wang-rui/phishguard-scaffold

Health v2 · maintenance only

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Adoption not part of the score

1012 stars · 169 forks observed · 2026-10-03

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PhishGuard is a Python research framework that jointly performs phishing detection and dissemination control on social media using LLaMA-based semantic modeling. It combines LoRA fine-tuning, adversarial training, and graph-based intervention strategies to classify phishing content and minimize its spread.

Use cases

• detect phishing tweets on social media
• fine-tune LLaMA for phishing classification
• simulate phishing message propagation on social networks
• select intervention nodes to minimize phishing spread
• run adversarial robustness training for phishing detectors
• evaluate phishing detection accuracy and AUC on Twitter data

When to choose

• you need a research-grade pipeline combining phishing detection with propagation control
• you want to fine-tune LLaMA with LoRA for social media phishing classification
• you need graph-based intervention strategies like independent cascade and greedy node selection

When to avoid

• you need a production-ready phishing filter with a supported license
• you require a plug-and-play API without training your own models
• you work outside social media text or lack GPU resources for LLaMA fine-tuning

Facets

library · maturity active

machine-learning deep-learning nlp llm-training rag security security social-media machine-learning artificial-intelligence python phishing-detection llama social-media propagation-control adversarial-training graph-intervention research-framework natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="wang-rui/phishguard-scaffold")