AI-enabled cyberattacks, driven by rapid advances in generative AIhave become a major concern in the cybersecurity landscape. Cybercriminals are increasingly leveraging these technologies to create sophisticated and convincing attacks that are often difficult to counter with traditional security measures.

From generating realistic phishing emails to creating sophisticated malware, Generative AI is changing the way threats are executed, making them more dangerous and harder to detect.

In this article, we explore how cybercriminals are using generative AI in their attacks and what it means for the future of cybersecurity.

How is Generative AI used for cyber attacks?

Generative AI has quickly become a powerful tool for both cybersecurity professionals and cybercriminals. The ability to generate realistic and sophisticated content has opened up new avenues for malicious activity, creating significant challenges in the cybersecurity landscape.

Here are three common ways generative AI can be used by attackers:

1. AI-generated phishing and social engineering

One of the most common applications of generative AI in cyberattacks is the creation of highly convincing phishing emails and social engineering campaigns.

AI models like GPT can generate emails that mimic the tone, style, and language of legitimate communications from trusted organizations. These AI-generated emails are often devoid of the common flaws that make traditional phishing attempts detectable, making them significantly harder to identify as fraudulent.

One example is HackerGPT, a generative AI model designed for ethical hacking; it is a test case, used to generate phishing emails that are virtually indistinguishable from legitimate communications. For example, when HackerGPT was asked to create a phishing email targeting hospital workers, it produced a highly convincing message that bypassed traditional security filters, making the “typos in phishing emails” point redundant.

2. Creation of malware and ransomware with AI

Generative AI can also be used to develop polymorphic malware: malicious software that continuously changes its code to evade detection.

AI-generated malware can be designed to adapt itself in real-time, making it nearly impossible for traditional security measures to identify and neutralize. This capability has been demonstrated in proof-of-concept attacks, such as those involving AI-generated malware like BlackMamba, which uses generative AI to create polymorphic code designed to evade endpoint detection and response (EDR) systems.

3. AI-Automated Scam and Fraud Operations

Cybercriminals are using AI to automate and scale their fraudulent activities. For example, AI-generated websites and fake reviews can trick consumers into trusting and transacting with fraudulent entities. Custom GPT models tailored to specific malicious purposes, such as generating scam websites or automating social engineering attacks, have further increased the scale of these operations.

By understanding how generative AI is used in cyberattacks, organizations can better prepare and implement robust defenses to counter these advanced threats. Integrating AI-driven threat detection tools into cybersecurity protocols is essential to staying ahead in this evolving battle between attackers and defenders.

In a previous post, we discussed in depth how AI, or Custom GPT models, can be weaponized by threat actors to commit fraud. Read it on the SOCRadar blog for more context: How Custom GPT Models Facilitate Fraud in the Digital Age.

What is AI-generated malware?

AI-generated malware represents a new era in cybersecurity threats, harnessing the power of artificial intelligence to create more sophisticated and adaptive malware.

Unlike traditional malware, which is typically static and relies on predefined code, AI-generated malware can be designed to dynamically change its behavior (becoming “polymorphic”), making it harder to detect and stop.

What is polymorphic malware?

One of the most significant examples of AI-generated malware is polymorphic malware. This type of malware can change its code structure every time it infects a new system, effectively evading traditional signature-based detection methods used by antivirus software.

The AI ​​engine behind this malware continuously rewrites its code, ensuring that each iteration is unique. For example, BlackMamba, also mentioned in an earlier section, is a polymorphic AI-generated malware that uses generative AI to create and execute multiple versions of itself, making it extremely difficult for security solutions to identify and eliminate.

Learn more about the context and how to prevent AI malware attacks in our blog, “AI vs. AI: Future of the Cybersecurity Battles.”

Key Features of AI-Aided Malware: Evasion Techniques and Autonomous Attack Strategies

AI-generated malware often uses advanced evasion techniques, such as code obfuscation, where the malware’s code is deliberately made more complex and difficult to analyze. Additionally, these malware types can use AI to mimic legitimate software behavior and blend in with normal system operations, allowing them to operate undetected. This adaptability makes AI-generated malware particularly dangerous, as it can continually evolve to evade even the most advanced security measures.

Another hallmark of AI-generated malware is its ability to autonomously strategize attacks. AI can analyze the target environment, choose the most effective attack vectors, and deploy payloads at optimal times. This level of sophistication means that AI-generated malware is not just a tool for initial infiltration, but can also manage and execute complex attack chains, from data exfiltration to launching ransomware attacks.

Mitigating the risks of AI-driven cyberattacks

To effectively mitigate the risks of AI-enabled cyber threats, organizations can implement several key strategies:

1. Continuous monitoring: Use continuous monitoring of networks and systems to detect and respond to threats in real time. SOCRadar’s platform enhances this process by providing up-to-date threat intelligence and actionable alerts.

2. Threat Detection: Deploy advanced threat detection systems that analyze behavioral patterns and identify suspicious activity early. SOCRadar’s platform integrates with existing tools, enhancing detection capabilities.
3. Regular security audits: Perform regular security audits and patch vulnerabilities quickly. Use SOCRadar’s Vulnerability Intelligence feature to stay informed about emerging threats and ensure timely remediation.

4. Advanced email and content filtering: Use advanced filtering solutions to block phishing attempts and other malicious content. SOCRadar’s Digital Risk Protection module helps monitor and protect against such threats, helping you detect fraud attempts and exposed information.

Additionally, you can use SOCRadar LABS’s free Email Analyzer feature to scan EML files for malicious content and protect yourself from phishing attacks.

5. Employee training:Provide regular training to employees on how to recognize and respond to cyber threats arising from AI, particularly those related to phishing and social engineering.

These strategies, combined with SOCRadar’s comprehensive cybersecurity solutions, create a robust defense against AI-driven threats. Furthermore, AI can be used to automate many of these defense procedures, improving efficiency and response times.

Conclusion – The Future of AI in Cybersecurity: Defense vs. Offense

As AI technology continues to develop, its role in cybersecurity will continue to evolve, with both cybercriminals and defenders leveraging its capabilities.

On the offensive side, AI is being used to create sophisticated malware, automate phishing campaigns, and execute large-scale fraud, posing significant challenges to traditional security measures. On the defensive side, however, AI-driven tools are helping organizations stay ahead of these threats by improving detection, response, and monitoring capabilities.

The future of cybersecurity is likely to see a continued arms race between these two parties, with AI playing a central role. By adopting advanced strategies and integrating solutions like SOCRadar’s, organizations can improve their defenses and remain resilient against the growing threat landscape. The balance between the offensive and defensive applications of AI will shape the future of cybersecurity.

Article Link: Examples of AI-Enabled Cyber ​​Attacks – SOCRadar® Cyber ​​​​Intelligence Inc.