LLM-Based Phishing Attacks: Detecting and Mitigating AI-Generated Threats

The Rise of AI-Powered Phishing

Traditional phishing relied on poor grammar, generic content, and suspicious formatting — making it relatively easy to detect.

However, attackers now use advanced LLMs like ChatGPT, WormGPT, or purpose-built malicious AIs to generate hyper-personalized and grammatically flawless phishing emails.

A 2025 IBM X-Force Threat Intelligence Index shows a 41% increase in successful phishing attacks involving AI-generated content. These emails can:

• Imitate a CEO’s tone to request wire transfers.
• Mimic IT support staff requesting password resets.
• Clone legal or HR communication templates to distribute malware.

Attackers also use AI to automate multilingual phishing, adapting emails to employees’ native languages — a tactic that boosts credibility.

Why They’re So Effective

AI-generated phishing attacks are effective because they leverage four key capabilities:

1. Hyper-personalization – Scraping social media or company profiles to tailor emails.
2. Impeccable language – No grammatical errors, raising fewer suspicions.
3. Contextual awareness – Using project names or org structures to sound real.
4. Rapid scalability – AI can create thousands of emails in seconds.

These features reduce the effectiveness of traditional email filtering and user awareness training.

How Stellar Cyber Helps Detect AI-Based Phishing

Stellar Cyber’s Open XDR platform detects advanced threats using:

• AI-driven behavioral analytics: Detects anomalous user behaviors instead of just keyword patterns.
• Email integration with SIEM correlation: Identifies suspicious activities post-phishing success.
• Threat intelligence fusion: Combines IOCs from global feeds, including AI phishing toolkits.

This ensures phishing emails that bypass native email security are still flagged by behavior anomalies.

SentinelOne’s Role in Preventing Execution

SentinelOne intercepts post-click threats with its behavioral AI engine by:

• Detecting memory-based payloads like fileless malware.
• Blocking C2 traffic after compromise.
• Automating rollback to restore pre-attack states.

In 2025, it also supports real-time phishing URL sandboxing using Singularity™ AI to detect new phishing domains instantly.

Tenable.io’s Exposure Management

Phishing sometimes exploits vulnerabilities rather than delivering malware. Tenable.io helps by:

• External attack surface scans to find outdated or vulnerable web apps.
• Cloud misconfiguration alerts for platforms like AWS and Azure.
• Vulnerability prioritization to focus on real, exploitable risks.

It integrates risk scoring with phishing simulations for actionable insight.

ISO 27001:2022 Alignment for AI-Based Phishing Defense

LLM phishing intersects with various ISO 27001 control domains. Focus areas include:

• A.5.23: Secure cloud service usage with XDR and SIEM monitoring.
• A.6.1: User responsibilities include advanced phishing simulation training.
• A.8.28: Test anti-phishing measures in development stages.
• A.5.25: Include LLM threats in your risk register and treatment plans.

Recommendations

1. Conduct quarterly AI-phishing simulations using tools like Cofense or KnowBe4.
2. Tune Stellar Cyber correlation rules for post-click behavior detection.
3. Enable SentinelOne rollback on endpoints of high-value personnel.
4. Run Tenable.io exposure scans after phishing tests.
5. Foster GRC-SOC collaboration to document and treat phishing risks effectively.

References