AI Malware

What is AI malware?

AI malware is any type of malware that relies, at least in part, on artificial intelligence technology.

AI can assist in malware creation and deployment in multiple ways, such as:

• Automatically generating code for malware programs: When it becomes possible to write malware with help from AI-assisted coding tools, hackers who would not otherwise have the time or skills necessary to create malware can generate their own, custom malicious software.
• Identifying targets: AI can help attackers find the most vulnerable targets by, for example, analyzing data about users to identify accounts that are likely to have weak passwords, or parsing network traffic data to find vulnerable ports or services.
• Spreading malware: Malware developers can incorporate AI features into their software that allow the malicious code to evolve or modify its behavior automatically. This capability may help increase the ability of malware to work around mitigation measures deployed by cybersecurity teams.

To be clear, not all malware uses AI; in fact, the incorporation of AI into malware strategies is a relatively new phenomenon that didn’t become noticeable until the advent of generative AI technologies, like ChatGPT, brought the power of AI to most people’s radar screens. In addition, many of the types of malware that use AI can also be implemented in other ways. For example, while AI can help to create adaptive malware (which we’ll discuss more in a minute), it’s also possible to implement adaptive malware by simply writing code that changes malware over time, as opposed to using an AI tool or service to change the malware.

That said, AI has opened the door to powerful new malware features that could make malware more challenging to remediate, while also lowering the bar of entry for people who want to create and deploy malware.

Three types of AI malware

Major types of malware that can leverage AI include the following.

Adaptive malware

Adaptive malware is malicious software that automatically changes its behavior or appearance, typically in an effort to make it harder to block or remove the malware.

For example, imagine a malware application that changes its own source code periodically by connecting to a generative AI service, using the service to generate additional code, and recompiling itself. This type of malware would be challenging to detect consistently because every time it adapted, the file signatures and execution processes associated with the malware would change – making it difficult for vulnerability scanners to detect the malware’s signature.

Dynamic malware payloads

In a similar vein, malware programs could use AI to generate unique payloads for each device they target, another technique that hampers malware detection.

As an example, a malware program might deploy malicious code by using an AI service to create a different malicious script for every device the malware targets – as opposed to distributing the same malware payload for every target, which is the traditional approach used by malware programs to deploy malicious software.

Dynamic malware payloads would make it challenging to detect and block the malware because each payload would look different, so signature-based malware detection would not work well.

AI-powered botnets

Botnets, meaning groups of compromised devices that attackers can use to distribute malicious content or spam, can benefit from AI to optimize attack techniques and neutralize containment measures.

For instance, if an organization facing a botnet attack attempts to block the IP addresses from which the botnet is sending malicious messages, an AI-based algorithm could detect the mitigation attempt and change the IP address assignments of the machines. Likewise, botnets could use AI to modify the contents of messages they distribute, making it more challenging to detect and block malicious content due to the content’s ever-changing nature.

AI malware and Trojan AI generation tools

Threat actors could potentially use any type of AI tool or service to help create and distribute malware – including mainstream, general-purpose public AI solutions like ChatGPT and GitHub Copilot.

However, attackers seeking to streamline the AI malware process can take advantage of tools designed specifically to produce AI malware and Trojan AIs. Examples include FraudGPT and WormGPT, malicious generative AI solutions that are purpose-built for the threat actor community and are available on the Dark Web.

The availability of AI malware tools like these is part of the reason why AI has lowered the bar to malicious activity so substantially: In the age of generative AI, individuals or groups who want to launch cyberattacks don’t even need to learn how to adapt legitimate AI tools to serve their needs. They can take advantage of AI services designed to streamline illegitimate activity, and that don’t require advanced AI skills.

Effective strategies for AI malware detection

Protecting against malware in the age of AI requires more than basic malware detection techniques, like simply scanning for binaries known to contain malware. Organizations must employ advanced malware detection strategies, such as:

• Monitoring the behavior of applications and processes to uncover anomalies that could be a sign of malware. This helps to detect malware in cases where signature-based detection fails due to adaptive malware behavior or dynamic payloads.
• Monitoring user behavior, which can also identify unusual patterns that reveal the presence of malware or compromised accounts.
• Analyzing network data for connections to AI services or other unknown hosts.
• Using threat intelligence to understand the types of AI-assisted attacks threat actors are planning, and block them proactively.

Mitigating AI malware with Aqua

As an end-to-end application security platform, Aqua offers the advanced capabilities that teams need to detect and remediate even the most sophisticated AI-based malware and Trojans. Capabilities like dynamic threat analysis and advanced malware protection help identify malware threats that evade basic security scans. Plus, features like AI-guided remediation help to mitigate malware risks quickly, minimizing the harm caused by a successful malware deployment.