Vibe coding has emerged as a cultural phenomenon in 2025 software development. It’s a style defined by coding on instinct and moving fast, often with the help of AI, rather than following rigid plans. It lets developers skip exhaustive design phases and dive straight into building, writing code (or prompting an AI to write it) in a rapid, conversational loop. It has caught on fast and boasts a dedicated following of developers hosting vibe coding game jams.
So why all the buzz? For one, vibe coding delivers speed and spontaneity. Enthusiasts say it frees them to prototype at the speed of thought, without overthinking architecture. A working feature can be blinked into existence after a few AI-assisted prompts, which is intoxicating for startups chasing product-market fit. But as with any trend that favors speed over process, there’s a flip side.
This article explores the benefits of vibe coding and the cybersecurity risks it introduces, examines real incidents where "just ship it" coding backfired, and outlines how security leaders can keep up without slowing innovation.
The upside: innovation at breakneck speed
Vibe coding addresses real development needs and has major benefits:
- Allows lightning-fast prototyping with AI assistance. Speed is a major advantage, especially for startups, and allows faster validation of ideas and product-market fit.
- Prioritizes creativity over perfection, rewarding flow and iteration over perfection.
- Lowers barriers to entry for non-experts. AI tooling lowers the skill floor, letting more people code.
- Produces real success stories, like a game built via vibe coding hitting $1M ARR in 17 days.
Vibe coding aligns well with lean, agile, and continuous delivery environments by removing overhead and empowering rapid iteration.
When speed bites back
Vibe coding isn’t inherently insecure, but the culture of speed it promotes can lead to critical oversights, especially when paired with AI tooling and lax process discipline. The following real-world incidents aren’t all examples of vibe coding per se, but they illustrate the kinds of risks that arise when developers prioritize velocity over security, skip reviews, or lean too heavily on AI without safeguards. These three cases show how fast-moving or under-documented development practices can open serious vulnerabilities.
xAI API key leak (2025)
A developer at Elon Musk’s AI company, xAI, accidentally committed internal API keys to a public GitHub repo. These keys provided access to proprietary LLMs trained on Tesla and SpaceX data. The leak went undetected for two months, exposing critical intellectual property until a researcher reported it. The error likely stemmed from fast-moving development where secrets were hardcoded for convenience.
Malicious NPM packages (2024)
In January 2024, attackers uploaded npm packages like warbeast2000 and kodiak2k, which exfiltrated SSH keys from developer machines. These were downloaded over 1,600 times before detection. Developers, trusting AI suggestions or searching hastily for functionality, unknowingly included these malicious libraries.
OpenAI API key abuse via Replit (2024)
Hackers scraped thousands of OpenAI API keys from public Replit projects, which developers had left in plaintext. These keys were abused to access GPT-4 for free, racking up massive bills for unsuspecting users. This incident shows how projects with weak secret hygiene, which is a risk of vibe coding, become easy targets.
Securing the vibe: smart risk mitigation
Cybersecurity teams can enable innovation without compromising safety by following a few simple cybersecurity best practices. While these don’t offer 100% security, they do mitigate many of the major vulnerabilities of vibe coding.
- Integrate scanning tools: Use SAST, SCA, and secret scanners in CI/CD. Supplement with AI-based code analyzers to assess LLM-generated code.
- Shift security left: Embed secure-by-default templates and dev-friendly checklists. Make secure SDKs and CLI wrappers easily available.
- Use guardrails, not gates: Enable runtime protections like WAF, bot filtering, DDoS defense, and rate limiting. Leverage progressive delivery to limit blast radius.
- Educate, don’t block: Provide lightweight, modular security learning paths for developers. Encourage experimentation in secure sandboxes with audit trails.
- Consult security experts: Consider outsourcing your cybersecurity to an expert like Gcore to keep your app or AI safe.
Secure innovation sustainably with Gcore
Vibe coding is here to stay, and for good reason. It unlocks creativity and accelerates delivery. But it also invites mistakes that attackers can exploit. Rather than fight the vibe, cybersecurity leaders must adapt: automating protections, partnering with devs, and building a culture where shipping fast doesn't mean shipping insecure.
