v1.0.0: Initial Release - Anti-PLUTO Framework

Excited to announce the inaugural open-source release of Anti-PLUTO (Anti-Phishing Lexical Utilities and Threat Observation).

This release introduces a unified, extensible applied research framework designed to handle the entire lifecycle of modern email security research. From raw dataset ingestion and privacy-preserving masking, to synthetic threat generation, baseline benchmarking, and finally, threat classification.

Framework Capabilities

• Robust Data Ingestion & Preprocessing:
  • End-to-end pipeline to parse complex RFC 5322 MIME structures.
  • Automated HTML stripping, thread slicing, and Unicode normalization to ingest chaotic real-world corporate corpora.
• Privacy-Preserving PII Masking:
  • Two-stage lexical sanitization engine utilizing deterministic regex tokenization alongside high-accuracy spaCy NER (Transformer-backed).
  • Safely masks URLs, IPs, email addresses, names, and organizations to eliminate target leakage and anonymize datasets.
• Procedural LLM Cohort Generation:
  • Built-in interfaces for Azure OpenAI, Anthropic, and OpenRouter to procedurally generate massive, contextually fluent synthetic email cohorts (Benign & Phishing).
  • Traceability tools to track generated emails back to their human seed data.
• Validation, Telemetry & Benchmarking:
  • Integrated statistical validation tools to monitor dataset integrity.
  • Automated benchmarking suite to evaluate new datasets and models against legacy industry-standard engines (SpamAssassin & Rspamd).
• Reference Implementation: Dual-Branch Classification Engine:
  • Provides a state-of-the-art reference model for differentiating 4 email cohorts (Human Benign, Human Phishing, LLM Benign, LLM Phishing).
  • Utilizes a 100k-dim stylometric feature extractor fused with a 768-dim semantic branch (fine-tuned DeBERTa-v3).
• Production API Serving:
  • Asynchronous FastAPI backend capable of real-time inference (up to 7,497 msgs/s) with ONNX model export support for seamless deployment.

Installation

Anti-PLUTO is available on PyPI! You can install the core framework via:

pip install antipluto

For ML classification, generation, and API capabilities, install the optional dependencies:

pip install "antipluto[dev,classifier,api]"

Documentation

For comprehensive setup instructions, configuration details, and full CLI usage, please refer to the README.

Released under the CC BY-NC 4.0 License for academic and non-commercial research.