detecting-qr-code-phishing-with-email-security
Helps your agent identify and block phishing emails that use QR codes. It analyzes images to find hidden malicious links, protecting against attacks that bypass common filters.
What it does
This tool helps identify and block dangerous emails that use QR codes to trick people. It analyzes images within emails to find these codes and prevent them from leading to malicious websites.
When to use it
- ✓When investigating suspicious emails containing QR codes.
- ✓When setting up defenses against scams using QR codes.
- ✓When analyzing how emails with QR codes can be dangerous.
Example of what to ask your AI
“Analyze this email with a QR code to see if it's a scam attempt.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool uses artificial intelligence to read QR codes within images in emails. It extracts the links from these codes and checks if they are safe, blocking dangerous ones before they reach you.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-qr-code-phishing-with-email-securityOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/detecting-qr-code-phishing-with-email-security/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Detecting QR Code Phishing with Email Security
Overview
QR code phishing (quishing) is a rapidly growing attack vector where malicious URLs are embedded in QR code images within phishing emails. Quishing incidents grew fivefold from 46,000 to 250,000 between August and November 2025, with credential phishing comprising 89.3% of detected incidents. Traditional email security filters struggle because QR codes cannot be read by humans or standard URL scanners, and when scanned, users typically use personal mobile devices that lack corporate security controls. Attackers have evolved to use split QR codes (two separate images), nested QR codes, and ASCII text-based QR codes to evade detection.
When to Use
- When investigating security incidents that require detecting qr code phishing with email security
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Email security gateway with image analysis capabilities
- Understanding of QR code structure and encoding
- Mobile device management (MDM) or mobile threat defense solution
- Security awareness training program
- SIEM platform for correlation and alerting
Key Concepts
Why Quishing Works
- Bypasses URL Scanners: Traditional gateways scan text-based URLs but cannot decode image-embedded URLs
- Shifts to Unprotected Devices: Corporate email arrives on secured systems but QR scan occurs on personal mobile devices
- User Trust: QR codes are normalized in daily life (payments, menus, parking)
- Low Detection Rate: Only 36% of quishing incidents are accurately identified by recipients
Evasion Techniques (2025)
- Split QR Codes: QR code divided into two separate images that look benign individually (Gabagool PhaaS kit)
- Nested QR Codes: QR code within a QR code, with first scan leading to intermediate page
- ASCII QR Codes: QR rendered as text characters instead of images, bypassing image analysis (12% of attacks in Jan 2026)
- Styled/Artistic QR Codes: Custom-designed QR codes with logos that evade pattern matching
- PDF Attachment QR: QR code embedded in PDF attachment rather than email body
Detection Challenges
- Pattern-based detection faces trade-off: aggressive tuning causes false positives, cautious tuning causes misses
- Average similarity score of 0.209 between quishing and legitimate QR emails
- QR codes in image attachments require OCR and deep image processing
Workflow
Step 1: Enable Image-Based Threat Detection
- Configure email gateway to scan embedded images for QR codes
- Enable OCR processing on image attachments (PNG, JPG, GIF, BMP)
- Deploy multimodal AI that combines image processing, OCR, and NLP analysis
- Configure PDF scanning to detect QR codes within attachments
- Set up detection for ASCII/text-based QR code rendering
Step 2: Configure QR Code URL Analysis
- Extract URLs from detected QR codes and submit to URL reputation services
- Apply same URL scanning policies to QR-extracted URLs as text-based URLs
- Enable real-time sandbox analysis for QR-decoded destination pages
- Configure time-of-click protection for QR-extracted URLs where possible
- Block known phishing domains extracted from QR codes
Step 3: Deploy Mobile-Side Protection
- Implement mobile threat defense (MTD) with QR code scanning capability
- Deploy Palo Alto ALFA or equivalent safe-by-design QR scanning
- Configure MDM policies to warn users before opening scanned URLs
- Enable corporate VPN/secure browser for QR-scanned destinations
- Block known credential harvesting domains at the mobile proxy level
Step 4: Build Detection Rules
- Alert on emails containing only an image and minimal text (common quishing pattern)
- Flag emails with QR code images from external first-time senders
- Detect urgency language combined with QR code presence
- Alert on emails impersonating IT/security team requesting QR scan for MFA setup
- Monitor for common quishing themes: MFA reset, document signing, voicemail notification
Step 5: Train Users on Quishing Recognition
- Update security awareness program to include QR code phishing scenarios
- Conduct quishing simulation campaigns using controlled QR codes
- Teach users to verify QR destination URLs before entering credentials
…
FAQ
Why are QR codes in emails dangerous?
Because they can hide links to fake websites that steal your information, and traditional security filters cannot read them like a normal link.
How do criminals try to trick people with QR codes?
They use codes split into multiple images, codes within other codes, or even codes made of text, to make detection difficult.
What do I need to use this protection?
An email security system that analyzes images, a mobile phone with some protection, and training to recognize suspicious emails.
Details
Original description by the author
Detect and prevent QR code phishing (quishing) attacks that embed malicious URLs inside QR code images to bypass link-based email security, using image-based threat detection, OCR/QR decoding, and mobile-side scanning (Microsoft Defender for O365, Proofpoint TAP, Barracuda Multimodal AI). Use when configuring gateway rules against QR phishing or investigating suspicious emails containing QR codes.
Popularity
- GitHub stars
- 35k
- Ranking position
- #5701
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
More skills from this repository
abusing-dpapi-for-credential-access
Gives your AI agent ready-made instructions for a specific task, so it does it better and faster.
mukul975/Anthropic-Cybersecurity-Skills
abusing-shadow-credentials-for-privesc
Teaches your AI agent to write clear, well-structured text.
mukul975/Anthropic-Cybersecurity-Skills
achieving-cmmc-level-2-compliance
Helps your agent prepare an environment for CMMC Level 2 compliance. It implements security requirements and manages necessary documentation.
mukul975/Anthropic-Cybersecurity-Skills
acquiring-disk-image-with-dd-and-dcfldd
Helps your agent create exact copies of hard drives. This is useful for ensuring no information is lost or altered during investigations.
mukul975/Anthropic-Cybersecurity-Skills
Similar skills
agent-architecture-audit
Makes your agent check if everything is working correctly. It finds and fixes problems in AI systems, ensuring they perform as expected.
affaan-m/ECC
agent-eval
Helps your agent compare different AI coding tools. It measures which one is faster, cheaper, and more reliable for specific tasks.
affaan-m/ECC
agent-harness-construction
Makes your agent better understand how to perform tasks. It optimizes the tools and instructions the agent uses to increase its chances of success.
affaan-m/ECC