignore previous instructions" patterns and malicious URLs in Parquet, CSV, TSV, and JSONL.
• Notebook Inspector: Scans Jupyter .ipynb files for execution threats, malicious markdown (XSS/Phishing), and leaked secrets in output cells.
• RAG Document Security: Protects your knowledge base by scanning PDF, DOCX, and PPTX for prompt injections and PII before vectorization.
• License Firewall: Blocks models with restrictive licenses (e.g., Non-Commercial, AGPL). Veritensor performs a hybrid check: it inspects embedded file metadata first, and automatically falls back to the Hugging Face API if metadata is missing (requires --repo).
• Supply Chain Security: Integrates with Sigstore Cosign to sign Docker containers. Includes timestamps to prevent replay attacks.
• CI/CD Native: Supports direct scanning from Amazon S3 and integrates with GitHub Actions, GitLab, and Pre-commit pipelines.

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📦 Installation

Via PyPI (Recommended for local use)

Veritensor is modular. Install only what you need to keep your environment lightweight:

Option	Command	Use Case
Core	pip install veritensor	Base model scanning (Pickle, Keras, Safetensors)
Data	pip install veritensor[data]	Datasets (Parquet, TSV, Pandas support)
RAG	pip install veritensor[rag]	Documents (PDF, DOCX, PPTX)
PII	pip install veritensor[pii]	PII detection
AWS	pip install veritensor[aws]	Direct scanning from S3 buckets
All	pip install veritensor[all]	Full suite for enterprise security

Via Docker (Recommended for CI/CD)

docker pull arseniibrazhnyk/veritensor:latest

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⚡ Quick Start

1. Scan a local model

Check a file or directory for malware:

veritensor scan ./models/bert-base.pt

Example Output:

╭────────────────────────────────╮
│ 🛡️  Veritensor Security Scanner │
╰────────────────────────────────╯
                                    Scan Results
┏━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ File         ┃ Status ┃ Threats / Details                    ┃ SHA256 (Short) ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ model.pt     │  FAIL  │ CRITICAL: os.system (RCE Detected)   │ a1b2c3d4...    │
└──────────────┴────────┴──────────────────────────────────────┴────────────────┘
❌ BLOCKING DEPLOYMENT

2. Verify against Hugging Face

Ensure the file on your disk matches the official version from the registry (detects tampering):

veritensor scan ./pytorch_model.bin --repo meta-llama/Llama-2-7b

3. License Compliance Check

Veritensor automatically reads metadata from safetensors and GGUF files. If a model has a Non-Commercial license (e.g., cc-by-nc-4.0), it will raise a HIGH severity alert.

To override this (Break-glass mode), use:

veritensor scan ./model.safetensors --force

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4. Scan AI Datasets

Veritensor uses streaming to handle huge files. It samples 10k rows by default for speed.

veritensor scan ./data/train.parquet --full-scan

5. Scan Jupyter Notebooks

Check code cells, markdown, and saved outputs for threats:

veritensor scan ./research/experiment.ipynb

6. Scan from Amazon S3

Scan remote assets without manual downloading:

veritensor scan s3://my-ml-bucket/models/llama-3.pkl

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📊 Reporting & Compliance

Veritensor supports industry-standard formats for integration with security dashboards and audit tools.

1. GitHub Security (SARIF)

Generate a report compatible with GitHub Code Scanning:

veritensor scan ./models --sarif > veritensor-report.sarif

2. Software Bill of Materials (SBOM)

Generate a CycloneDX v1.5 SBOM to inventory your AI assets:

veritensor scan ./models --sbom > sbom.json

3. Raw JSON

For custom parsers and SOAR automation:

veritensor scan ./models --json

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🔐 Supply Chain Security (Container Signing)

Veritensor integrates with Sigstore Cosign to cryptographically sign your Docker images only if they pass the security scan.

