Dramatic rise in fraudulent PDF files detected this year

Most traditional security controls cannot identify and mitigate links to scams or malware hidden in PDF files.

Update: 2019-04-29 06:20 GMT

SonicWall Capture Labs threat researchers are reporting a substantial increase in fraudulent PDF files. This fraud campaign takes advantage of recipients’ trust in PDF files as a “safe” file format that is widely used and relied upon for business operations.

Last year, SonicWall Real-Time Deep Memory Inspection (RTDMI) identified over 74,000 never-before-seen attacks, a number that has already been surpassed in the first quarter of 2019 with more than 173,000 new variants detected. In March, the company’s patent-pending RTDMI technology identified over 83,000 unique, never-before-seen malicious events, of which over 67,000 were PDFs linked to scammers and more than 5,500 were PDFs with direct links to other malware.

Targets of the phishing style PDF scam campaigns typically receive malicious documents from “businesses” luring victims with attached PDF files that look deceivingly realistic with misleading links to fraudulent pages. The business offer within the PDF attachment is enticing to recipients, as it promises to be free and profitable with just the click of a link.

Most traditional security controls cannot identify and mitigate links to scams or malware hidden in PDF files, greatly increasing the success of the payload. This increase implies a growing, widespread and effective strategy against small- and medium-sized businesses, enterprises and government agencies.

RTDMI identifies and blocks malware that may not exhibit any detectable malicious behaviour or hides its weaponry via encryption. By forcing malware to reveal its weaponry into memory, RTDMI detects and proactively stops mass-market, zero-day threats and unknown malware accurately utilizing real-time, memory-based inspection techniques. RTDMI also analyzes documents dynamically via proprietary exploit detection technology, along with static inspection, to detect many malicious document categories.

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