In early 2024, a sophisticated deep-fake scam targeted WPP, the world’s largest advertising group. Fraudsters created a WhatsApp account using the CEO’s photo and then organized a Microsoft Teams meeting in which the executives’ voices and images were convincingly cloned via artificial intelligence.
The impostors attempted to coerce an agency leader into setting up a new business and transferring funds. The fraud was stopped only thanks to the team’s vigilance. This incident illustrates a major advancement in cyber threats: when AI is used for hacking, the attacks can be super persuasive.
Threat actors are no longer limited to mass email campaigns or generic phishing links. With AI-driven tools, they can scale attacks, imitate real people in real time, and exploit trust at a deeper level, faster, broader, and more convincingly.
In this article, we’ll explain how hackers are using AI to launch advanced threats, the methods they’re using, real-world examples of AI-powered cyber attacks, and how organizations can protect themselves against these threats.
What Is AI Hacking?
In simple terms, AI hacking is the use of artificial intelligence to enhance, automate, or scale cyberattacks.
Rather than a traditional hacker manually writing each phishing email or creating malware by hand, attackers now have access to tools that can produce convincing lures and identify vulnerabilities at machine speed.
IMPORTANT: We are focusing on AI as a tool for hacking here, not AI as the target of hacking. That is, our interest lies in how threat actors use AI (and related technologies) to launch attacks, rather than how attackers may attempt to compromise an AI system itself (though that is also important). Focusing on AI as a tool for hacking helps us understand how attackers’ capabilities are changing, and how defenders (like your organization) need to adapt. |
AI Cyberattacks Are No Science Fiction
Make no mistake: AI-assisted hacking isn’t science fiction. It is already emerging in multiple forms, including phishing campaigns, automated malware generation, voice or video deepfakes, and other advanced social engineering techniques.
More and more security institutions are informing about this problem. For example, the National Cyber Security Centre (NCSC) in the UK warns that artificial intelligence “will almost certainly increase the volume and heighten the impact of cyberattacks over the next two years.” In other words, we are not waiting for this problem to appear; it is already here.
Why Hackers Are Turning to AI
Here’s why AI has become so commonly used by threat actors, and what that means for your organization.
Efficiency
As the entire phishing attack can be automated using large language models (LLMs), it’s much easier and faster to set up a campaign. LLMs enable threat actors to generate thousands of email variants in minutes, each one being tailored, unique, and ready to send.
Effectiveness
AI doesn’t just speed things up, but also helps attackers get better results by improving personalization and plausibility. They can pull in data from social media, mimic writing styles, create urgency or trust cues, and send a message that seems highly specific to the recipient. This allows for higher click-through or response rates.
Accessibility
What used to require only skilled actors and custom code is now broadly available. There are AI systems built specifically to support phishing or fraud operations, allowing attackers with lower skill levels to launch sophisticated campaigns.
Anonymity and Evasion
AI helps mask attacker signatures and evade typical defences. Polymorphic malware generation, unique message texts tailored to each target, and deepfakes in voice or video can all make traditional detection insufficient.
Logs can be manipulated, payloads tweaked, and social engineering amplified—all with minimal manual effort.
Economic Driver
According to the Harvard Business Review, the cost of launching a phishing campaign can be reduced by over 95% when using LLMs, while maintaining equal or better success rates.
Such cost efficiency makes phishing and other AI cyberattacks much more scalable, even for lower-budget attackers.
Implications for Your Organization
- The lower barrier to entry means that even less-experienced attackers can use techniques that were once the domain of advanced persistent threat (APT) groups.
- Higher personalization and scale mean your employees can be targeted with messages that feel credible and urgent, increasing the odds of falling victim.
- Traditional defenses based on detecting bulk campaigns or known malicious templates become less effective when each message is unique and generated in real time.
- Because the economics favor the attacker, expect to see more volume, greater variation, and more sophisticated campaigns in the future.
If you’re ready to explore how you can strengthen your organization’s defense and reduce risk from AI-enhanced phishing, talk to our team at VanishID.
