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Weaponized GenAI: how cybercriminals are using GPT models to commit fraud, and what you can do about it

  • By Gcore
  • 5 min read
Weaponized GenAI: how cybercriminals are using GPT models to commit fraud, and what you can do about it

Cybercrime has reached a whole new dimension. Generative AI models stopped being just a tool of innovation and have become strong facilitators of deception and fraud. Think of phishing emails so spot-on that they are, for all intents and purposes, indistinguishable from personal writings, or video scams with deepfake technology bringing existing or fabricated identities to life. These threats just scratch the surface of what organizations must evolve to contend with. Those that don’t will be outmaneuvered by attackers who use AI to manipulate the vulnerabilities in human perception, digital infrastructure, and trust.

How weaponized GenAI works

Generative AI (GenAI) is a subset of artificial intelligence that creates new content—text, images, videos, and more—based on training data. Unlike traditional AI, which follows pre-programmed rules, GenAI can produce highly realistic and adaptable outputs, making it a powerful tool for legitimate innovation but also for malicious purposes. Cybercriminals exploit these capabilities to craft convincing scams, generate fake identities, and automate attacks that target human vulnerabilities at scale.

Attackers use GenAI in the following ways:

  • Malware and exploit development: GenAI can help attackers write or improve malicious code, making malware more effective or harder to detect. It can also assist in creating polymorphic malware that changes its appearance to evade detection by antivirus software.
  • Web application exploitation: GenAI can automate the process of finding vulnerabilities in web applications, such as SQL injection or cross-site scripting (XSS). It can then generate complex and tailored payloads to exploit specific vulnerabilities.
  • Password and CAPTCHA bypass: AI models trained on leaked password datasets can predict likely passwords for specific targets. AI can also analyze and bypass CAPTCHA systems meant to differentiate bots from humans.
  • Evasion techniques: AI can design payloads that evade intrusion detection and prevention systems (IDPS) and can automate scripts for botnets that rotate IPs dynamically to avoid detection.

The operational advantages of GenAI for cybercriminals are staggering and include:

  • Scalability: AI can generate thousands of personalized phishing messages in seconds, adapting content dynamically based on recipient responses.
  • Accessibility: Cybercriminals now have access to “Phishing-as-a-Service” (PhaaS) platforms that integrate AI tools, lowering the barrier to entry for less skilled actors.
  • Believability: The precision of AI-generated content eliminates the grammatical and contextual errors that once gave away scams.
  • Undetectability: AI models are even being trained to bypass CAPTCHA tests, simulate human interaction patterns, and evade detection mechanisms by constantly evolving their tactics.

3 major AI-driven cybercrime threats

Models such as ChatGPT have acted as force multipliers on cybercrime. Before GenAI was widely available, human limitations to scalability and precision included the need for labor, time, and technical specialism. But with GenAI, these factors are irrelevant. The result is a paradigm shift across a number of attack vectors, in particular phishing, deepfakes, and fake identities.

Personalized phishing campaigns

Social engineering attacks have become more precise because, with GenAI, it’s possible to craft messages that are practically indistinguishable from real communications. Attackers take advantage of publicly available information from sources like LinkedIn profiles, breached databases, and corporate press releases to create contextually correct, extremely convincing phishing emails.

Deepfake audio and video threats

The rise in popularity of valid accounts as an attack vector highlights the danger of manipulative AI technology such as deepfakes. Deepfake technology is a subset of generative AI that enables the creation of highly convincing audio and video clips of individuals, often targeting executives or public figures, to facilitate fraud, such as fund transfers or data theft. It has now reached alarming levels of sophistication.

In one notable case from early 2024, a finance employee at a multinational corporation transferred $25 million to scammers after deepfake technology was used to impersonate the company’s chief financial officer during a video call. This example illustrates the sophistication of such attacks and their potential to undermine even tightly controlled corporate processes.