Want to secure your edge-built AI or fast-moving app infrastructure? Gcore’s Edge Security platform offers robust, low-latency protection with next-gen WAAP and DDoS mitigation to help you innovate confidently, even at speed. As AI and security experts, we understand the risks and rewards of vibe coding, and we’re ideally positioned to help you secure your workloads without slowing down development.
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We’ve expanded our model library for Gcore Everywhere Inference with three powerful additions from the Qwen3 series. These new models bring advanced reasoning, faster response times, and even better multilingual support, helping you power everything from chatbots and coding tools to complex R&D workloads.With Gcore Everywhere Inference, you can deploy Qwen3 models in just three clicks. Read on to discover what makes Qwen3 special, which Qwen3 model best suits your needs, and how to deploy it with Gcore today.Introducing the new Qwen3 modelsQwen3 is the latest evolution of the Qwen series, featuring both dense and Mixture-of-Experts (MoE) architectures. It introduces dual-mode reasoning, letting you toggle between “thinking” and “non-thinking” modes to balance depth and speed:Thinking mode (enable_thinking=True): The model adds a <think>…</think> block to reason step-by-step before generating the final response. Ideal for tasks like code generation or math where accuracy and logic matter.Non-thinking mode (enable_thinking=False): Skips the reasoning phase to respond faster. Best for straightforward tasks where speed is a priority.Model sizes and use casesWith three new sizes available, you can choose the level of performance required for your use case:Qwen3-14B: A 14B parameter model tuned for responsive, multilingual chat and instruction-following. Fast, versatile, and ready for real-time applications with lightning-fast responses.Qwen3-30B-A3B: Built on the Arch-3 backbone, this 30B model offers advanced reasoning and coding capabilities. It’s ideal for applications that demand deeper understanding and precision while balancing performance. It provides high-quality output with faster inference and better efficiency.Qwen3-32B: The largest Qwen3 model yet, designed for complex, high-performance tasks across reasoning, generation, and multilingual domains. It sets a new standard for what’s achievable with Gcore Everywhere Inference, delivering exceptional results with maximum reasoning power. Ideal for complex computation and generation tasks where every detail matters.ModelArchitectureTotal parametersActive parametersContext lengthBest suited forQwen3-14BDense14B14B128KMultilingual chatbots, instruction-following tasks, and applications requiring strong reasoning capabilities with moderate resource consumption.Qwen3-30B-A3BMoE30B3B128KScenarios requiring advanced reasoning and coding capabilities with efficient resource usage; suitable for real-time applications due to faster inference times.Qwen3-32BDense32B32B128KHigh-performance tasks demanding maximum reasoning power and accuracy; ideal for complex R&D workloads and precision-critical applications.How to deploy Qwen3 models with Gcore in just a few clicksGetting started with Qwen3 on Gcore Everywhere Inference is fast and frictionless. Simply log in to the Gcore Portal, navigate to the AI Inference section, and select your desired Qwen3 model. From there, deployment takes just three clicks—no setup scripts, no GPU wrangling, no DevOps overhead. Check out our docs to discover how it works.Deploying Qwen3 via the Gcore Customer Portal takes just three clicksPrefer to deploy programmatically? Use the Gcore API with your project credentials. We offer quick-start examples in Python and cURL to get you up and running fast.Why choose Qwen3 + Gcore?Flexible performance: Choose from three models tailored to different workloads and cost-performance needs.Immediate availability: All models are live now and deployable via portal or API.Next-gen architecture: Dense and MoE options give you more control over reasoning, speed, and output quality.Scalable by design: Built for production-grade performance across industries and use cases.With the latest Qwen3 additions, Gcore Everywhere Inference continues to deliver on performance, scalability, and choice. Ready to get started? Get a free account today to explore Qwen3 and deploy with Gcore in just a few clicks.Sign up free to deploy Qwen3 today
Run AI workloads faster with our new cloud region in Southern Europe
