菜单
Detect and prevent QR code phishing (quishing) attacks that bypass traditional email security by embedding malicious URLs in QR code images within emails. Use when detecting and prevent qr code phishing (quishing) attacks that bypass.
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基于 SOC 职业分类
| name | detecting-qr-code-phishing-with-email-security |
| description | Detect and prevent QR code phishing (quishing) attacks that bypass traditional email security by embedding malicious URLs in QR code images within emails. Use when detecting and prevent qr code phishing (quishing) attacks that bypass. |
| domain | cybersecurity |
| subdomain | phishing-defense |
| tags | ["quishing","qr-code","phishing","email-security","image-analysis","ocr","mobile-security"] |
| version | 1.0 |
| author | oyi77 |
| license | Apache-2.0 |
| atlas_techniques | ["AML.T0052","AML.T0024","AML.T0035"] |
| nist_ai_rmf | ["MEASURE-2.8","MAP-5.1"] |
| nist_csf | ["PR.AT-01","DE.CM-09","RS.CO-02","DE.AE-02"] |
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.
Trigger phrases:
"detecting qr code phishing with email security"
"Detect and prevent QR code phishing (quishing) attacks that bypass traditional e"
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
This section covers key concepts for detecting qr code phishing with email security.
# Example: IOC detection
import re
IOC_PATTERNS = {
"ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
"domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
"hash_md5": r"\b[a-f0-9]{32}\b",
"hash_sha256": r"\b[a-f0-9]{64}\b",
}
def extract_iocs(text: str) -> dict:
return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |