detecting-qr-code-phishing-with-email-security
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.
- License 许可
- Apache-2.0
- 最近更新
- 2026-08-25
- Stars
- ★ 0
- Forks
- 0
01 前言 Front Matter
- name
- detecting-qr-code-phishing-with-email-security
- tags
- [ "quishing", "qr-code", "phishing", "email-security", "image-analysis", "ocr", "mobile-security" ]
- author
- mahipal
- domain
- cybersecurity
- license
- Apache-2.0
- version
- 1.0
- mitre_f3
- { "tactics": [ "reconnaissance", "resource-development", "initial-access" ], "version": "1.1", "techniques": [ { "id": "T1598", "name": "Phishing for Information", "source": "attack", "tactic": "reconnaissance" }, { "id": "T1660", "name": "Phishing", "source": "attack", "tactic": "initial-access" }, { "id": "F1020.002", "name": "Create Fake Materials: Fake Website", "source": "f3", "tactic": "resource-development" }, { "id": "T1583.001", "name": "Acquire Infrastructure: Domains", "source": "attack", "tactic": "resource-development" }, { "id": "F1006.002", "name": "Account Takeover: Exposed Login Credential", "source": "f3", "tactic": "initial-access" } ] }
- nist_csf
- [ "PR.AT-01", "DE.CM-09", "RS.CO-02", "DE.AE-02" ]
- subdomain
- phishing-defense
- description
- 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.
- nist_ai_rmf
- [ "MEASURE-2.8", "MAP-5.1" ]
- mitre_attack
- [ "T1566", "T1598", "T1534", "T1036", "T1027" ]
- atlas_techniques
- [ "AML.T0052", "AML.T0024", "AML.T0035" ]
02 资源文件 7
- 资产文件assets/template.md1.5 KB
- 许可证LICENSE11.0 KB
- 参考文档references/api-reference.md2.4 KB
- 参考文档references/standards.md1.6 KB
- 参考文档references/workflows.md2.5 KB
- 脚本scripts/agent.py5.9 KB
- 脚本scripts/process.py11.5 KB
03 SKILL.md 正文
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
- Establish reporting process for suspicious QR code emails
- Distribute guidance on safe QR scanning practices
Tools & Resources
- Barracuda Multimodal AI: OCR + deep image processing for QR detection
- Palo Alto ALFA: Safe-by-design QR code scanning assessment
- Microsoft Defender for O365: QR code detection in email images
- Proofpoint TAP: Image-based threat analysis with QR decoding
- Lookout/Zimperium: Mobile threat defense with QR scanning
Validation
- QR code phishing emails detected in controlled testing
- Split QR code and ASCII QR code evasion techniques caught
- QR-extracted URLs submitted to sandbox analysis
- Mobile devices alert on malicious QR destinations
- User reporting rate for quishing simulations exceeds 50%
- False positive rate for QR detection below 1%
04 同仓库更多 Skills
syllabus
Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.
pulse
Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.
patent
Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search / freedom-to-operate / competitive landscape / acquisition diligence / litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.
notebooklm
Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio/Video Overviews, Mind Maps, Reports incl. Briefing Doc/Study Guide/FAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.
litreview
Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER / Decomposition / hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5/10/20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed/OpenAlex (or Consensus) query.
grants
NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance/Innovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget/scope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.