Good news for businesses operating in Southern Europe! Our newest cloud region in Sines, Portugal, gives you faster, more local access to the infrastructure you need to run advanced AI, ML, and HPC workloads across the Iberian Peninsula and wider region. Sines-2 marks the first region launched in partnership with Northern Data Group, signaling a new chapter in delivering powerful, workload-optimized infrastructure across Europe.Strategically positioned in Portugal, Sines-2 enhances coverage in Southern Europe, providing a lower-latency option for customers operating in or targeting this region. With the explosive growth of AI, machine learning, and compute-intensive workloads, this new region is designed to meet escalating demand with cutting-edge GPU and storage capabilities.Built for AI, designed to scaleSines-2 brings with it next-generation infrastructure features, purpose-built for today’s most demanding workloads:NVIDIA H100 GPUs: Unlock the full potential of AI/ML training, high-performance computing (HPC), and rendering workloads with access to H100 GPUs.VAST NFS (file sharing protocol) support: Benefit from scalable, high-throughput file storage ideal for data-intensive operations, research, and real-time AI workflows.IaaS portfolio: Deploy Virtual Machines, manage storage, and scale infrastructure with the same consistency and reliability as in our flagship regions.Organizations operating in Portugal, Spain, and nearby regions can now deploy workloads closer to end users, improving application performance. For finance, healthcare, public sector, and other organisations running sensitive workloads that must stay within a country or region, Sines-2 is an easy way to access state-of-the-art GPUs with simplified compliance. Whether you're building AI models, running simulations, or managing rendering pipelines, Sines-2 offers the performance and proximity you need.And best of all, servers are available and ready to deploy today.Run your AI workloads in Portugal todayWith Sines-2 and our partnership with Northern Data Group, we’re making it easier than ever for you to run AI workloads at scale. If you need speed, flexibility, and global reach, we’re ready to power your next AI breakthrough.Unlock the power of Sines-2 today
How AI is transforming gaming experiences
AI is reshaping how games are played, built, and experienced. Although we are in a period of flux where the optimal combination of human and artificial intelligence is still being ironed out, the potential for AI to greatly enhance both gameplay and development is clear.PlayStation CEO Hermen Hulst recently emphasized the importance of striking the right balance between the handcrafted human touch and the revolutionary advances that AI brings. AI will not replace the decades of design, storytelling, and craft laid down by humans—it will build on that foundation to unlock entirely new possibilities. In addition to an enhanced playing experience, AI is shaking up gaming aspects such as real-time analytics, player interactions, content generation, and security.In this article, we explore three specific developments that are enriching gaming storyworlds, as well as the technology that’s bringing them to life and what the future might hold.#1 Responsive NPC behavior and smarter opponentsAI is evolving to create more realistic, adaptive, and intelligent non-player characters (NPCs) that can react to individual player choices with greater depth and reasoning. The algorithms allow NPCs to respond dynamically to players’ decisions so they can adjust their strategies and behaviors in real time. This provides a more immersive and dynamic gameplay environment and means gamers have endless opportunities to experience new gaming adventures and write their own story every time.A recent example is Red Dead Redemption 2, which enables players to interact with NPCs in the Wild West. Players were impressed by its complexity and the ability to interact with fellow cowboys and bar patrons. Although this is limited for now, eventually, it could become like a video game version of the TV series Westworld, in which visitors pay to interact with incredibly lifelike robots in a Wild West theme park.AI also gives in-game opponents more “agency,” making them more reactive and challenging for players to defeat. This means smarter, more unpredictable enemies who provide a heightened level of achievement, novelty, and excitement for players.For example, AI Limit, released in early 2025, is an action RPG incorporating AI-driven combat mechanics. While drawing comparisons to Soulslike games, the developers emphasize its unique features, including the Sync Rate system, which adds depth to combat interactions.#2 AI-assisted matchmaking and player behavior predictionsAI-powered analytics can identify and predict player skill levels and playing styles, leading to more balanced and competitive matchmaking. A notable example is the implementation of advanced matchmaking systems in competitive games such as Apex Legends and Call of Duty: Modern Warfare III. These titles use AI algorithms to analyze not just skill levels but also playstyle preferences, weapon selections, and playing patterns to create matches optimized for player retention and satisfaction. The systems continuously learn from match outcomes to predict player behavior and create more balanced team compositions across different skill levels.By analyzing a player’s past performance, AI can also create smarter team formations. 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How gaming studios can use technology to safeguard players