Deepfakes can be deployed to discredit organizations, spread misinformation, or manipulate markets. Their ability to bypass traditional verification methods creates a serious challenge for existing cybersecurity frameworks.

Fake identities and synthetic content

Cybercriminals increasingly use AI to create synthetic identities, blending fake and real data to craft convincing personas with AI-generated photos, names, and backstories. These fake identities bypass verification systems, such as Know Your Customer (KYC) checks, to open fraudulent accounts, apply for loans, or steal benefits. Attackers also bolster their schemes with AI-generated documents, reviews, and testimonials, adding credibility to their scams and making detection exceedingly difficult.

Relatedly, GenAI enables the creation of realistic fake content at scale, from counterfeit IDs to glowing customer reviews. With these tools, criminals infiltrate online communities, build trust, and execute scams ranging from phishing campaigns to e-commerce fraud. These synthetic entities can impersonate real people, manipulate social proof, and evade standard detection methods, which are often not equipped to identify subtle AI-generated inconsistencies.

Countermeasures against AI-driven cybercrime

Countering AI threats effectively requires a multifaceted strategy that combines advanced technology, comprehensive training, and ongoing adaptability to address the risks posed by weaponized generative AI. This can be accomplished by improving employee training, strengthening identity verification, proactively using AI-powered cybersecurity solutions, and conducting continuous monitoring.

Improve employee training programs

Human error is still one of the leading causes of successful cyberattacks. Employees should be empowered to identify AI-powered scams, which mostly have more subtle signs of fraud. Areas of attention should include the following:

  • Ways to spot phishing attempts that are grammatically perfect and contextually relevant
  • Signs of deepfake audio or video, such as inconsistencies in visual fidelity or unnatural speech patterns
  • Review of reporting mechanisms of suspicious activity for further investigation
  • Simulated phishing tests to enhance employee preparedness by exposing them to increasingly sophisticated scenarios

Strengthen identity verification systems

Deepfake and synthetic identity attacks are advanced forms of cybercrime that exploit AI-generated content to deceive and manipulate. Deepfake attacks use AI to create highly realistic but fake videos, audio, or images that impersonate real individuals. For example, an attacker might generate a video of a CEO authorizing a fraudulent transaction, tricking employees or systems into compliance. Synthetic identity attacks involve creating entirely fake identities by combining real and fabricated information, such as blending stolen Social Security numbers with false names or addresses. These synthetic identities are then used to commit fraud, evade detection, or exploit systems.

To defend against these AI threats, organizations must adopt stronger identity verification protocols. Start with biometric authentication, such as facial recognition or fingerprint scanning, which verifies identity by matching unique physical traits. Enhance this with behavioral biometrics, which monitors patterns like typing speed, mouse movements, and device usage to detect anomalies. Together, these methods make it significantly harder for GenAI-powered attacks to succeed.

Leveraging AI in cybersecurity

Organizations can turn the tables on attackers by deploying AI-powered defense mechanisms. Read more about why AI-powered cybersecurity is the answer to AI-powered attacks in our dedicated blog post.

Some of the benefits of using AI in cybersecurity to counter weaponized GenAI are as follows:

  • Real-time threat detection: Advanced machine learning models in network traffic and user behavior continuously analyze and identify deviations that may pass through the traditional monitoring systems. The models are good at finding minute deviations from normal patterns, thus enabling early detection of potential breaches.
  • Email and content filtering: AI-powered systems scan the content of e-mails, syntax, semantics, and metadata for any phishing attempts or malicious payloads. The solution identifies the fraudulent element with accuracy and thus minimizes the chance of falling prey to deceiving communication.
  • Automated incident response: AI-powered automation streamlines threat response time by taking direct action, enabling systems to isolate resources immediately or block malicious traffic. Containment speed reduces the breach impact and limits an attacker’s ability to escalate the attack.

