A volumetric attack is a Distributed Denial of Service (DDoS) attack that floods a server or network with massive amounts of traffic to overwhelm its bandwidth and cause service disruption.
Volumetric attacks target Layers 3 (Network) and 4 (Transport) of the OSI model. Attackers use botnets (networks of compromised devices) to generate the high volume of malicious traffic required to exhaust bandwidth.
Traffic volume is measured in bits per second (bps), packets per second (pps), or connections per second (cps). The largest attacks now exceed three terabits per second (Tbps).
The main types include DNS amplification, NTP amplification, and UDP flood attacks. Reflection and amplification techniques are common, where attackers send small requests to vulnerable servers with a spoofed source IP (the target), causing the server to respond with much larger packets to the victim. This amplification can increase attack traffic by 50 to 100 times the original request size.
Recognizing the signs of a volumetric attack is critical for a fast response.
Network performance drops sharply when bandwidth is exhausted. You will see slow connectivity, timeouts, and complete service outages. These attacks typically last from minutes to hours, though some persist for days without proper defenses in place.
Understanding volumetric attacks is crucial because they can bring down services in minutes and result in organizations losing thousands of dollars in revenue per hour.
Modern attacks regularly reach multi-terabits per second, overwhelming even well-provisioned networks without proper DDoS protection.
What are volumetric attacks?
Volumetric attacks are Distributed Denial of Service (DDoS) attacks that flood a target's network or server with massive amounts of traffic. The goal? Overwhelm bandwidth and disrupt service.
These attacks work at Layers 3 (Network) and 4 (Transport) of the OSI model. They focus on bandwidth exhaustion rather than exploiting application vulnerabilities. Attackers typically use botnets (networks of compromised devices) to generate the high volume of malicious traffic needed.
Here's how it works. Attackers often employ reflection and amplification techniques, sending small requests to vulnerable servers, such as DNS or NTP, with a spoofed source IP address. This causes these servers to respond with much larger packets to the victim, multiplying the attack's impact.
Attack volume is measured in bits per second (bps), packets per second (pps), or connections per second (cps). The largest attacks now exceed multiple terabits per second.
How do volumetric attacks work?
Volumetric attacks flood a target's network or server with massive amounts of traffic to exhaust bandwidth and make services unavailable to legitimate users. Attackers use botnets (networks of compromised devices) to generate enough traffic volume to overwhelm the target's capacity, typically measured in bits per second (bps), packets per second (pps), or connections per second (cps).
The attack targets Layers 3 (Network) and 4 (Transport) of the OSI model. Attackers commonly use reflection and amplification techniques to multiply their attack power.
Here's how it works: They send small requests to vulnerable servers, such as DNS, NTP, or memcached, with a spoofed source IP address (the victim's address). The servers respond with much larger packets directed at the target, amplifying the attack traffic by 10 times to 100 times or more.
The sheer volume of malicious traffic, combined with legitimate requests, makes detection difficult. When the flood of packets arrives, it consumes all available bandwidth and network resources.
Routers, firewalls, and servers can't process the volume. This causes service disruption or complete outages. Common attack types include DNS amplification, UDP floods, and ICMP floods (also known as ping floods), each targeting different protocols to maximize bandwidth consumption.
Modern volumetric attacks regularly exceed multiple terabits per second in size. IoT devices comprise a significant portion of botnets due to their often weak security and always-on internet connections.
Attacks typically last minutes to hours but can persist for days without proper protection.
What are the main types of volumetric attacks?
The main types of volumetric attacks refer to the specific methods attackers use to flood a target with massive amounts of traffic and exhaust its bandwidth. The main types of volumetric attacks are listed below.
- DNS amplification: Attackers send small DNS queries to open resolvers with a spoofed source IP address (the victim's). The DNS servers respond with much larger replies to the target, creating traffic volumes 28–54 times the original request size. This method remains one of the most effective amplification techniques.
- UDP flood: The attacker sends a high volume of UDP packets to random ports on the target system. The target checks for applications listening on those ports and responds with ICMP "Destination Unreachable" packets, exhausting network resources. These attacks are simple to execute but highly effective at consuming bandwidth.
- ICMP flood: Also called a ping flood, this attack bombards the target with ICMP Echo Request packets. The target attempts to respond to each request with ICMP Echo Reply packets. This consumes both bandwidth and processing power. The sheer volume of requests can bring down network infrastructure.
- NTP amplification: Attackers exploit Network Time Protocol servers by sending small requests with spoofed source addresses. The NTP servers respond with much larger packets to the victim, creating amplification factors up to 556 times the original request. This makes NTP one of the most dangerous protocols for reflection attacks.
- SSDP amplification: Simple Service Discovery Protocol, used by Universal Plug and Play devices, can amplify attack traffic by 30–40 times. Attackers send discovery requests to IoT devices with spoofed source IPs, causing these devices to flood the victim with response packets. The proliferation of unsecured IoT devices makes this attack increasingly common.