Online gaming can be an enjoyable and rewarding pastime, providing a sense of community and even improving cognitive skills. During the pandemic, for example, online gaming was proven to boost many players’ mental health and provided a vital social outlet at a time of great isolation. However, despite the overall benefits of gaming, there are two factors that can seriously spoil the gaming experience for players: toxic behavior and cyber attacks.Both toxic behavior and cyberattacks can lead to players abandoning games in order to protect themselves. While it’s impossible to eradicate harmful behaviors completely, robust technology can swiftly detect and ban bullies as well as defend against targeted cyberattacks that can ruin the gaming experience.This article explores how gaming studios can leverage technology to detect toxic behavior, defend against cyber threats, and deliver a safer, more engaging experience for players.Moderating toxic behavior with AI-driven technologyToxic behavior—including harassment, abusive messages, and cheating—has long been a problem in the world of gaming. Toxic behavior not only affects players emotionally but can also damage a studio’s reputation, drive churn, and generate negative reviews.The online disinhibition effect leads some players to behave in ways they may not in real life. But even when it takes place in a virtual world, this negative behavior has real long-term detrimental effects on its targets.While you can’t control how players behave, you can control how quickly you respond.Gaming studios can implement technology that makes dealing with toxic incidents easier and makes gaming a safer environment for everyone. While in the past it may have taken days to verify a complaint about a player’s behavior, today, with AI-driven security and content moderation, toxic behavior can be detected in real time, and automated bans can be enforced. The tool can detect inappropriate images and content and includes speech recognition to detect derogatory or hateful language.In gaming, AI content moderation analyzes player interactions in real time to detect toxic behavior, harmful content, and policy violations. Machine learning models assess chat, voice, and in-game media against predefined rules, flagging or blocking inappropriate content. For example, let’s say a player is struggling with in-game harassment and cheating. With AI-powered moderation tools, chat logs and gameplay behavior are analyzed in real time, identifying toxic players for automated bans. This results in healthier in-game communities, improved player retention, and a more pleasant user experience.Stopping cybercriminals from ruining the gaming experienceAnother factor negatively impacting the gaming experience on a larger scale is cyberattacks. Our recent Radar Report showed that the gaming industry experienced the highest number of DDoS attacks in the last quarter of 2024. The sector is also vulnerable to bot abuse, API attacks, data theft, and account hijacking.Prolonged downtime damages a studio’s reputation—something hackers know all too well. As a result, gaming platforms are prime targets for ransomware, extortion, and data breaches. Cybercriminals target both servers and individual players’ personal information. This naturally leads to a drop in player engagement and widespread frustration.Luckily, security solutions can be put in place to protect gamers from this kind of intrusion:DDoS protection shields game servers from volumetric and targeted attacks, guaranteeing uptime even during high-profile launches. In the event of an attack, malicious traffic is mitigated in real-time, preventing zero downtime and guaranteeing seamless player experiences.WAAP secures game APIs and web services from bot abuse, credential stuffing, and data breaches. It protects against in-game fraud, exploits, and API vulnerabilities.Edge security solutions reduce latency, protecting players without affecting game performance. The Gcore security stack helps ensure fair play, protecting revenue and player retention.Take the first steps to protecting your customersGaming should be a positive and fun experience, not fraught with harassment, bullying, and the threat of cybercrime. Harmful and disruptive behaviors can make it feel unsafe for everyone to play as they wish. That’s why gaming studios should consider how to implement the right technology to help players feel protected.Gcore was founded in 2014 with a focus on the gaming industry. Over the years, we have thwarted many large DDoS attacks and continue to offer robust protection for companies such as Nitrado, Saber, and Wargaming. Our gaming specialization has also led us to develop game-specific countermeasures. If you’d like to learn more about how our cybersecurity solutions for gaming can help you, get in touch.Speak to our gaming solutions experts today
