Proofpoint has issued an open-source tool, PDF Object Hashing, that is aimed at identifying malicious PDFs based on their structural fingerprints. Instead of the usual detection using volatile indicators like URLs, images, or metadata, this tool looks at the inherent structure of PDF files. Checking stable object properties, security teams can establish strong detection rules and link campaigns to particular threat actors, even when attackers change superficial aspects to avoid detection. PDFs have traditionally been a cybercriminals' go-to delivery vehicle for malware, phishing, and business email compromise (BEC) campaigns. Malicious links, QR codes, or spoofed invoices are often embedded by attackers in PDF attachments with the intent to deceive users into downloading payloads or exposing credentials. Nevertheless, the PDF format's built-in malleability supporting various whitespace types, compressible tables, and encrypted streams renders traditional signature-based detection untrustworthy. Simple changes can entirely transform a file's hash without altering its malicious purpose. PDF Object Hashing addresses this hurdle by analyzing an object hierarchy of a PDF and deriving stable attributes like Pages, Catalog, XObject/Image, Annotations/Link, Metadata/XML, Producer, and Font/Type1. These are concatenated and hashed into a distinct structural fingerprint, akin to an imphash for binaries. This methodology groups similar PDFs on a structural basis instead of content, allowing analysts to connect and follow mutating threats. In the wild, Proofpoint employed the technology to detect campaigns by UAC-0050, which targeted Ukraine via encrypted OneDrive-themed PDFs delivering NetSupport RAT, and UNK_ArmyDrive, an Indian collective responsible for BEC lures imitating Bangladesh Ministry documents. In both instances, structural hashes indicated obvious connections among variants, speeding up signature development and enhancing threat attribution among campaigns.
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