- Memcached amplification: Attackers target misconfigured memcached servers with small requests that trigger massive responses. This protocol can achieve amplification factors exceeding 50,000 times, making it capable of generating multi-terabits-per-second attacks. Several record-breaking attacks in recent years have used this method.
- SYN flood: The attacker sends a rapid succession of SYN requests to initiate TCP connections without completing the handshake. The target allocates resources for each half-open connection, quickly exhausting its connection table. While technically targeting connection resources, large-scale SYN floods can also consume a significant amount of bandwidth.
What are the signs of a volumetric attack?
Signs of a volumetric attack are the observable indicators that a network or server is experiencing a DDoS attack designed to exhaust bandwidth through massive traffic floods. Here are the key signs to watch for.
- Sudden traffic spikes: Network monitoring tools show an abrupt increase in traffic volume, often reaching gigabits or terabits per second. These spikes happen without any corresponding increase in legitimate user activity.
- Network congestion: Bandwidth becomes saturated, causing legitimate traffic to slow or stop entirely. Users experience timeouts, failed connections, and complete service unavailability.
- Unusual protocol activity: Monitoring reveals abnormal levels of specific protocols, such as DNS, NTP, ICMP, or UDP traffic. Attackers commonly exploit these protocols in reflection and amplification attacks.
- High packet rates: The network receives an extreme number of packets per second (pps), overwhelming routers and firewalls. This flood exhausts processing capacity even when individual packets are small.
- Traffic from multiple sources: Logs show incoming connections from thousands or millions of different IP addresses simultaneously. This pattern indicates botnet activity rather than legitimate user behavior.
- Asymmetric traffic patterns: Inbound traffic dramatically exceeds outbound traffic, creating an imbalanced flow. Normal operations typically show more balanced bidirectional communication.
- Repeated connection attempts: Systems log massive numbers of connection requests to random or non-existent ports. These requests aim to exhaust server resources through sheer volume.
- Geographic anomalies: Traffic originates from unexpected regions or countries where the service has few legitimate users. This geographic mismatch suggests coordinated attack traffic rather than organic usage.
What impact do volumetric attacks have on businesses?
Volumetric attacks hit businesses hard by flooding network bandwidth with massive traffic surges, causing complete service outages, revenue loss, and damaged customer trust. When these attacks overwhelm a network with hundreds of gigabits or even terabits per second of malicious traffic, legitimate users can't access your services. This results in direct revenue loss during downtime and potential long-term customer attrition.
The financial damage doesn't stop when the attack ends. Beyond immediate outages, you'll face costs from emergency mitigation services, increased infrastructure investments, and reputational damage that can persist for months or years after the incident.
How to protect against volumetric attacks
You can protect against volumetric attacks by deploying traffic filtering, increasing bandwidth capacity, and using specialized DDoS mitigation services that can absorb and filter malicious traffic before it reaches your network.
First, deploy traffic filtering at your network edge to identify and block malicious packets. Configure your routers and firewalls to drop traffic from known malicious sources and apply rate-limiting rules to suspicious IP addresses. This stops basic attacks before they consume your bandwidth.
Next, increase your bandwidth capacity to absorb traffic spikes without service degradation. While this won't stop an attack, having 2 to 3 times your normal bandwidth gives you buffer time to apply other defenses. Major attacks regularly exceed multiple terabits per second, so plan capacity accordingly.
Then, set up real-time traffic monitoring to detect unusual patterns early. Configure alerts for sudden spikes in bits per second, packets per second, or connections per second. Early detection lets you respond within minutes instead of hours.
After that, work with your ISP to implement upstream filtering when attacks exceed your capacity. ISPs can drop malicious traffic at their network edge before it reaches you. Establish this relationship before an attack happens because response time matters.
Deploy anti-spoofing measures to prevent your network from being used in reflection attacks. Enable ingress filtering (BCP 38) to verify source IP addresses and reject packets with spoofed origins. This protects both your network and potential victims.
Finally, consider using a DDoS protection service that can handle multi-terabit attacks through global scrubbing centers. These services route your traffic through their infrastructure, filtering out malicious packets while allowing legitimate requests to pass through. This is essential since volumetric attacks account for over 75% of all DDoS incidents.
Test your defenses regularly with simulated attacks to verify your response procedures and identify weak points before real attackers do.
What are the best practices for volumetric attack mitigation?
Best practices for volumetric attack mitigation refer to the proven strategies and techniques organizations use to defend against bandwidth exhaustion attacks. The best practices for mitigating volumetric attacks are listed below.
- Deploy traffic scrubbing: Traffic scrubbing centers filter malicious packets before they reach your network infrastructure. These specialized facilities can absorb multi-Tbps attacks by analyzing traffic patterns in real-time and blocking suspicious requests while allowing legitimate users through.
