Phishing attacks are getting smarter. Cybercriminals no longer rely on generic “You’ve won a prize” emails—today’s phishing scams are highly personalized, context-aware, and harder to detect with traditional security measures. Traditional phishing detection relies on static rules, keyword analysis, and domain reputation. While these methods work well against common scams, they often struggle with context-based phishing, the kind that mimics real conversations, exploits human psychology, and tricks even security-savvy users. 

This is where Large Language Models (LLMs) come into play. By integrating LLMs into the phishing detection engine, businesses can shift from reactive to proactive defense against evolving cyberattacks. 

Think of it as a librarian who doesn’t just catalog books based on their title. Instead, this librarian actively reads each book, understanding its storyline, tone, and hidden messages. They scan through pages, cross-reference with other books, and assess the context of each piece of information. When a new book arrives (just like a new email), the librarian doesn’t just judge it by the cover or title but digs deeper to understand if it’s misleading, deceptive, or inappropriate.  

This is exactly what Coro does with LLM-powered phishing detection. By leveraging Gemini, our system can now analyze the context of emails by identifying sophisticated phishing attempts that would otherwise go unnoticed. Just as a librarian can detect an inauthentic book from a mile away, Coro can spot sophisticated phishing attempts that don’t fit the usual pattern.  

Catching Advanced Phishing Attempts: Real Use Cases 

Let’s explore how Coro’s latest enhancement empowers its LLM-powered detector to catch even the most sophisticated scams.  

Bitcoin Extortion Scams 

These emails threaten to expose sensitive information allegedly stolen from the recipient unless they pay a Bitcoin ransom. Traditional detection methods often miss these threats because attackers avoid obvious scam language or make subtle wording changes. Recipients unfamiliar with cryptocurrency may panic and comply with payment demands when these emails evade filtering. 

Advance-Fee Scams (Modern Nigerian Prince Fraud) 

Contemporary versions of these scams promise substantial financial rewards after a small initial payment. Unlike their blatantly obvious predecessors, modern variants use sophisticated approaches, spectating legal documents, impersonating legal professionals, or infiltrating legitimate business communications. Traditional filters struggle to identify these threats since they lack malicious links and rely primarily on persuasive language, while LLMs can recognize the fraudulent intent from context. 

Highly Personalized Attacks 

Today’s attackers craft phishing emails specifically for targeted industries, employees, or ongoing conversations. For instance, an attacker might pose as a CEO requesting a wire transfer using language that matches the company’s communication style. These emails bypass traditional filters by avoiding typical red flags, but LLM-powered detection identifies inconsistencies in tone, intent, and contextual elements. By reducing false negatives, we ensure that fewer phishing emails reach user inboxes, providing a stronger safety net for businesses. 

Conclusion 

LLM-based detection has significantly improved phishing identification. It detects previously overlooked threats and analyzes email content and sender intent to improve threat detection. As the cybersecurity landscape evolves, continuous advancements in AI-driven security will further protect businesses from emerging threats. With LLM-based detection, businesses are not just reacting to threats—they are proactively defending against the next wave of cyberattacks, ensuring a stronger, more resilient security posture. 

Stay tuned for updates or explore a demo to see it in action.