1. Generate Keys

Generate a key pair for signing:

veritensor keygen
# Output: veritensor.key (Private) and veritensor.pub (Public)

2. Scan & Sign

Pass the --image flag and the path to your private key (via env var).

# Set path to your private key
export VERITENSOR_PRIVATE_KEY_PATH=veritensor.key

# If scan passes -> Sign the image
veritensor scan ./models/my_model.pkl --image my-org/my-app:v1.0.0

3. Verify (In Kubernetes / Production)

Before deploying, verify the signature to ensure the model was scanned:

cosign verify --key veritensor.pub my-org/my-app:v1.0.0

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🛠️ Integrations

GitHub Actions

Add this to your .github/workflows/security.yml to block malicious models in Pull Requests:

name: AI Security Scan

on: [pull_request]

jobs:
  veritensor-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4 # Recommended to use latest checkout
      
      - name: Veritensor AI Security Platform
        uses: ArseniiBrazhnyk/Veritensor@v1.4.1
        with:
          path: '.'         # Scans everything: Models, Notebooks, Datasets, and Lock-files
          force: 'false'

Pre-commit Hook

Prevent committing malicious models to your repository. Add this to .pre-commit-config.yaml:

repos:
  - repo: https://github.com/ArseniiBrazhnyk/Veritensor
    rev: v1.4.1
    hooks:
      - id: veritensor-scan

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📂 Supported Formats

Format	Extension	Analysis Method
Models	.pt, .pth, .bin, .pkl, .joblib, .h5, .keras, .safetensors, .gguf, .whl	AST Analysis, Pickle VM Emulation, Metadata Validation
Datasets	.parquet, .csv, .tsv, .jsonl, .ndjson, .ldjson	Streaming Regex Scan (URLs, Injections, PII)
Notebooks	.ipynb	JSON Structure Analysis + Code AST + Markdown Phishing
RAG Docs	.pdf, .docx, .pptx, .txt, .md	Document Object Model (DOM) Text Extraction

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⚙️ Configuration

You can customize security policies by creating a veritensor.yaml file in your project root. Pro Tip: You can use regex: prefix for flexible matching.

# veritensor.yaml

# 1. Security Threshold
# Fail the build if threats of this severity (or higher) are found.
# Options: CRITICAL, HIGH, MEDIUM, LOW.
fail_on_severity: CRITICAL

# 2. Dataset Scanning
# Sampling limit for quick scans (default: 10000)
dataset_sampling_limit: 10000

# 3. License Firewall Policy
# If true, blocks models that have no license metadata.
fail_on_missing_license: false

# List of license keywords to block (case-insensitive).
custom_restricted_licenses:
  - "cc-by-nc"       # Non-Commercial
  - "agpl"           # Viral licenses
  - "research-only"

# 4. Static Analysis Exceptions (Pickle)
# Allow specific Python modules that are usually blocked by the strict scanner.
allowed_modules:
  - "my_company.internal_layer"
  - "sklearn.tree"

# 5. Model Whitelist (License Bypass)
# List of Repo IDs that are trusted. Veritensor will SKIP license checks for these.
# Supports Regex!
allowed_models:
  - "meta-llama/Meta-Llama-3-70B-Instruct"  # Exact match
  - "regex:^google-bert/.*"                 # Allow all BERT models from Google
  - "internal/my-private-model"

To generate a default configuration file, run: veritensor init

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🧠 Threat Intelligence (Signatures)

Veritensor uses a decoupled signature database (signatures.yaml) to detect malicious patterns. This ensures that detection logic is separated from the core engine.

• Automatic Updates: To get the latest threat definitions, simply upgrade the package:

  pip install --upgrade veritensor
  

• Transparent Rules: You can inspect the default signatures in src/veritensor/engines/static/signatures.yaml.
• Custom Policies: If the default rules are too strict for your use case (false positives), use veritensor.yaml to whitelist specific modules or models.

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📜 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.