- Use anycast network routing: Anycast routing distributes incoming traffic across multiple data centers instead of directing it to a single location. This distribution prevents attackers from overwhelming a single point of failure and spreads the attack load across your infrastructure.
- Implement rate limiting: Rate limiting controls restrict the number of requests a single source can send within a specific timeframe. You can configure these limits at your network edge to drop excessive traffic from suspicious IP addresses before it consumes bandwidth.
- Monitor baseline traffic patterns: Establish normal traffic baselines for your network to detect anomalies quickly. When traffic volume suddenly spikes by 300% or more, automated systems can trigger mitigation protocols within seconds rather than minutes.
- Configure upstream filtering: Work with your ISP to filter attack traffic before it reaches your network perimeter. ISPs can block malicious packets at their backbone level, preventing bandwidth saturation on your connection and preserving service availability.
- Enable connection tracking: Connection tracking systems maintain state information about active network connections to identify suspicious patterns. These systems can detect when a single source opens thousands of connections simultaneously (a common sign of volumetric attacks).
- Maintain excess bandwidth capacity: Keep at least 50% more bandwidth capacity than your peak legitimate traffic requires. This buffer won't stop large attacks, but it gives you time to activate other defenses before services degrade.
How to respond during an active volumetric attack
When a volumetric attack occurs, you need to act quickly: activate your DDoS mitigation service, reroute traffic through scrubbing centers, and isolate affected network segments while maintaining service availability.
First, confirm you're facing a volumetric attack. Check your network monitoring tools for sudden traffic spikes measured in gigabits per second (Gbps) or packets per second (pps). Look for patterns such as UDP floods, ICMP floods, or DNS amplification attacks that target your bandwidth rather than your application logic.
Next, activate your DDoS mitigation service immediately or contact your provider to reroute traffic through scrubbing centers. These centers filter out malicious packets before they reach your infrastructure. You'll typically see attack traffic reduced by 90-95% within 3-5 minutes of activation.
Then, implement rate limiting on your edge routers to cap incoming traffic from suspicious sources. Set thresholds based on your normal traffic baseline. If you typically handle 10 Gbps, limit individual source IPs so no single origin consumes more than 1-2% of capacity.
After that, enable geo-blocking or IP blacklisting for regions where you don't operate if attack sources concentrate in specific countries. This immediately cuts off large portions of botnet traffic while preserving access for legitimate users.
Isolate critical services by redirecting less important traffic to secondary servers or temporarily turning off non-essential services. This preserves bandwidth for your core business functions during the attack.
Finally, document the attack details. Record start time, peak traffic volume, attack vectors used, and source IP ranges for post-incident analysis. This data helps you strengthen defenses and may be required for law enforcement or insurance claims.
Monitor your traffic continuously for 24 to 48 hours after the attack subsides. Attackers often launch follow-up waves to test your defenses or exhaust your mitigation resources.
Frequently asked questions
What's the difference between volumetric attacks and application-layer attacks?
Volumetric attacks flood your network with massive traffic to exhaust bandwidth at Layers 3 and 4. Application-layer attacks work differently. They target specific software vulnerabilities at Layer 7 using low-volume, sophisticated requests that are harder to detect.
How large can volumetric attacks get?
Volumetric attacks regularly reach multiple terabits per second (Tbps). The largest recorded attacks exceeded 3 Tbps in 2024.
Can small businesses be targeted by volumetric attacks?
Yes, small businesses are frequently targeted by volumetric attacks. Attackers often view them as easier targets with weaker defenses and less sophisticated DDoS protection than enterprises.
How quickly can volumetric attack mitigation be deployed?
Modern DDoS protection platforms activate automatically when they detect attack patterns. Once traffic reaches the protection service, volumetric attack mitigation deploys in under 60 seconds, routing malicious traffic away from your network.
Initial setup of the protection infrastructure takes longer. You'll need hours to days to configure your defenses properly before you're fully protected.
What is the cost of volumetric DDoS protection?
Volumetric DDoS protection costs vary widely. Basic services start at $50 to $500+ per month, while enterprise solutions can run $10,000+ monthly. The price depends on three main factors: bandwidth capacity, attack size limits, and response times.
Most providers use a tiered pricing model. You'll pay based on your clean bandwidth needs (measured in Gbps) and the maximum attack mitigation capacity you need (measured in Tbps).
Do volumetric attacks always target specific organizations?
No, volumetric attacks don't target specific organizations. They flood any available bandwidth indiscriminately and often hit unintended victims through reflection and amplification techniques. Here's how it works: attackers spoof the target's IP address when sending requests to vulnerable servers, which causes those servers to overwhelm the victim with massive response traffic.
How does Gcore detect volumetric attacks in real-time?
The system automatically flags suspicious traffic when it exceeds your baseline thresholds, measured in bits per second (bps) or packets per second (pps).
