AI-powered email phishing detection using a private, local LLM. No data leaves your device. Supported Local LLM Providers: - Ollama - LM Studio - llama.cpp server - vLLM - etc.
Informasi Versi
Versi 1.0.6
49.3 KiB
Bekerja pada
Thunderbird 115.0 dan kemudian
v1.0.6 - False Positive Reduction Update Changes Made 1. Added "Suspicious" Verdict Category - New verdict: suspicious - for borderline emails that aren't clearly malicious - Three-tier system: legitimate → suspicious → malicious - UI shows orange badge with "⚡ SUSPICIOUS" for this category 2. Enhanced System Prompt with Legitimate Email Patterns Added guidance for the LLM to recognize legitimate emails: - Mailing list headers (newsletters) - No credential requests - Links matching sender domain - Transactional emails (receipts, confirmations) - Personalized greetings 3. Mailing List Recognition - Emphasized is_mailing_list: true as a strong legitimate indicator - Prompt explicitly states "newsletters are typically legitimate" 4. Confidence Threshold Logic - Low-confidence "malicious" verdicts (< 70%) automatically downgraded to "suspicious" - Adds explanatory reason when adjustment occurs 5. Reply-To/Return-Path Context - Clarified these mismatches are common in legitimate newsletters - "Only suspicious when combined with phishing content" 6. Updated UI - New orange "warning" badge style for suspicious emails - Pulsing animation to draw attention without alarming Files Modified | File | Changes | |------|---------| | src/shared/state.js | Added 'suspicious' to VERDICTS array | | src/shared/api.js | New prompt, confidence threshold logic | | src/messageDisplay/inject.js | 3-tier verdict display logic | | src/messageDisplay/inject.css | Orange warning badge styling | | manifest.json | Version 1.0.6 | | package.json | Version 1.0.6 | Expected Impact - Fewer legitimate emails marked as "malicious" - Borderline cases now show "suspicious" instead of binary verdict - Newsletters and mailing lists less likely to trigger false positives - Low-confidence detections handled more gracefully
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