How do AI-powered cyberattacks work?

AI has become a key technology in every enterprise IT toolbox, and it has also become a weapon in the arsenals of cybercriminals.

AI-powered cyberattacks use AI or machine learning (ML) algorithms and techniques to automate, accelerate, or enhance various phases of a cyberattack. This includes identifying vulnerabilities, deploying campaigns along identified attack vectors, advancing attack paths, establishing backdoors within systems, exfiltrating or tampering with data, and interfering with system operations.

Like all AI algorithms, those used in AI-powered cyberattacks can learn and evolve. This means that AI-enabled cyberattacks can adapt to avoid detection or create attack patterns that a security system can't detect.

Characteristics of AI-powered cyberattacks

AI-powered cyberattacks have five main characteristics:

• Attack automation: Growing access to AI and generative AI (GenAI) enabled tools allows adversaries to automate attack research and execution with minimal hands-on support.
• Efficient data gathering: AI automates reconnaissance, helping attackers quickly identify targets, vulnerabilities, and assets to compromise while improving the accuracy of their analysis.
• Customization: AI scrapes public sources, such as social media and corporate websites, to create hyper-personalized messages for phishing and social engineering attacks.
• Reinforcement learning: AI algorithms learn and adapt in real time, helping adversaries improve their techniques and avoid detection.
• Employee targeting: AI identifies high-value targets within organizations, such as people with access to sensitive data, broad system permissions, or relationships with other key targets.

Types of AI-powered cyberattacks

There are many different types of cyberattacks enabled by AI and machine learning, including:

AI-driven social engineering attacks

 A social engineering attack is any cyberattack that aims to manipulate human behavior to achieve a goal, such as sharing sensitive data, transferring money or ownership of high-value items, or gaining access to a system, application, database, or device.

AI-driven social engineering attacks use AI algorithms to assist with research, creative concept development, or the execution of a social engineering attack.

AI algorithms can identify ideal targets within organizations, develop personas with corresponding online presences, create realistic scenarios, and write personalized messages or multimedia assets to engage targets.

AI-driven phishing attacks

Modern phishing attacks may use GenAI to create highly personalized, realistic emails, SMS messages, phone calls, or social media outreach to achieve a desired outcome. In most cases, the goals of these attacks are the same as those of social engineering attacks: to access sensitive information, gain access to a system, obtain funds, or prompt a user to install a malicious file on their device.

In 2025, there was an 89% increase in attacks by AI-enabled adversaries year-over-year, according to the CrowdStrike 2026 Global Threat Report.

In advanced cases, AI can be used to automate real-time communication in phishing attacks. For example, interactions with AI-powered chatbots may be nearly indistinguishable from those with humans. Chatbots pose as customer support or service agents, seeking to gather personal information and account credentials, reset account passwords, or access systems and devices.

With these tools deployed at scale, attackers can attempt to connect with countless targets simultaneously. 

Vishing (voice phishing)

Vishing refers to voice phishing attacks that use phone calls or voice messages to manipulate victims. AI has made vishing dramatically more effective through voice cloning technology.

In these attacks, threat actors use publicly available audio or video footage of trusted contacts to create convincing voice clones. The AI-generated voices can then be used in real-time phone conversations or pre-recorded messages to authorize fraudulent wire transfers, request sensitive information, or manipulate employees into taking specific actions.

In May 2024, attackers impersonated a CEO using AI voice cloning technology and attempted to solicit fraudulent payments.

Deepfakes

A deepfake is an AI-generated video, image, or audio file intended to deceive people. Deepfakes commonly appear on the internet for no other purpose than to entertain and confuse. However, they can also be used more maliciously in disinformation campaigns, "fake news," smear campaigns against high-profile individuals, or cyberattacks.

In the context of a cyberattack, a deepfake is usually part of a social engineering campaign. Attackers use tools to mimic a person's likeness to authorize actions such as transferring funds, changing a password, or granting system access.

One well-publicized case of a deepfake attack involved threat actors using public footage of a company's chief financial officer and other employees to create credible deepfakes. The attackers staged a video conference call and invited an employee who could authorize financial transactions. They socially engineered the victim into transferring $25.6 million USD.

Real-time deepfake technology is a particularly dangerous tool for insider threats. Threat actors associated with DPRK-nexus adversary FAMOUS CHOLLIMA have used this technology to mask their identities during video job interviews, allowing a single operator to interview for the same position multiple times using different synthetic personas.

Adversarial AI/ML

Adversarial AI or adversarial ML is the use of manipulation or deliberate misinformation to disrupt the performance of AI/ML systems. Attackers may use several techniques targeting different areas of model development and operation:

• Poisoning attacks: Injecting fake or misleading information into the training dataset to compromise the model's accuracy or objectivity.
• Evasion attacks: Applying subtle changes to input data, causing it to be misclassified and negatively impacting the model's predictive capabilities.
• Model tampering: Making unauthorized alterations to a pre-trained model's parameters or structure to compromise its ability to create accurate outputs.
• Prompt injection attacks: Engineering prompts in an attempt to circumvent access controls, achieve code execution, or lead to unintentional disclosure of sensitive information.

Malicious GPTs

A generative pre-trained transformer (GPT) is an AI model that can generate intelligent text in response to user prompts. A malicious GPT refers to an altered version of a GPT that produces harmful or deliberately misinformed outputs.