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What if you could ask your infrastructure questions and get real answers?With Gcore’s open-source implementation of the Model Context Protocol (MCP), now you can. MCP turns generative AI into an agent that understands your infrastructure, responds to your queries, and takes action when you need it to.In this post, we’ll demo how to use MCP to explore and inspect your Gcore environment just by prompting, to list resources, check audit logs, and generate cost reports. We’ll also walk through a fun bonus use case: provisioning infrastructure and exporting it to Terraform.What is MCP and why do devs love it?Originally developed by Anthropic, the Model Context Protocol (MCP) is an open standard that turns language models into agents that interact with structured tools: APIs, CLIs, or internal systems. Gcore’s implementation makes this protocol real for our customers.With MCP, you can:Ask questions about your infrastructureList, inspect, or filter cloud resourcesView cost data, audit logs, or deployment metadataExport configs to TerraformChain multi-step operations via natural languageGcore MCP removes friction from interacting with your infrastructure. Instead of wiring together scripts or context-switching across dashboards and CLIs, you can just…ask.That means:Faster debugging and auditsMore accessible infra visibilityFewer repetitive setup tasksBetter team collaborationBecause it’s open source, backed by the Gcore Python SDK, you can plug it into other APIs, extend tool definitions, or even create internal agents tailored to your stack. Explore the GitHub repo for yourself.What can you do with it?This isn’t just a cute chatbot. Gcore MCP connects your cloud to real-time insights. Here are some practical prompts you can use right away.Infrastructure inspection“List all VMs running in the Frankfurt region”“Which projects have over 80% GPU utilization?”“Show all volumes not attached to any instance”Audit and cost analysis“Get me the API usage for the last 24 hours”“Which users deployed resources in the last 7 days?”“Give a cost breakdown by region for this month”Security and governance“Show me firewall rules with open ports”“List all active API tokens and their scopes”Experimental automation“Create a secure network in Tokyo, export to Terraform, then delete it”We’ll walk through that last one in the full demo below.Full video demoWatch Gcore’s AI Software Engineer, Algis Dumbris, walk through setting up MCP on your machine and show off some use cases. If you prefer reading, we’ve broken down the process step-by-step below.Step-by-step walkthroughThis section maps to the video and shows exactly how to replicate the workflow locally.1. Install MCP locally (0:00–1:28)We use uv to isolate the environment and pull the project directly from GitHub.curl -Ls https://astral.sh/uv/install.sh | sh uvx add gcore-mcp-server https://github.com/G-Core/gcore-mcp-server Requirements:PythonGcore account + API keyTool config file (from the repo)2. Set up your environment (1:28–2:47)Configure two environment variables:GCORE_API_KEY for authGCORE_TOOLS to define what the agent can access (e.g., regions, instances, costs, etc.)Soon, tool selection will be automatic, but today you can define your toolset in YAML or JSON.3. Run a basic query (3:19–4:11)Prompt:“Find the Gcore region closest to Antalya.”The agent maps this to a regions.list call and returns: IstanbulNo need to dig through docs or write an API request.4. Provision, export, and clean up (4:19–5:32)This one’s powerful if you’re experimenting with CI/CD or infrastructure-as-code.Prompt:“Create a secure network in Tokyo. Export to Terraform. Then clean up.”The agent:Provisions the networkExports it to Terraform formatDestroys the resources afterwardYou get usable .tf output with no manual scripting. Perfect for testing, prototyping, or onboarding.Gcore: always building for developersTry it now:Clone the repoInstall UVX + configure your environmentStart prompting your infrastructureOpen issues, contribute tools, or share your use casesThis is early-stage software, and we’re just getting started. Expect more tools, better UX, and deeper integrations soon.Watch how easy it is to deploy an inference instance with Gcore
How to protect login pages with Gcore WAAP
Exposed login pages are a common vulnerability across web applications. Attackers often use automated tools to guess credentials in brute-force or credential-stuffing attacks, probe for login behavior to exploit session or authentication logic, or overload your infrastructure with fake requests.Without specific rules for login-related traffic, your application might miss these threats or apply overly broad protections that disrupt real users. Fortunately, Gcore WAAP makes it easy to defend these sensitive endpoints without touching your application code.In this guide, we’ll show you how to use WAAP’s custom rule engine to identify login traffic and apply protections like CAPTCHA to reduce risk, block automated abuse, and maintain a smooth experience for legitimate users. We’ve also included a complete video walkthrough from Gcore’s Security Presales Engineer, Michal Zalewski.Video walkthroughHere’s Gcore’s Michal Zalewski giving a full walkthrough of the steps in this article.Step 1: Access your WAAP configurationGo to portal.gcore.com and log in.Navigate to WAAP in the sidebar. If you’re not yet a WAAP user, it costs just $26/month.Select the resource that hosts your login form; for example, gcore.zalewski.cloud.Step 2: Create a custom ruleIn the main panel of your selected resource, go to WAAP Rules.Click Add Custom Rule in the upper-right corner.Step 3: Define the login page URLIdentify the login endpoint you want to protect:Use tools like