AI Hacking: How Hackers Use Artificial Intelligence in Cyberattacks

AI hacking is the use of artificial intelligence to enhance or automate cyberattacks. It allows threat actors to generate code, analyze systems, and evade defenses with minimal manual effort. 

AI models, especially large language models (LLMs), make attack development faster, cheaper, and more accessible to less experienced hackers. The result is a new set of AI attacks which is faster, more scalable, and often harder to stop with traditional defenses.  

AI threat actors now use artificial intelligence to launch faster, smarter, and more adaptive cyberattacks. From malware creation to phishing automation, AI is transforming cybercrime into a scalable operation. It can now generate malicious code, write persuasive phishing content, and even guide attackers through full attack chains.

Cybercriminals use AI in several core areas:

• Payload generation: Tools like HackerGPT and WormGPT can write obfuscated malware, automate evasion tactics, and convert scripts into executables. These are examples of generative AI attacks, often seen in AI agent cyberattacks where models make autonomous decisions.
• Social engineering: AI creates realistic phishing emails, clones voices, and generates deepfakes to manipulate victims more effectively.
• Reconnaissance and planning: AI speeds up target research, infrastructure mapping, and vulnerability identification.
• Automation at scale: Attackers use AI to launch multistage campaigns with minimal human input.

According to the Ponemon Institute, AI has already been used in ransomware campaigns and phishing attacks that caused major operational disruptions, including credential theft and the forced shutdown of over 300 retail locations during a 2023 breach. 

Several emerging patterns are reshaping the threat landscape:

• Democratization of cyberattacks: Open-source models like LLaMA, fine-tuned for offense, are now available to anyone with local computing power
• Lower barrier to entry: What once required expert skills can now be done with simple prompts and a few clicks
• Increased evasion success: AI-generated malware is better at hiding from static detection, sandboxes, and even dynamic analysis
• Malware-as-a-Service (MaaS) models: Cybercriminals are bundling AI capabilities into subscription-based kits, making it easier to launch complex attacks at scale
• Use of AI in real-world breaches:  The Ponemon Institute reports that attackers have already used AI to automate ransomware targeting, including in incidents that forced shutdowns of major operations

AI-enabled phishing and social engineering with AI are transforming traditional scams into scalable, personalized attacks that are harder to detect. Threat actors now use generative models to craft believable emails, clone voices, and even produce fake video calls to manipulate their targets.

Unlike traditional scams, AI-generated phishing emails are polished and convincing. Tools like ChatGPT and WormGPT produce messages that mimic internal communications, customer service outreach, or HR updates. When paired with breached data, these emails become personalized and more likely to succeed.

AI also powers newer forms of social engineering: 

• Voice cloning attacks mimic executives using short audio samples to trigger urgent actions like wire transfers
• Deepfake attacks simulate video calls or remote meetings for high-stakes scams

In a recent incident, attackers used AI-generated emails during a benefits enrollment period, posing as HR to steal credentials and gain access to employee records. The danger lies in the illusion of trust. When an email looks internal, the voice sounds familiar, and the request feels urgent, even trained staff can be deceived. 

AI is accelerating how attackers find and exploit software vulnerabilities. What once took days of manual probing can now be done in minutes using machine learning models trained for reconnaissance and exploit generation.

Threat actors use AI to automate vulnerability scanning across public-facing systems, identifying weak configurations, outdated software, or unpatched CVEs. Unlike traditional tools, AI can assess exposure context to help attackers prioritize high-value targets. 

Common AI-assisted exploitation tactics include:

• Automated fuzzing to uncover zero-day vulnerabilities faster
• Custom script generation for remote code execution or lateral movement
• Password cracking and brute force attacks optimized through pattern learning and probabilistic models 
• Reconnaissance bots that scan networks for high-risk assets with minimal noise

Generative models like LLaMA, Mistral, or Gemma can be fine-tuned to generate tailored payloads, such as shellcode or injection attacks, based on system-specific traits, often bypassing safeguards built into commercial models.

The trend is clear: AI enables attackers to discover and act on vulnerabilities at machine speed. According to the Ponemon Institute, 54% of cybersecurity professionals rank unpatched vulnerabilities as their top concern in the age of AI-powered attacks.

AI cyberattacks are no longer theoretical; they’ve already been used to steal data, bypass defenses, and impersonate humans at scale.  

The examples in the three notable categories below show that attackers no longer need advanced skills to cause serious damage in cyberattacks. AI lowers the barrier to entry while increasing attack speed, scale, and stealth. Defenders must adapt by proactively testing AI-integrated systems and deploying safeguards that account for both technical manipulation and human deception.

Prompt injection is a technique where attackers exploit vulnerabilities in LLMs (large language models) by feeding them specially crafted inputs designed to override intended behavior. These attacks exploit the way LLMs interpret and prioritize instructions, often with little or no traditional malware involved. 