AI-Powered Cyberattack Techniques
Artificial intelligence is transforming the way attackers operate. Instead of relying solely on manual coding or human-written messages, they now use AI to automate reconnaissance, generate phishing lures, and even create polymorphic malware that rewrites itself.
AI-Generated Phishing & Smishing
- How it works: Large language models produce fluent, contextually accurate messages in seconds. Threat actors feed an LLM target details (such as a job title, company name, or recent posts) and receive numerous tailored, multilingual, typo-free email or SMS variants. Each message can be slightly different to evade bulk-pattern detection.
- Attacker use case: A threat actor scrapes LinkedIn and other public profiles for a target’s biography, then asks an LLM to write a short email that mirrors the voice of a recruiter or partner. The messages are personalized, reference real projects, and include a convincing call to action, increasing the odds of a reply or a click.
- Why it’s dangerous: AI messages are more convincing than mass spam, often bypass grammar/spam filters, and can be produced at scale so that even low-skill attackers run sophisticated spear-phishing campaigns.
- Real-world example: In October 2025, The Guardian Nigeria reported that Microsoft had identified a significant rise in AI-enhanced phishing attacks in Africa, with threat actors using AI to write highly personalized messages in local languages. These messages impersonate trusted individuals and exploit well-known platforms, making them highly convincing and difficult to detect. Over 87,000 victims have already been scammed for $484 million in total.
Deepfakes & Voice Cloning
- How it works: Generative audio and video models can clone a person’s voice or create a realistic video from a handful of samples. Attackers pair the clone with social engineering psychology to impersonate executives, board members, or family to request wire transfers or sensitive actions.
- Attacker use case: A threat actor produces an audio clip of a CEO approving an urgent payment and then uses it during a call or uploads it to pressure an employee or finance team to transfer funds immediately. The clip can be delivered via instant messaging or played in a synchronous call.
- Why it’s dangerous: Verbal confirmation has traditionally been an important human verification step. Deepfakes undermine that trust and can pass manual checks, especially under pressure.
- Real-world example: In early 2024, a finance employee of a multinational company, Arup, received a message seemingly from their UK-based CFO, requesting a confidential transaction. To ensure its validity, the employee attended a video conference with what appeared to be the CFO and senior staff, all of whom were AI-generated deepfakes. This convincing scam resulted in multiple financial transfers totaling $25 million to the fraudsters.
Malware & Polymorphic Code Generation
- How it works: Generative AI can help attackers create or adapt malicious code fragments, obfuscate payloads, and suggest new combinations of exploits. Polymorphic techniques allow malware to change its shape or strings with each build to evade signature-based detection.
- Attacker use case: A threat actor requests code snippets that implement a particular type of persistence or obfuscation. They then combine those snippets into a payload that security tools have never seen before, increasing the chance of bypassing antivirus and sandboxing.
- Why it’s dangerous: Automated code generation speeds and facilitates the development of innovative malware, reducing the need for advanced programming skills and increasing both the variety and volume of online threats. The dark web has also seen specialized LLM variants advertised to support such activity.
- Real-world example: As WIRED and other sources inform, threat actors have developed “black-hat” LLMs and dark web services that can be used for phishing text generation and other social engineering techniques.
CAPTCHA & Security Evasion
- How it works: Modern image- and audio-based CAPTCHAs rely on tasks that AI models can now accurately solve. Advanced vision models can identify traffic signs, objects, or distorted text more reliably and faster than humans.
- Attacker use case: Automated account creation, comment spam, or scripted fraud flows often use CAPTCHA-solving to get through. Attackers integrate ML/CV models or outsourced LLM agents to clear CAPTCHAs and pretend to be human.
- Why it’s dangerous: Breaking CAPTCHAs makes a long-standing anti-automation control insufficient, facilitating credential stuffing, fake accounts creation, ticket scalping, and API abuse.
- Real-world example: In a 2024 study, researchers at ETH Zurich in Switzerland demonstrated that advanced AI bots can now defeat certain types of image-based CAPTCHAs with 100% accuracy, matching or even surpassing human performance.
Brute Force & Password Cracking
- How it works: Generative models learn common human password patterns from leaked lists and then produce high-probability guesses.