Burp Suite or the "Inspect" feature in your browser to verify the login page URL.In Burp Suite, use the Proxy tab, or in the browser, check the Network tab to inspect a login request.Look for the path (e.g., /login.php) and HTTP method (POST).In the custom rule setup:Enter the URL (e.g., /login.php).Tag the request using a predefined tag. Select Login Page.Step 4: Name and save the ruleProvide a name for the rule, such as “Login Page URL”, and save it.Step 5: Add a CAPTCHA challenge ruleTo protect the login page from automated abuse:Create a new custom rule.Name it something like “Login Page Challenge”.Under Conditions, select the previously created Login Page tag.Set the Action to CAPTCHA.Save the rule.Step 6: Test the protectionReturn to your browser and turn off any proxy tools.Refresh the login page.You should now be challenged with a CAPTCHA each time the login page loads.Once the CAPTCHA is completed successfully, users can log in as usual.Monitor, adapt, and alertAfter deployment:Track rate limit trigger frequencyMonitor WAAP logs for anomaly detectionRotate exemptions or thresholds based on live behaviorFor analytics, refer to the WAAP analytics documentation.Bonus tips for hardened protectionCombine with bot protection: Enable WAAP’s bot mitigation to identify headless browsers and automation tools like Puppeteer or Selenium. See our bot protection docs for setup instructions.Customize 429 responses: Replace default error pages with branded messages or a fallback action. Consider including a support link or CAPTCHA challenge. Check out our response pages documentation for more details.Use geo or ASN exceptions: Whitelist trusted locations or block known bot-heavy ASNs if your audience is localized.Automate it: optional API and Terraform supportTeams with IaC pipelines or security automation workflows might want to automate login page protection with rate limiting. This keeps your WAAP config version-controlled and repeatable.You can use the WAAP API or Terraform to:Create or update rulesRotate session keys or thresholdsExport logs for auditingExplore the WAAP API documentation and WAAP Terraform provider documentation for more details.Stop abuse before it starts with GcoreLogin pages are high-value targets, but they don’t have to be high risk. With Gcore WAAP, setting up robust defenses takes just a few minutes. By tagging login traffic and applying challenge rules like CAPTCHA, you can reduce automated attack risk without sacrificing user experience.As your application grows, revisit your WAAP rules regularly to adapt to new threats, add behavior-based detection, and fine-tune your protective layers. For more advanced configurations, check out our documentation or reach out to Gcore support.Get WAAP today for just $26/month
3 underestimated security risks of AI workloads and how to overcome them
Artificial intelligence workloads introduce a fundamentally different security landscape for engineering and security teams. Unlike traditional applications, AI systems must protect not just endpoints and networks, but also training data pipelines, feature stores, model repositories, and inference APIs. Each phase of the AI life cycle presents distinct attack vectors that adversaries can exploit to corrupt model behavior, extract proprietary logic, or manipulate downstream outputs.In this article, we uncover three security vulnerabilities of AI workloads and explain how developers and MLOps teams can overcome them. We also look at how investing in your AI security can save time and money, explore the challenges that lie ahead for AI security, and offer a simplified way to protect your AI workloads with Gcore.Risk #1: data poisoningData poisoning is a targeted attack on the integrity of AI systems, where malicious actors subtly inject corrupted or manipulated data into training pipelines. The result is a model that behaves unpredictably, generates biased or false outputs, or embeds hidden logic that can be triggered post-deployment. This can undermine business-critical applications—from fraud detection and medical diagnostics to content moderation and autonomous decision-making.For developers, the stakes are high: poisoned models are hard to detect once deployed, and even small perturbations in training data can have system-wide consequences. Luckily, you can take a few steps to mitigate against data poisoning and then implement zero-trust AI to further protect your workloads.Mitigation and hardeningRestrict dataset access using IAM, RBAC, or identity-aware proxies.Store all datasets in versioned, signed, and hashed formats.Validate datasets with automated schema checks, label distribution scans, and statistical outlier detection before training.Track data provenance with metadata logs and checksums.Block training runs if datasets fail predefined data quality gates.Integrate data validation scripts into CI/CD pipelines pre-training.Enforce zero-trust access policies for data ingestion services.Solution integration: zero-trust AIImplement continuous authentication and authorization for each component interacting with data (e.g., preprocessing scripts, training jobs).Enable real-time threat detection during training using runtime security tools.Automate incident response triggers for unexpected file access or data source changes.Risk #2: adversarial attacksAdversarial attacks manipulate model inputs in subtle ways that trick AI systems into making incorrect or dangerous decisions. These perturbations—often imperceptible to humans—can cause models to misclassify images, misinterpret speech, or misread sensor data. In high-stakes environments like facial recognition, autonomous vehicles, or fraud detection, these failures can result in security breaches, legal liabilities, or physical