When integrated into existing security infrastructures, these AI-driven solutions further enable organizational resilience and give organizations the tools to respond to evolving threats more quickly and efficiently.

Continuous monitoring

AI-driven cybercrime evolves at a pace that outstrips traditional security systems, rendering static defenses ineffective against these rapidly mutating attack vectors. To keep up, organizations must adopt dynamic strategies, including continuous monitoring across social media networks, app marketplaces, and other external digital platforms. These efforts aim to preempt threats, providing early warnings and intercepting potential attacks before they can escalate.

Advanced brand monitoring tools play a critical role by detecting fraudulent activities that misuse company names, logos, or domains. These tools quickly identify and flag phishing emails, counterfeit websites, or other impersonation attempts, enabling swift removal and minimizing risks to customers and brand reputation. In addition, threat intelligence platforms leverage data-driven insights to counter emerging attacks such as AI-generated deepfakes.

Prepare for the future with Gcore Edge Security

With the rapid development of generative AI technology, threats will keep changing. Organizations should be agile and invest in systems and processes that can keep pace with adversaries. Businesses can reduce risks and uphold trust with customers and partners by creating a culture of vigilance, integrating advanced technologies, and focusing on continuous improvement.

Our WAAP (web application and API protection) solution empowers organizations to stay ahead of growing AI challenges. With features specifically designed to find and neutralize AI-driven threats in real time, we give businesses the power to protect themselves and their reputation in a hostile digital landscape.

Explore our AI-enhanced WAAP solution

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Building a startup is tough. Every decision about your infrastructure can make or break your speed to market and burn rate. Your time, team, and budget are stretched thin. That’s why you need a partner that helps you scale without compromise.At Gcore, we get it. We’ve been there ourselves, and we’ve helped thousands of engineering teams scale global applications under pressure.That’s why we created the Gcore Startups Program: to give early-stage founders the infrastructure, support, and pricing they actually need to launch and grow.At Gcore, we launched the Startups Program because we’ve been in their shoes. We know what it means to build under pressure, with limited resources, and big ambitions. We wanted to offer early-stage founders more than just short-term credits and fine print; our goal is to give them robust, long-term infrastructure they can rely on.Dmitry Maslennikov, Head of Gcore for StartupsWhat you get when you joinThe program is open to startups across industries, whether you’re building in fintech, AI, gaming, media, or something entirely new.Here’s what founders receive:Startup-friendly pricing on Gcore’s cloud and edge servicesCloud credits to help you get started without riskWhite-labeled dashboards to track usage across your team or customersPersonalized onboarding and migration supportGo-to-market resources to accelerate your launchYou also get direct access to all Gcore products, including Everywhere Inference, GPU Cloud, Managed Kubernetes, Object Storage, CDN, and security services. They’re available globally via our single, intuitive Gcore Customer Portal, and ready for your production workloads.When startups join the program, they get access to powerful cloud and edge infrastructure at startup-friendly pricing, personal migration support, white-labeled dashboards for tracking usage, and go-to-market resources. Everything we provide is tailored to the specific startup’s unique needs and designed to help them scale faster and smarter.Dmitry MaslennikovWhy startups are choosing GcoreWe understand that performance and flexibility are key for startups. From high-throughput AI inference to real-time media delivery, our infrastructure was designed to support demanding, distributed applications at scale.But what sets us apart is how we work with founders. We don’t force startups into rigid plans or abstract SLAs. We build with you 24/7, because we know your hustle isn’t a 9–5.One recent success story: an AI startup that migrated from a major hyperscaler told us they cut their inference costs by over 40%…and got actual human support for the first time. What truly sets us apart is our flexibility: we’re not a faceless hyperscaler. We tailor offers, support, and infrastructure to each startup’s stage and needs.Dmitry MaslennikovWe’re excited to support startups working on AI, machine learning, video, gaming, and real-time apps. Gcore for Startups is delivering serious value to founders in industries where performance, cost efficiency, and responsiveness make or break product experience.Ready to scale smarter?Apply today and get hands-on support from engineers who’ve been in your shoes. If you’re an early-stage startup with a working product and funding (pre-seed to Series A), we’ll review your application quickly and tailor infrastructure that matches your stage, stack, and goals.To get started, head on over to our Gcore for Startups page and book a demo.Discover Gcore for Startups