- Attacker use case: Attackers use AI-generated password lists for targeted offline cracking or online guessing attacks, combined with credential collecting from breach databases for account takeover.
- Why it’s dangerous: AI-driven password guessing increases the efficiency of cybersecurity attacks and reduces manual input. This is particularly dangerous when users reuse passwords across services.
- Real-world example: In 2025, advanced tools like Hashcat and John the Ripper integrated AI-driven modules that adapt their cracking techniques in real-time, focusing guesses based on incoming feedback from each attempt. This significantly improves success rates and reduces the time required to crack even complex passwords compared to traditional brute-force or dictionary attacks.
AI in Reconnaissance & Exploitation
- How it works: AI tools accelerate footprinting and vulnerability identification by scanning open-source code repositories, parsing cloud-storage misconfigurations, and triaging large findings to prioritize the most effective opportunities for exploitation.
- Attacker use case: An attacker feeds a public GitHub organization into a tooling pipeline that extracts secrets, finds outdated dependencies, and shows exposed cloud buckets or misconfigured CI/CD tokens. The attacker then focuses on the highest impact targets.
- Why it’s dangerous: Automation compresses reconnaissance time – a task that used to take days may now take hours or minutes.
- Real-world example: WebAsha Technologies reports that attackers now use such AI-powered tools as Shodan for scanning internet-connected devices and systems or Maltego for collecting OSINT data to establish entity relationships.
The Psychology of AI Phishing
AI-written messages are often hard to resist because they attack the same cognitive shortcuts people use every day. When an email or text reads naturally, references real work details, or perfectly imitates the tone of a known colleague, our brains automatically treat it as familiar and trustworthy.
That familiarity is combined with classic social-engineering levers (urgency, fear of missing out, authority cues) to make recipients more likely to act quickly and less likely to question the request.
Due to perfect grammar and smooth personalization, there are no typical red flags people rely on. Instead of spotting spelling errors or awkward phrasing, targets see a message that aligns with their expectations, so they lower their guard.
Likewise, voice and video deepfakes exploit the authenticity bias: humans tend to give extra credibility to information presented in a realistic vocal or visual form, especially when it appears to come from a leader.
AI does not simply automate old tricks, but combines social engineering with technical novelties, creating a double potency: fast, highly credible attacks that reach many targets with a very high chance of succeeding. That makes human risk the weak link in modern defenses.
VanishID’s approach recognizes that technical security measures alone are not enough. Reducing human risk means minimizing the amount of exposed, exploitable personal and organizational data attackers can use to prepare convincing campaigns.
We offer comprehensive digital executive protection, including proactive data removal, dark-web monitoring, and identity risk alerts to reduce the raw material that threat actors look for.
How Organizations Can Defend Themselves From AI Hacking
Defending against AI-driven cyber threats requires a dual strategy: strengthening individual awareness and reinforcing enterprise-wide resilience.
Individual Defenses
- Healthy skepticism: “Don’t trust, always verify.” Every message, no matter how genuine it seems, should be read carefully and verified if there are even the slightest doubts they are genuine. AI makes deception sound flawless, so don’t let polished language or a familiar tone deceive you.
- Always confirm wire or data requests through secondary channels. If you receive an urgent transfer or credentials request (even from a person with high authority), verify it by calling the sender directly or using a different communication channel.
- Beware of voice or video authenticity. Deepfakes are getting alarmingly common. Treat unusual video calls or voice messages with suspicion, especially when they involve sensitive information or financial transactions.
- Slow down—time is your ally. AI phishing relies on urgency. Pausing to verify, even for a minute, often breaks the psychological momentum of an attack.
Enterprise Defenses
- Privacy & Footprint Reduction: Limit the personal and company data available to attackers. Services like VanishID’s workforce protection make it harder for AI tools to gather details that personalize phishing lures. The less personal information available online, the weaker an AI’s social-engineering advantage.
- Dark Web Monitoring: AI attacks often begin with stealing sensitive data. Continuous dark web monitoring helps detect compromised credentials or leaked employee details before they’re used in targeted phishing or credential-stuffing campaigns.