In the context of cyberattacks, a malicious GPT can generate attack vectors (such as malware) or supporting attack materials (such as fraudulent emails or fake online content) to advance an attack.

LLMjacking

LLMjacking is an emerging attack type where threat actors exploit stolen cloud credentials to gain unauthorized access to AI services. The goal is to fuel an expanding criminal market for unauthorized large language model (LLM) queries.

In 2024, an unknown threat actor compromised a North American consulting company and prepared for LLMjacking by listing available foundational machine learning models for a cloud-based AI service. For unavailable models, the attacker attempted to gain access by using an API that enables users to request permission for restricted models. In this case and others like it, the threat actors likely intended to resell access to ML models to others seeking to use them for malicious purposes.

AI tools as attack surfaces

The increasing adoption of AI tools and agents within organizational networks has created a new attack surface for threat actors to exploit. Rather than viewing AI tools as peripheral applications, adversaries are targeting them as primary attack vectors for post-exploitation operations. Additionally, modern AI agents increasingly execute workflows, access enterprise systems, and act with user or service account permissions, making them attractive targets for prompt injection, tool manipulation, and workflow compromise.

In 2025, 90+ organizations had legitimate AI tools exploited to generate malicious commands and steal sensitive data, according to the CrowdStrike 2026 Global Threat Report.

AI-powered software supply chain attacks

A software supply chain attack occurs when adversaries upload malicious packages to public registries like npm or PyPI, waiting for organizations to unknowingly download and execute compromised code. Because package managers automatically run embedded scripts at install time, a single poisoned dependency can steal credentials or establish persistent access across an environment.

AI amplifies this threat from both directions. Adversaries use AI to generate convincing package names mimicking legitimate libraries and scale the creation of poisoned packages across registries. Meanwhile, the adoption of AI coding assistants and agentic AI applications has extended package installations well beyond developer workstations, expanding the attack surface to any endpoint running AI-powered tools.

Ransomware attacks

AI-enabled ransomware is a type of ransomware that uses AI to improve its performance or automate aspects of the attack path.

For example, AI can be used to research targets, identify system vulnerabilities, or encrypt data. AI can also be used to adapt and modify ransomware files over time, making them harder to detect with cybersecurity tools.

How to mitigate AI-powered cyberattacks

AI technology can make it easier and faster for cybercriminals to carry out cyberattacks, effectively lowering the barrier to entry for some actors and increasing the sophistication of established players. AI-powered attacks are often more difficult to detect and prevent than those using traditional techniques and manual processes, posing a significant security threat to all companies.

Recommendations to protect and defend against AI-powered cyberattacks include:

Conduct regular and frequent security assessments

Deploy a comprehensive cybersecurity platform that offers continuous monitoring, intrusion detection, and endpoint protection. Develop baselines for system activity and user behavior to establish user and entity behavior analytics (UEBA), and implement real-time analysis of input and output data for your AI/ML systems.

Develop an incident response plan

Create a document that outlines your organization's procedures, steps, and responsibilities in the event of a cyberattack. Base your plan on the NIST incident response framework, which covers preparation, detection and analysis, containment, eradication, recovery, and post-incident activity.

Train employees to raise awareness

Add a module to your existing security training that focuses specifically on AI-powered attacks, emphasizing how realistic AI-enabled techniques can be. Implement real-time deepfake challenges during interview sessions, enhance identity verification during hiring, and train teams to recognize suspicious AI/ML system activity.

Implement AI-powered solutions

Adopt AI-native cybersecurity that analyzes vast datasets to identify patterns and operationalize agentic AI systems capable of reasoning, adapting, and acting autonomously within defined guardrails. Use AI-enabled tools to automate monitoring, analysis, patching, prevention, and remediation while developing system parameters that alert teams to high-risk activity.

In addition, organizations should also deploy controls that provide visibility into AI usage, protect sensitive data flowing through AI systems, detect prompt injection and other AI-specific attacks, as well as monitor AI agent behavior as AI adoption continues to expand.

CrowdStrike's AI-native platform

The CrowdStrike Falcon® platform is cybersecurity's AI-native platform, driving the convergence of data, security, and IT through GenAI and natively built workflow automation to stop breaches, reduce complexity, and lower costs.

The platform's use of AI techniques and technology includes:

• Using AI-powered indicators of attack (IOAs) and AI-powered behavioral analysis for enhanced threat detection.
• Stopping identity-based attacks in real time with CrowdStrike Falcon® Identity Threat Detection module, thereby thwarting lateral attack movement within networks.
• Bringing AI-native capabilities to protect endpoints through extended detection and response (XDR) with CrowdStrike Falcon® Insight XDR.
• Integrating CrowdStrike® Charlotte AI™, which powers generative AI capabilities across the Falcon platform to accelerate analyst workflows, drawing on CrowdStrike's automated intelligence enriched by security experts.
• Using threat intelligence from ExPRT.AI, powered by the CrowdStrike Falcon® platform and CrowdStrike Threat Graph®, to bring technology and techniques to enterprise vulnerability management.
• Securing the entire software as a service (SaaS) stack through CrowdStrike® Falcon Shield®, which includes continuous security for SaaS GenAI implementations, helping organizations maintain threat prevention, detection, and response across all configurations and AI integrations.

Want to learn how your organization can harness the power of AI to defend against the most sophisticated cyberattacks? Contact our team of experts today to learn more about the CrowdStrike Falcon platform and schedule a demo.

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