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Organizations must rethink cloud security from the ground up, prioritizing speed, automation, and continuous monitoring.Solution #1: AI-powered threat detection forsmarter defensesModern threats evolve faster than any manual security process can track. Rule-based defenses simply can’t adapt fast enough.The solution? AI-driven threat detection.Instead of relying on static rules, AI models monitor massive volumes of data in real time, spotting subtle anomalies that signal an attack before real damage is done. For example, an AI-based platform can detect an unauthorized process in a container trying to access confidential data, flag it as suspicious, and isolate the threat within milliseconds before attackers can move laterally or exfiltrate information.This proactive approach learns, adapts, and neutralizes new attack vectors before they become widespread. By continuously monitoring system behavior and automatically responding to abnormal activity, AI closes the gap between detection and action, critical in cloud-native, regulated environments where even milliseconds matter.Solution #2: Zero trust as the new security baseline“Trust but verify” no longer cuts it. In a cloud-native world, the new rule is “trust nothing, verify everything”.Zero-trust security assumes that threats exist both inside and outside the network perimeter. Every request—whether from a user, device, or application—must be authenticated, authorized, and validated.In distributed architectures, zero trust isolates workloads, meaning even if attackers breach one component, they can’t easily pivot across systems. Strict identity and access management controls limit the blast radius, minimizing potential damage.Combined with AI-driven monitoring, zero trust provides deep, continuous verification, blocking insider threats, compromised credentials, and advanced persistent threats before they escalate.Solution #3: Automated security policies for scalingprotectionManual security management is impossible in dynamic environments where thousands of containers and microservices are spun up and down in real time.Automation is the way forward. AI-powered security policies can continuously analyze system behavior, detect deviations, and adjust defenses automatically, without human intervention.This eliminates the lag between detection and response, shrinks the attack window, and drastically reduces the risk of human error. It also ensures consistent security enforcement across all environments: public cloud, private cloud, and on-premises.For example, if a system detects an unusual spike in API calls, an automated security policy can immediately apply rate limiting or restrict access, shutting down the threat without impacting overall performance.Automation doesn’t just respond faster. It maintains resilience and operational continuity even in the face of complex, distributed threats.Unifying security across cloud environmentsSecuring distributed workloads isn’t just about having smarter tools, it’s about making them work together. Different cloud platforms, technologies, and management protocols create fragmentation, opening cracks that attackers can exploit. Security gaps between systems are as dangerous as the threats themselves.Modern cloud-native security demands a unified approach. Organizations need centralized platforms that pull real-time data from every endpoint, regardless of platform or location, and present it through a single management dashboard. This gives IT and security teams full, end-to-end visibility over threats, system health, and compliance posture. It also allows security policies to be deployed, updated, and enforced consistently across every environment, without relying on multiple, siloed tools.Unification strengthens security, simplifies operations, and dramatically reduces overhead, critical for scaling securely at cloud-native speeds. That’s why at Gcore, our integrated suite of products includes security for cloud, network, and AI workloads, all managed in a single, intuitive interface.Why choose Gcore for cloud-native security?Securing cloud-native workloads requires more than legacy firewalls and patchwork solutions. It demands dynamic, intelligent protection that moves as fast as your business does.Gcore Edge Security delivers robust, AI-driven security built for the cloud-native era. By combining real-time AI threat detection, zero-trust enforcement, automated responses, and compliance-first design, Gcore security solutions protect distributed applications without slowing down development cycles.Discover why WAAP is essential for cloud security in 2025

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