Phishing without links as image-based attacks take over

Image-based attacks represent a quiet but significant shift in how phishing works. Instead of relying on obvious links or attachments, attackers now hide malicious content inside images, placing URLs, instructions, or prompts where traditional filters can’t easily see them.

This change reflects a broader problem in phishing defense, one that researchers in a Scientific Reports study describe clearly: “Traditional methods of identifying phishing websites, such as blacklist and heuristic approaches, often fail to provide sufficient protection… attackers constantly evolve their methods in order to bypass current security measures.”

What makes this tactic especially effective is how well it sidesteps modern defenses. Email gateways and web filters have become very good at catching dangerous URLs, but they are far less consistent at interpreting what’s inside an image.

Many phishing campaigns now embed their lure in banners or fake alerts, messages like ‘your account is locked’ or ‘claim your refund’, knowing that scanners may never read the text if it isn’t written out. This weakness is amplified by scale: research shows that more than 80% of organizations experience phishing attacks each year.

This gives attackers an edge over traditional link-based scams. Blacklists and heuristic tools can block most malicious URLs with high accuracy, but those same tools often miss threats when the danger is buried in visual content. Phishers exploit this gap by copying the look and feel of trusted brands, logos, layouts, and even favicons, to create a sense of legitimacy without presenting anything that looks overtly risky.

What traditional phishing looked like

Traditional phishing typically appears as an email that seems to come from a trusted source such as a bank, government agency, or employer. These messages often create urgency, warning of account suspensions or security issues, and prompt recipients to click a link or open an attachment. The links usually lead to fake websites designed to collect login credentials, while attachments may contain malware.

Common warning signs include generic greetings like ‘Dear User,’ minor spelling mistakes, sender addresses that do not quite match the organization they claim to represent, and shortened URLs that disguise their true destination. Despite these red flags, such messages remain effective because they rely on familiar social engineering tactics, exploiting trust in known brands and encouraging quick reactions before careful judgment can take place.

As phishing techniques evolve, security tools have had to adapt as well. Platforms such as Paubox now scan QR codes embedded in emails and use generative AI to analyze visual and contextual cues, helping detect threats that no longer rely on traditional links or obvious malware.

As one study titled Susceptibility to phishing on social network sites: A personality information processing model explains, “Today, the traditional approach used to conduct phishing attacks through email and spoofed websites has evolved to include social network sites (SNSs). This is because phishers are able to use similar methods to entice social network users to click on malicious links masquerading as fake news, controversial videos and other opportunities thought to be attractive or beneficial to the victim.”

Why image-based attacks are effective

Image-based attacks have become especially effective in modern phishing because they slip past many of the defenses that were built to stop text and links. Most email security systems still focus on scanning written content and suspicious URLs, but when the dangerous message is hidden inside an image, those filters often miss it. Attackers take advantage of this by embedding instructions, fake login prompts, or redirection cues directly into visuals.

As one recent fraud-detection study from Entropy notes, “Previous research has mainly relied on expert knowledge for feature engineering, which lags behind and struggles to adapt to the continuously evolving patterns of fraud effectively.”

Phishing sites now commonly reduce visible, machine-readable text altogether. Instead, they rely on screenshots, banners, and page designs that look nearly identical to real brands. To a detection system, these pages may appear clean. To a user, they feel familiar and safe. While tools like optical character recognition can help extract text from images, they are not yet fast or consistent enough to catch every threat in real time, which leaves a gap that attackers continue to exploit.

Human behavior makes these attacks even more effective. People tend to trust what looks familiar, a recognizable logo, a known layout, or a brand color scheme, often more than they trust the small details in a browser’s address bar. Even when security indicators are present, attention naturally gravitates toward the image on the screen, not the fine print around it.

How generative AI email solutions offer an impenetrable defense

Generative AI is becoming a part of how organizations defend against newer forms of phishing, especially attacks that rely on images rather than obvious links. These systems go beyond basic keyword scanning. They look at the full context of a message, analyzing written content, visual elements, and behavioral patterns together. Platforms like Paubox, for example, use generative AI to examine how an email is constructed, not just what it says, helping security teams catch image-based lures that traditional filters often miss.

This broader approach reflects what researchers have observed in another Scientific Reports study about modern security needs: “Traditional cyber defense has lost its effectiveness since conventional cyber threats have become more advanced and necessitate more competent protective measures.” As phishing grows more visual and more deceptive, security tools have had to evolve in the same direction.

Many modern email platforms now train their detection models using simulated attacks, creating realistic phishing examples so systems can learn to recognize new tricks before they show up in the wild. This includes teaching models how to interpret text pulled from images and how to flag visuals that resemble common scams.

At the same time, it’s clear that no system is perfect. As the study notes, “Though promising, AI utilization for secure software design remains immature,” especially when attackers actively look for ways to exploit gaps in automated defenses.

Instead of judging an email on one signal alone, these tools weigh several factors at once: how the sender usually behaves, whether the imagery matches known brands, and whether the tone of the message feels unusually urgent or out of place. When something doesn’t add up, the system can quarantine the message automatically, often before anyone ever sees it.

See also: HIPAA Compliant Email: The Definitive Guide (2025 Update)

FAQs

Does generative AI reduce false positives?

In many cases, yes. Because it evaluates multiple factors at once—sender behavior, visual cues, message tone, and historical patterns—it can make more accurate decisions than systems that only scan for keywords or links.

Can attackers use generative AI too?

Yes. Attackers increasingly use AI to write more convincing phishing emails, generate fake images, and automate large-scale campaigns. This is why defensive AI tools must continue to evolve.

Is generative AI enough to stop phishing on its own?

No. It works best as part of a layered security strategy that includes email authentication (DMARC, SPF, DKIM), endpoint protection, user training, and incident response planning.