harm.For developers, the threat is real: even state-of-the-art models can be easily fooled without adversarial hardening. The good news? You can make your models more robust by combining defensive training techniques, input sanitization, and secure API practices. While encrypted inference doesn’t directly block adversarial manipulation, it ensures that sensitive inference data stays protected even if attackers attempt to probe the system.Mitigation and hardeningUse adversarial training frameworks like CleverHans or IBM ART to expose models to perturbed inputs during training.Apply input sanitization layers (e.g., JPEG re-encoding, blurring, or noise filters) before data reaches the model.Implement rate limiting and authentication on inference APIs to block automated adversarial probing.Use model ensembles or randomized smoothing to improve resilience to small input perturbations.Log and analyze input-output patterns to detect high-variance or abnormal responses.Test models regularly against known attack vectors using robustness evaluation tools.Solution integration: encrypted inferenceWhile encryption doesn't prevent adversarial inputs, it does mean that input data and model responses remain confidential and protected from observation or tampering during inference.Run inference in trusted environments like Intel SGX or AWS Nitro Enclaves to protect model and data integrity.Use homomorphic encryption or SMPC to process encrypted data without exposing sensitive input.Ensure that all intermediate and output data is encrypted at rest and in transit.Deploy access policies that restrict inference to verified users and approved applications.Risk #3: model leakage of intellectual assetsModel leakage—or model extraction—happens when an attacker interacts with a deployed model in ways that allow them to reverse-engineer its structure, logic, or parameters. Once leaked, a model can be cloned, monetized, or used to bypass the very defenses it was meant to enforce. For businesses, this means losing competitive IP, compromising user privacy, or enabling downstream attacks.For developers and MLOps teams, the challenge is securing deployed models in a way that balances performance and privacy. If you're exposing inference APIs, you’re exposing potential entry points—but with the right controls and architecture, you can drastically reduce the risk of model theft.Mitigation and hardeningEnforce rate limits and usage quotas on all inference endpoints.Monitor for suspicious or repeated queries that indicate model extraction attempts.Implement model watermarking or fingerprinting to trace unauthorized model use.Obfuscate models before deployment using quantization, pruning, or graph rewriting.Disable or tightly control any model export functionality in your platform.Sign and verify inference requests and responses to ensure authenticity.Integrate security checks into CI/CD pipelines to detect risky configurations—such as public model endpoints, export-enabled containers, or missing inference authentication—before they reach production.Solution integration: native security integrationIntegrate model validation, packaging, and signing into CI/CD pipelines.Serve models from encrypted containers or TEEs, with minimal runtime exposure.Use container and image scanning tools to catch misconfigurations before deployment.Centralize monitoring and protection with tools like Gcore WAAP for real-time anomaly detection and automated response.How investing in AI security can save your business moneyFrom a financial point of view, the use of AI and machine learning in cybersecurity can lead to massive cost savings. Organizations that utilize AI and automation in cybersecurity have saved an average of $2.22 million per data breach compared to organizations that do not have these protections in place. This is because the necessity for manual oversight is reduced, lowering the total cost of ownership, and averting costly security breaches. The initial investment in advanced security technologies yields returns through decreased downtime, fewer false positives, and an enhanced overall security posture.Challenges aheadWhile securing the AI lifecycle is essential, it’s still difficult to balance robust security with a positive user experience. Rigid scrutiny can add additional latency or false positives that can stop operations, but AI-powered security can avoid such incidents.Another concern organizations must contend with is how to maintain current AI models. With threats changing so rapidly, today's newest model could easily become outdated by tomorrow’s. Solutions must have an ongoing learning ability so that security detection parameters can be revised.Operational maturity is also a concern, especially for companies that operate in multiple geographies. Well-thought-out strategies and sound governance processes must accompany the integration of complex AI/ML tools with existing infrastructure, but automation still offers the most benefits by reducing the overhead on security teams and helping ensure consistent deployment of security policies.Get ahead of AI security with GcoreAI workloads introduce new and often overlooked security risks that can compromise data integrity, model behavior, and intellectual property. By implementing practices like zero-trust architecture, encrypted inference, and native security integration, developers can build more resilient and trustworthy AI systems. As threats evolve, staying ahead means embedding security at every phase of the AI lifecycle.Gcore helps teams apply these principles at scale, offering native support for zero-trust AI, encrypted inference, and intelligent API protection. As an experienced AI and security solutions provider, our DDoS Protection and AI-enabled WAAP solutions integrate natively with Everywhere Inference and GPU Cloud across 210+ global points of presence. That means low latency, high performance, and proven, robust security, no matter where your customers are located.Talk with our AI security experts and secure your workloads today
