Why organizations should scan files and email text

Time and time again, threat actors use attached files to send malware, remote access tools, and ransomware while keeping the message body safe enough to get past tests that look for links and keywords. As one forensic study, Interpol review of digital evidence for 2019-2022, notes, “The unsuspecting user is duped into opening an email attachment with malicious code that executes a ransomware payload.” Another study published in the BMJ offers that malicious email “encourages recipients to click links to websites running malicious code or to download or install malware.”

Microsoft’s ClickFix write-up shows how attackers evade automated defenses by using HTML attachments or ZIP lures to trick users into copying and pasting commands into Windows tools like Run or PowerShell. Microsoft warns the technique can “get past conventional and automated security solutions” because execution comes from user action, not a suspicious link in the email body. Once triggered, ClickFix chains can deliver infostealers and RATs such as AsyncRAT.

Healthcare settings feel that danger more strongly because email is still such a central part of operations. There are more attachments and a higher tolerance for normal-looking documents, letting these attachments sneak by through human error.

Good defenses like Paubox are especially valuable as they check the whole message surface area, including the headers, body content, and any attachments. Solutions like Paubox also use layered controls like malware screening, sandbox-style detonation, and policy enforcement before sending.

Types of file scanning

Signature-based screening

Signature-based screening meticulously compares attachments to established malware fingerprints. The system identifies patterns or file hashes from an attachment and matches them against threat-intelligence databases.

Detection remains swift and dependable for familiar malware due to the speed of matching. Coverage diminishes when faced with zero-day exploits and polymorphic variants that alter their code, rendering the original signature ineffective.

Deep learning

Custom networks and other models can look at raw file data and learn its features without using preset signatures. Some attachment models and language models that look at email text work together, as well as signals from headers and message structure. The ensemble design is better at catching payloads that are hard to find, like malicious macros in Office files or weaponized papers that seem like normal PDFs.

According to the study Advancing Phishing Email Detection: A Comparative Study of Deep Learning Models, deep learning phishing detectors report that an augmented 1D-CNN model achieves 99.68% accuracy, 99.66% F1, 99.32% recall, and 100% precision, showing how high-performance models can outperform traditional approaches when attacks get more complex. The same study also notes that phishing often sits at the front of malware spread, citing evidence that malware propagation in a network begins with a phishing message in a large share of cases.

Why does payload usually live in files

Cybercriminals embed ransomware droppers, remote access tools, and credential-stealing code within everyday files such as PDFs and Office documents. They use macros, embedded objects, or other forms of active content to execute the code.

A Multidisciplinary Digital Publishing Institute (MDPI) research paper on infrastructure attack patterns makes the attachment point explicit, noting that “malicious e-mails carry ransomware as an attachment,” which frames the file as the delivery vehicle rather than the message body. Email text cannot hold a complete binary or the structures malware needs for persistence and follow-on operations, so attackers keep the body short and believable while placing the real payload in the file.

Campaigns built around these methods often use archives and deeply concealed files, complicating the efforts of controls that search for links or keywords to detect them. Blocking the file restricts the execution path.

What file scanning catches that email-body scanning can miss

File scanning identifies threats that body-only scanning frequently overlooks, as the real danger often resides in the attachments rather than the text itself. Cybercriminals cleverly bundle executable malware, macro-enabled Office documents, weaponized PDFs, and archived droppers, ensuring that the email body remains concise, unremarkable, and difficult to detect through keyword or URL filters.

Healthcare workflows ensure that the strategy remains effective, as staff consistently manage routine files. One journal article from Health Communication of patient-provider emails in a safety-net clinic finds that patients requested action in 77% of emails, with many requests tied to medications or treatment (29%), and that requests were resolved in 84% of exchanges. The pattern that normalizes “open, read, act” behavior can make everyday attachments blend into legitimate work.

File-based inspection is especially useful when attackers leverage compressed archives, embedded objects, or obfuscation techniques to bypass straightforward signature matches and text-centric heuristics.

How Paubox fills the gaps

Paubox Inbound Security improves email protection with AI and advanced filtering, building on controls like content and metadata analysis, inbound DLP rules, and automated quarantine for any flagged emails.

It offers the use of large language models and vector databases alongside generative AI to effectively classify incoming emails. The approach improves the ability to make informed decisions regarding suspicious attachments and social-engineering tactics.

With Paubox, admins and senders receive timely notifications whenever an email is quarantined by DLP. All of these features create a foolproof email security environment that limits the chances of malware sneaking through an attachment.

FAQs

What file types benefit most from generative AI scanning?

Office files (Word, Excel, PowerPoint), PDFs, HTML files, disk images/archives, and script-like files often benefit because attackers hide active content and multi-stage lures in them.

Can generative AI detect living off the land attachment attacks?

It can help by recognizing social-engineering patterns that push users to run built-in tools (PowerShell, Run, and cmd) and by flagging attachment types commonly used to start those chains.

How does it handle clean attachments that only become dangerous after a click?

Context-aware analysis can flag suspicious sequences. Many stacks also add URL protection and detonation for downloaded files.