- Anti-Smishing & Impersonation Detection: Use specialized SMS and email protection for executives and finance teams. AI-driven filters can flag messages that imitate internal senders, spoof domains, or mimic executive tone patterns.
- Awareness Training 2.0: Go beyond outdated phishing tests. Simulate AI-generated attacks that use clean grammar, personalization, and multi-language content so that employees can be aware of how modern threats work.
- Phishing-Resistant MFA: Adopt authentication methods like FIDO2 security keys or passkeys that are resistant to phishing and session hijacking.
Proactive Security: AI vs. AI
The next frontier in defense is using AI to counter AI. Detection systems powered by machine learning can identify deepfakes, spot subtle linguistic anomalies, and flag spear-phishing attempts faster than humans can.
Behavioral AI can learn normal user patterns and raise alerts when communication style or login behavior is different from the standard.
In short, organizations should fight fire with fire, using advanced AI solutions to detect synthetic content, automate analysis, and prevent phishing scams.
Pro Tip: Pair technological defense with proactive data security protection. VanishID helps organizations reduce their employees’ digital exhaust in real-time, making it harder for attackers’ AI tools to find data necessary to prepare convincing campaigns. |
VanishID’s Role in Reducing AI Attack Success
- Human attack surface protection — executive protection plus family coverage to keep high-risk targets and their close contacts safe from doxxing and targeted social engineering.
- Data broker removals — removing personal and corporate records from people-search sites and brokers to reduce the raw material available for attackers to feed into LLMs.
- Credential and dark-web monitoring — continuous scans for leaked credentials, emails, and mentions so you get early warning before stolen data is weaponized in AI phishing campaigns.
- Anti-smishing and impersonation detection — mobile-first protections that flag SMS, WhatsApp, and messaging attacks that AI can produce at scale, protecting employees and customers on handheld devices.
- Rapid response and remediation — in the event of a suspected AI cyberattack, VanishID helps you act quickly and take all possible steps to prevent damage before it’s too late.
Note: VanishID is not a cure for AI hacking itself. Instead, we make such scams far less convincing and far less likely to succeed by reducing the attack surface, detecting leaks early, and enhancing the human defenses that AI hackers try to exploit. |
The Future of Hacking with AI
The line between human and machine-driven attacks is blurring fast. Over the next few years, we can expect to see autonomous AI agents capable of planning and executing multi-step intrusions with minimal human input. These systems can analyze networks, exploit vulnerabilities, move laterally, and even create phishing messages or deepfake videos on demand.
Beyond corporate breaches, AI can also play a central role in disinformation and election manipulation. Synthetic media can spread convincing false narratives across social media, spread controversial content, and impersonate trusted public figures. As generative models become increasingly sophisticated, distinguishing truth from fabrication will become more and more challenging.
On the defensive side, the race is underway to counter these capabilities. Emerging safeguards such as AI content labeling, digital watermarking, and continuous privacy management aim to restore trust in information authenticity and limit data exposure. Organizations that adopt these measures early will be better positioned to verify what’s real, protect what’s private, and identify AI-generated threats before they escalate.
For businesses, the takeaway is clear: AI will continue to empower both online threats and the defense against them. To stay safe, you need to combine advanced data security solutions with ongoing human risk protection — exactly what VanishID can provide your business with.
The Final Note
AI is now commonly used for hacking, as it enables attackers to conduct smarter, faster, and more difficult-to-detect phishing scams. Thanks to AI-generated spear-phishing emails, deepfake voice calls, and other advanced technologies, threat actors need fewer skills and can achieve better results in a shorter time.
Fortunately, organizations are far from powerless. By reducing the amount of exposed personal and corporate data, training employees to recognize AI giveaways, and implementing proactive protection such as dark web monitoring and phishing-resistant authentication, it’s possible to stay one step ahead.
The future of cybersecurity depends on both intelligent technology and well-informed people.
Get a Complimentary Risk Analysis and learn how VanishID solutions help keep personal and location details out of attackers’ hands, and make AI hacking attempts far less convincing and far less successful.