Gcore and Northern Data Group partner to transform global AI deployment
Gcore and Northern Data Group have joined forces to launch a new chapter in enterprise AI. By combining high-performance infrastructure with intelligent software, the commercial and technology partnership will make it dramatically easier to deploy AI applications at scale—wherever your users are. At the heart of this exciting new partnership is a shared vision: global, low-latency, secure AI infrastructure that’s simple to use and ready for production.Introducing the Intelligence Delivery NetworkAI adoption is accelerating, but infrastructure remains a major bottleneck. Many enterprises discover blockers regarding latency, compliance, and scale, especially when deploying models in multiple regions. The traditional cloud approach often introduces complexity and overhead just when speed and simplicity matter most.That’s where the Intelligence Delivery Network (IDN) comes in.The IDN is a globally distributed AI network built to simplify inference at the edge. It combines Northern Data’s state-of-the-art infrastructure with Gcore Everywhere Inference to deliver scalable, high-performance AI across 180 global points of presence.By locating AI workloads closer to end users, the IDN reduces latency and improves responsiveness—without compromising on security or compliance. Its geo-zoned, geo-balanced architecture ensures resilience and data locality while minimizing deployment complexity.A full AI deployment toolkitThe IDN is a full AI deployment toolkit built on Gcore’s cloud-native platform. The solution offers a vertically integrated stack designed for speed, flexibility, and scale. Key components include the following:Managed Kubernetes for orchestrationA container-based deployment engine (Docker)An extensive model library, supporting open-source and custom modelsEverywhere Inference, Gcore’s software for distributing inferencing across global edge points of presenceThis toolset enables fast, simple deployments of AI workloads—with built-in scaling, resource management, and observability. The partnership also unlocks access to one of the world’s largest liquid-cooled GPU clusters, giving AI teams the horsepower they need for demanding workloads.Whether you’re building a new AI-powered product or scaling an existing model, the IDN provides a clear path from development to production.Built for scale and performanceThe joint solution is built with the needs of enterprise customers in mind. It supports multi-tenant deployments, integrates with existing cloud-native tools, and prioritizes performance without sacrificing control. Customers gain the flexibility to deploy wherever and however they need, with enterprise-grade security and compliance baked in.Andre Reitenbach, CEO of Gcore, comments, “This collaboration supports Gcore’s mission to connect the world to AI anywhere and anytime. Together, we’re enabling the next generation of AI applications with low latency and massive scale.”“We are combining Northern Data’s heritage of HPC and Data Center infrastructure expertise, with Gcore’s specialization in software innovation and engineering.” says Aroosh Thillainathan, Founder and CEO of Northern Data Group. “This allows us to accelerate our vision of delivering software-enabled AI infrastructure across a globally distributed compute network. This is a key moment in time where the use of AI solutions is evolving, and we believe that this partnership will form a key part of it.”Deploy AI smarter and faster with Gcore and Northern Data GroupAI is the new foundation of digital business. Deploying it globally shouldn’t require a team of infrastructure engineers. With Gcore and Northern Data Group, enterprise teams get the tools and support they need to run AI at the edge at scale and at speed.No matter what you and your teams are trying to achieve with AI, the new Intelligence Delivery Network is built to help you deploy smarter and faster.Read the full press release
How to achieve compliance and security in AI inference
AI inference applications today handle an immense volume of confidential information, so prioritizing data privacy is paramount. Industries such as finance, healthcare, and government rely on AI to process sensitive data—detecting fraudulent transactions, analyzing patient records, and identifying cybersecurity threats in real time. While AI inference enhances efficiency, decision-making, and automation, neglecting security and compliance can lead to severe financial penalties, regulatory violations, and data breaches. Industries handling sensitive information—such as finance, healthcare, and government—must carefully manage AI deployments to avoid costly fines, legal action, and reputational damage.Without robust security measures, AI inference environments present a unique security challenge as they process