Hackers Use Generative AI to Build Custom Malware and Evade Traditional Security Detection

Cybercriminals are increasingly using generative artificial intelligence (AI) to accelerate malware development, create convincing phishing campaigns, and identify valuable data after gaining access to a victim’s network. The trend is putting pressure on traditional security systems that rely heavily on known malware signatures.

In a security analysis published on October 16, 2026, Zscaler principal information security researcher Phil Hagen explained how generative AI is changing ransomware operations. The research describes how attackers can use AI throughout different stages of an attack, from social engineering to custom script development and data discovery.

The concern is not that every AI-generated program automatically bypasses security tools. Rather, attackers can produce and modify tools more quickly, making detection based exclusively on previously identified malware patterns less reliable.

How Hackers Use Generative AI to Create Custom Malware

Traditional antivirus and endpoint security systems often use signatures, hashes, and other known indicators to recognize malicious files. These techniques remain useful, particularly when threats have already been identified and documented.

However, attackers can use generative AI to create customized scripts and modify existing tools, producing variations that may not match previously recorded signatures. This makes it harder for defenders to rely on a single indicator when identifying suspicious activity.

According to Zscaler, AI also reduces the need for ransomware operators to depend on widely used offensive tools that security teams already monitor. Groups with fewer development resources may now be able to produce bespoke scripts more efficiently.

This development also complicates threat attribution. Custom tooling has sometimes been treated as a sign of a sophisticated or well-funded attacker. As AI-assisted development becomes more accessible, unique tools alone may provide less reliable evidence of an attacker’s skill level or identity.

It is important to distinguish AI-assisted malware development from malware that uses AI while executing. The Zscaler analysis discusses faster script creation and iteration; it does not establish that every AI-assisted payload independently rewrites its own code during an attack.

AI Makes Phishing Emails More Convincing

Generative AI is also changing the social-engineering techniques used to gain initial access to corporate systems.

Attackers can use language models to produce polished emails, realistic website content, and messages written naturally in a target’s language. These capabilities reduce some of the obvious spelling and grammar mistakes that security-awareness training has traditionally taught employees to recognize.

As a result, an email that appears professional may still be malicious. Employees and security teams must consider the context of a message, the legitimacy of its requests, and the destination of its links rather than relying on writing quality alone.

Zscaler also describes phishing scenarios in which users are persuaded to run remote monitoring and management (RMM) software. These tools have legitimate business purposes, but unauthorized use can give an attacker control over a compromised system and potentially provide a route to other resources.

Organizations should therefore monitor unexpected software installations, unusual remote-access activity, and requests that pressure employees into running unfamiliar tools.

Generative AI Can Accelerate Data Discovery

After gaining initial access, attackers need to identify which information is valuable. This can involve searching documents, reviewing accessible files, and locating sensitive business or customer information.

Zscaler reports that generative AI can help classify and search large amounts of data, potentially accelerating this stage of a ransomware attack. Rather than relying only on traditional scripts and predefined playbooks, attackers may use AI-assisted analysis to prioritize material for theft or encryption

This can increase the pressure on organizations facing extortion. Stolen information may be used to threaten public disclosure even when an organization can restore its systems from backups.

The potential impact extends beyond operational disruption. Exposed customer records, confidential business documents, and intellectual property can create privacy, financial, and reputational consequences.

For defenders, the key issue is response time. If attackers can identify sensitive information more quickly, security teams may have less time to detect suspicious access and prevent data exfiltration.

Why Traditional Security Detection Is Not Enough

Signature-based detection remains an important security control, but it should not be the only method used to identify attacks.

A previously unseen script may lack a known malicious hash while still exhibiting suspicious behavior. For this reason, organizations should combine signature-based controls with behavioral analysis, endpoint monitoring, network telemetry, and identity security.

Security teams should pay particular attention to:

• Unusual processes and unexpected script execution.
• Suspicious use of remote-access and administrative tools.
• Unusual authentication patterns or access to sensitive resources.
• Unexpected increases in data transfers or access to large numbers of files.
• Attempts to disable security controls or interfere with monitoring.
• Connections to suspicious infrastructure identified by trusted threat-intelligence sources.

These signals do not individually prove malicious activity. Their value comes from correlating multiple events, examining the surrounding context, and investigating deviations from normal behavior.

How Organizations Can Defend Against AI-Assisted Ransomware

Organizations can reduce exposure by strengthening controls at several stages of the attack lifecycle.

1. Improve phishing resistance. Train employees to verify unusual requests, report suspicious messages, and avoid running software at the direction of an unverified contact. Multi-factor authentication can help reduce the risk of compromised passwords, although it does not prevent every form of account takeover.

2. Monitor behavior, not just file signatures. Use endpoint detection and response, centralized logging, and behavioral analytics to identify suspicious execution, privilege changes, and unexpected access to sensitive files.

3. Restrict access to sensitive information. Apply least-privilege access controls so users and applications can access only the resources required for their work. This can limit the amount of data available to an attacker who compromises one account.

4. Control remote-management tools. Maintain an inventory of approved RMM software, restrict its use to authorized personnel, and investigate unexpected installations or remote sessions.

5. Strengthen data protection. Maintain isolated or otherwise protected backups, test restoration procedures, and monitor unusual data movement. Backups can support recovery from encryption, but they do not reverse the exposure of information already stolen.

6. Prepare for rapid incident response. Establish clear procedures for isolating affected systems, preserving evidence, disabling compromised accounts, and coordinating recovery. Regular exercises can help teams act decisively when an incident occurs.

7. Use AI defensively with appropriate oversight. AI-assisted security tools may help analysts summarize alerts, correlate events, and prioritize investigations. Their results should be validated, and sensitive information should be handled according to organizational policy.

Zscaler describes AI-assisted detection and the integration of security telemetry as potential defensive measures. These approaches can improve visibility and response, but no single product or detection method guarantees protection against every AI-enabled attack.

Indicators of Compromise (IoCs)

The supplied research does not identify a specific new malware family or provide campaign-specific hashes, malicious domains, IP addresses, or file paths.

Because the report describes broader attacker methods rather than one specific campaign, defenders should not treat generic behaviors as definitive indicators of compromise. Detection should instead combine behavioral evidence with validated threat intelligence and the organization’s own telemetry.

Conclusion

Generative AI is making it easier for cybercriminals to create custom scripts, develop convincing phishing material, and search compromised environments for valuable information. These capabilities can shorten parts of the ransomware attack lifecycle and reduce the effectiveness of security strategies that depend solely on known malware signatures.

However, AI does not make every attack undetectable. Behavioral monitoring, identity protection, least-privilege access, strong backup practices, and timely incident response remain important defensive measures.

The priority for security teams is to detect suspicious activity as early as possible, limit the access available to compromised accounts, and reduce the time attackers have to steal or encrypt sensitive data.