Flexible DDoS mitigation with BGP Flowspec
For customers who understand their own network traffic patterns, rigid DDoS protection can be more of a limitation than a safeguard. That’s why Gcore supports BGP Flowspec: a flexible, standards-based method for defining granular filters that block or rate-limit malicious traffic in real time…before it reaches your infrastructure.In this article, we’ll walk through:What Flowspec is and how it worksThe specific filters and actions Gcore supportsCommon use cases, with example rule definitionsHow to activate and monitor Flowspec in your environmentWhat is the BGP Flowspec?BGP Flowspec (RFC 8955) extends Border Gateway Protocol to distribute traffic filtering rules alongside routing updates. Instead of static ACLs or reactive blackholing, Flowspec enables near-instantaneous propagation of mitigation rules across networks.BGP tells routers how to reach IP prefixes across the internet. With Flowspec, those same BGP announcements can now carry rules, not just routes. Each rule describes a pattern of traffic (e.g., TCP SYN packets >1000 bytes from a specific subnet) and what action to take (drop, rate-limit, mark, or redirect).What are the benefits of the BGP Flowspec?Most traditional DDoS protection services react to threats after they start, whether by blackholing traffic to a target IP, redirecting flows to a scrubbing center, or applying rigid, static filters. These approaches can block legitimate traffic, introduce latency, or be too slow to respond to fast-evolving attacks.Flowspec offers a more flexible alternative.Proactive mitigation: Instead of waiting for attacks, you can define known-bad traffic patterns ahead of time and block them instantly. Flowspec lets experienced operators prevent incidents before they start.Granular filtering: You’re not limited to blocking by IP or port. With Flowspec, you can match on packet size, TCP flags, ICMP codes, and more, enabling fine-tuned control that traditional ACLs or RTBH don’t support.Edge offloading: Filtering happens directly on Gcore’s routers, offloading your infrastructure and avoiding scrubbing latency.Real-time updates: Changes to rules are distributed across the network via BGP and take effect immediately, faster than manual intervention or standard blackholing.You still have the option to block traffic during an active attack, but with Flowspec, you gain the flexibility to protect services with minimal disruption and greater precision than conventional tools allow.Which parts of the Flowspec does Gcore implement?Gcore supports twelve filter types and four actions of the Flowspec.Supported filter typesGcore supports all 12 standard Flowspec match components.Filter FieldDescriptionDestination prefixTarget subnet (usually your service or app)Source prefixSource of traffic (e.g., attacker IP range)IP protocolTCP, UDP, ICMP, etc.Port / Source portMatch specific client or server portsDestination portMatch destination-side service portsICMP type/codeFilter echo requests, errors, etc.TCP flagsFilter packets by SYN, ACK, RST, FIN, combinationsPacket lengthFilter based on payload sizeDSCPQuality of service code pointFragmentMatch on packet fragmentation characteristicsSupported actionsGcore DDoS Protection supports the following Flowspec actions, which can be triggered when traffic matches a specific filter:ActionDescriptionTraffic-rate (0x8006)Throttle/rate limit traffic by byte-per-second rateredirectRedirect traffic to alternate location (e.g., scrubbing)traffic-markingApply DSCP marks for downstream classificationno-action (drop)Drop packets (rate-limit 0)Rule orderingRFC 5575 defines the implicit order of Flowspec rules. The crucial point is that more specific announcements take preference, not the order in which the rules are propagated.Gcore also respects Flowspec rule ordering per RFC 5575. More specific filters override broader ones. Future support for Flowspec v2 (with explicit ordering) is under consideration, pending vendor adoption.Blackholing and extended blackholing (eBH)Remote-triggered blackhole (RTBH) is a standardized protection method that the client manages via BGP by analyzing traffic, identifying the direction of the attack (i.e., the destination IP address). This method protects against volumetric attacks.Customers using Gcore IP Transit can trigger immediate blackholing for attacked prefixes via BGP, using the well-known blackhole community tag 65000:666. All traffic to that destination IP is dropped at Gcore’s edge.The list of supported BGP communities is available here.BGP extended blackholeExtended blackhole (eBH) allows for more granular blackholing that does not affect legitimate traffic. For customers unable to implement Flowspec directly, Gcore supports eBH. You announce target prefixes with pre-agreed BGP communities, and Gcore translates them into Flowspec mitigations.To configure this option, contact our NOC at noc@gcore.lu.Monitoring and limitationsGcore can support several logging transports, including mail and Slack.If the number of Flowspec prefixes exceeds the configured limit, Gcore DDoS Protection stops accepting new announcements, but BGP sessions and existing prefixes will stay active. Gcore will receive a notification that you reached the limit.How to activateActivation takes just two steps:Define rules on your edge router using Flowspec NLRI formatAnnounce rules via BGP to Gcore’s intermediate control planeThen, Gcore validates and propagates the filters to border routers. Filters are installed on edge devices and take effect immediately.If attack patterns are unknown, you’ll first need to detect anomalies using your existing monitoring stack, then define the appropriate Flowspec rules.Need help activating Flowspec? Get in touch via our 24/7 support channels and our experts will be glad to assist.Set up GRE and benefit from Flowspec today