real-time data and interact directly with users. This article explores the security challenges enterprises face and best practices for guaranteeing compliance and protecting AI inference workloads.Key inference security and compliance challengesAs businesses scale AI-powered applications, they will likely encounter challenges in meeting regulatory requirements, preventing unauthorized access, and making sure that AI models (whether proprietary or open source) produce reliable and unaltered outputs.Data privacy and sovereigntyRegulations such as GDPR (Europe), CCPA (California), HIPAA (United States, healthcare), and PCI DSS (finance) impose strict rules on data handling, dictating where and how AI models can be deployed. Businesses using public cloud-based AI models must verify that data is processed and stored in appropriate locations to avoid compliance violations.Additionally, compliance constraints restrict certain AI models in specific regions. Companies must carefully evaluate whether their chosen models align with regulatory requirements in their operational areas.Best practices:To maintain compliance and avoid legal risks:Deploy AI models in regionally restricted environments to keep sensitive data within legally approved jurisdictions.Use Smart Routing with edge inference to process data closer to its source, reducing cross-border security risks.Model security risksBad actors can manipulate AI models to produce incorrect outputs, compromising their reliability and integrity. This is known as adversarial manipulation, where small, intentional alterations to input data can deceive AI models. For example, researchers have demonstrated that minor changes to medical images can trick AI diagnostic models into misclassifying benign tumors as malignant. In a security context, attackers could exploit these vulnerabilities to bypass fraud detection in finance or manipulate AI-driven cybersecurity systems, leading to unauthorized transactions or undetected threats.To prevent such threats, businesses must implement strong authentication, encryption strategies, and access control policies for AI models.Best practices:To prevent adversarial attacks and maintain model integrity:Enforce strong authentication and authorization controls to limit access to AI models.Encrypt model inputs and outputs to prevent data interception and tampering.Endpoint protection for AI deploymentsThe security of AI inference does not stop at the model level. It also depends on where and how models are deployed.For private deployments, securing AI endpoints is crucial to prevent unauthorized access.For public cloud inference, leveraging CDN-based security can help protect workloads against cyber threats.Processing data within the country of origin can further reduce compliance risks while improving latency and security.AI models rely on low-latency, high-performance processing, but securing these workloads against cyber threats is as critical as optimizing performance. CDN-based security strengthens AI inference protection in the following ways:Encrypts model interactions with SSL/TLS to safeguard data transmissions.Implements rate limiting to prevent excessive API requests and automated attacks.Enhances authentication controls to restrict access to authorized users and applications.Blocks bot-driven threats that attempt to exploit AI vulnerabilities.Additionally, CDN-based security supports compliance by:Using Smart Routing to direct AI workloads to designated inference nodes, helping align processing with data sovereignty laws.Optimizing delivery and security while maintaining adherence to regional compliance requirements.While CDNs enhance security and performance by managing traffic flow, compliance ultimately depends on where the AI model is hosted and processed. Smart Routing allows organizations to define policies that help keep inference within legally approved regions, reducing compliance risks.Best practices:To protect AI inference environments from endpoint-related threats, you should:Deploy monitoring tools to detect unauthorized access, anomalies, and potential security breaches in real-time.Implement logging and auditing mechanisms for compliance reporting and proactive security tracking.Secure AI inference with Gcore Everywhere InferenceAI inference security and compliance are critical as businesses handle sensitive data across multiple regions. Organizations need a robust, security-first AI infrastructure to mitigate risks, reduce latency, and maintain compliance with data sovereignty laws.Gcore’s edge network and CDN-based security provide multi-layered protection for AI workloads, combining DDoS protection and WAAP (web application and API protection. By keeping inference closer to users and securing every stage of the AI pipeline, Gcore helps businesses protect data, optimize performance, and meet industry regulations.Explore Gcore AI Inference
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