Rather than using AI to launch external attacks, prompt injection turns an organization’s own AI systems into unwitting agents of compromise. If an LLM is embedded in tools like help desks, chatbots, or document processors without proper safeguards, an attacker can embed hidden commands that the model then interprets and acts upon. This can result in:

• Leaking private or restricted data
• Executing unintended actions (e.g., sending emails, altering records)
• Manipulating output to spread misinformation or trigger follow-on actions

These risks escalate in systems where multiple models pass information between each other. For example, a malicious prompt embedded in a document could influence an AI summarizer, which then passes flawed insights to downstream systems. 

As AI becomes more integrated into business workflows, attackers increasingly target these models to compromise trust, extract data, or manipulate decisions from the inside out.

Deepfake attacks use AI-generated audio or video to impersonate people in real time. Paired with social engineering tactics, these tools are being used for fraud, credential theft, and unauthorized system access. 

Voice cloning has become especially dangerous. With just a few seconds of recorded speech, threat actors can generate audio that mimics tone, pacing, and inflection. These cloned voices are then used to:

• Impersonate executives or managers during urgent calls
• Trick employees into wire transfers or password resets
• Bypass voice authentication systems

Deepfakes take this one step further by generating synthetic videos. Threat actors can simulate a CEO on a video call requesting confidential data—or appear in recorded clips “announcing” policy changes that spread disinformation. The Ponemon Institute reported an incident where attackers used AI-generated messages impersonating HR during a company’s benefits enrollment period which has led to credential theft. 

As AI tools become more accessible, even small-time threat actors can generate high-fidelity impersonations. These attacks bypass traditional spam filters or endpoint defenses by exploiting trust. 

Classifier model attacks manipulate the inputs or behavior of an AI system to force incorrect decisions without necessarily writing malware or exploiting code.  These tactics fall under the broader category of adversarial machine learning.  

There are two primary strategies:

• Evasion attacks: The attacker crafts an input that fools a classifier into misidentifying it, such as malware that mimics harmless files to bypass antivirus engines 
• Poisoning attacks: A model is trained or fine-tuned on deliberately skewed data, altering its ability to detect threats 

Supervised models often "overfit": they learn specific patterns from training data and miss attacks that deviate just slightly. Attackers exploit this by using tools like WormGPT (an open-source LLM fine-tuned for offensive tasks) to create payloads just outside known detection boundaries.

This is the machine-learning version of a zero-day exploit.

AI hackers use AI to automate, enhance, and scale AI cyberattacks. In contrast, traditional hacking often requires manual scripting, deep technical expertise, and significant time investment. The fundamental difference lies in speed, scalability, and accessibility: even novice attackers can now launch sophisticated AI-powered cyberattacks with a few prompts and a consumer-grade GPU.

Many organizations are turning to AI-assisted red teaming to test their defenses against LLM misuse, prompt injection, and classifier evasion. These simulations help uncover vulnerabilities in AI-integrated systems before attackers do. 

Red teams use adversarial prompts, synthetic phishing, and AI-generated payloads to evaluate whether systems are resilient or exploitable. AI security testing also includes: 

• Fuzzing models for prompt injection risks 
• Evaluating how LLMs handle manipulated or chained inputs 
• Testing classifier robustness to evasive behavior 

Security leaders are also starting to scan their own LLMs for prompt leakage, hallucinations, or unintended access to internal logic, which is a crucial step as generative AI is embedded into products and workflows.

Instead of trying to “fight AI with AI” in a reactive loop, Martin Kallas argues for a multi-layered, proactive defense. OPSWAT solutions combine technologies that target the full threat lifecycle:

• Metascan™ Multiscanning: Runs files through multiple antivirus engines to detect threats missed by single-AV engine systems 
• MetaDefender Sandbox™: Analyzes file behavior in isolated environments, even detecting AI-generated payloads that evade static rules 
• Deep CDR™: Neutralizes threats by rebuilding files into safe versions, stripping out embedded exploits 

This layered security stack is designed to counter the unpredictable nature of AI-generated malware, including polymorphic threats, in-memory payloads, and deepfake lures that bypass traditional tools. Because each layer targets a different stage of the attack chain, it only takes one to detect or neutralize the threat and collapse the entire operation before it can do damage.

The FBI and other U.S. agencies are increasingly integrating artificial intelligence into their cyber defense operations not only to triage and prioritize threats, but also to enhance data analysis, video analytics, and voice recognition.

According to the FBI, AI helps process large volumes of data to generate investigative leads, including vehicle recognition, language identification, and speech-to-text conversion. Importantly, the bureau enforces strict human oversight: all AI-generated outputs must be verified by trained investigators before any action is taken. 

AI is not a replacement for human decision-making. The FBI stresses that a human is always accountable for investigative outcomes, and that AI must be used in a way that respects privacy, civil liberties, and legal standards.