Securing AI from the ground up: defense across the lifecycle
As more AI workloads shift to the edge for lower latency and localized processing, the attack surface expands. Defending a data center is old news. Now, you’re securing distributed training pipelines, mobile inference APIs, and storage environments that may operate independently of centralized infrastructure, especially in edge or federated learning contexts. Every stage introduces unique risks. Each one needs its own defenses.Let’s walk through the key security challenges across each phase of the AI lifecycle, and the hardening strategies that actually work.PhaseTop threatsHardening stepsTrainingData poisoning, leaksValidation, dataset integrity tracking, RBAC, adversarial trainingDevelopmentModel extraction, inversionRate limits, obfuscation, watermarking, penetration testingInferenceAdversarial inputs, spoofed accessInput filtering, endpoint auth, encryption, TEEsStorage and deploymentModel theft, tamperingEncrypted containers, signed builds, MFA, anomaly monitoringTraining: your model is only as good as its dataThe training phase sets the foundation. If the data going in is poisoned, biased, or tampered with, the model will learn all the wrong lessons and carry those flaws into production.Why it mattersData poisoning is subtle. You won’t see a red flag during training logs or a catastrophic failure at launch. These attacks don’t break training, they bend it.A poisoned model may appear functional, but behaves unpredictably, embeds logic triggers, or amplifies harmful bias. The impact is serious later in the AI workflow: compromised outputs, unexpected behavior, or regulatory non-compliance…not due to drift, but due to training-time manipulation.How to protect itValidate datasets with schema checks, label audits, and outlier detection.Version, sign, and hash all training data to verify integrity and trace changes.Apply RBAC and identity-aware proxies (like OPA or SPIFFE) to limit who can alter or inject data.Use adversarial training to improve model robustness against manipulated inputs.Development and testing: guard the logicOnce you’ve got a trained model, the next challenge is protecting the logic itself: what it knows and how it works. The goal here is to make attacks economically unfeasible.Why it mattersModels encode proprietary logic. When exposed via poorly secured APIs or unprotected inference endpoints, they’re vulnerable to:Model inversion: Extracting training dataExtraction: Reconstructing logicMembership inference: Revealing whether a datapoint was in trainingHow to protect itApply rate limits, logging, and anomaly detection to monitor usage patterns.Disable model export by default. Only enable with approval and logging.Use quantization, pruning, or graph obfuscation to reduce extractability.Explore output fingerprinting or watermarking to trace unauthorized use in high-value inference scenarios.Run white-box and black-box adversarial evaluations during testing.Integrate these security checks into your CI/CD pipeline as part of your MLOps workflow.Inference: real-time, real riskInference doesn’t get a free pass because it’s fast. Security needs to be just as real-time as the insights your AI delivers.Why it mattersAdversarial attacks exploit the way models generalize. A single pixel change or word swap can flip the classification.When inference powers fraud detection or autonomous systems, a small change can have a big impact.How to protect itSanitize input using JPEG compression, denoising, or frequency filtering.Train on adversarial examples to improve robustness.Enforce authentication and access control for all inference APIs—no open ports.Encrypt inference traffic with TLS. For added privacy, use trusted execution environments (TEEs).For highly sensitive cases, consider homomorphic encryption or SMPC—strong but compute-intensive solutions.Check out our free white paper on inference optimization.Storage and deployment: don’t let your model leakOnce your model’s trained and tested, you’ve still got to deploy and store it securely—often across multiple locations.Why it mattersUnsecured storage is a goldmine for attackers. With access to the model binary, they can reverse-engineer, clone, or rehost your IP.How to protect itStore models on encrypted volumes or within enclaves.Sign and verify builds before deployment.Enforce MFA, RBAC, and immutable logging on deployment pipelines.Monitor for anomalous access patterns—rate, volume, or source-based.Edge strategy: security that moves with your AIAs AI moves to the edge, centralized security breaks down. You need protection that operates as close to the data as your inference does.That’s why we at Gcore integrate protection into AI workflows from start to finish:WAAP and DDoS mitigation at edge nodes—not just centralized DCs.Encrypted transport (TLS 1.3) and in-node processing reduce exposure.Inline detection of API abuse and L7 attacks with auto-mitigation.180+ global PoPs to maintain consistency across regions.AI security is lifecycle securityNo single firewall, model tweak, or security plugin can secure AI workloads in isolation. You need defense in depth: layered, lifecycle-wide protections that work at the data layer, the API surface, and the edge.Ready to secure your AI stack from data to edge inference?Talk to our AI security experts
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