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What Is OWASP and What Is the OWASP Top 10?

  • By Gcore
  • October 11, 2023
  • 10 min read
What Is OWASP and What Is the OWASP Top 10?

The OWASP (Open Web Application Security Project) Top 10 is a list of the most critical and widespread application security risks, chosen by top security experts based on data from hundreds of thousands of applications. In this article, you’ll learn how to use the OWASP Top 10 to protect your web app from cyberthreats, how to test for these vulnerabilities, and how to avoid the OWASP Top 10 vulnerabilities.

What Is the OWASP Top 10? Why Does It Matter?

The OWASP Top 10 is a list of today’s ten most critical and widespread web app security risks. Top security experts update the list every few years based on data from hundreds of thousands of applications. Each risk includes a description, vulnerability examples, attack examples, guidance on how to avoid them, and references to more detailed resources. Public-facing applications were the most common initial attack vector in 2022, and almost 70% of applications have flaws that fall under the OWASP Top 10, meaning that most apps have cybersecurity flaws that could be remedied by paying attention to the OWASP Top 10.

The OWASP Top 10 isn’t the only application security framework—there are others more advanced and industry specific, such as ISO/IEC 27001 and NIST Cybersecurity Framework—and it’s not comprehensive, since it addresses only the most common vulnerabilities. But the OWASP Top 10 list is a good place to start with your application security program, since it covers the most common and dangerous vulnerabilities that you should fix first.

How Are the OWASP Top 10 Selected?

To choose and prioritize the top security risks, OWASP experts rely on two sources of information and go through the following steps.

  1. A call for data is publicized through social media channels.
  2. Security companies submit their data.
  3. Vulnerabilities are categorized based on their root cause.
  4. The OWASP team selects eight top risk categories based on incidence rate.
  5. Eight categories are ranked in order of risk based on generalized factors for exploitability and technical impact.
  6. Seasoned security practitioners are surveyed to identify the last two risks.
  7. The process is repeated every three to four years.

As mentioned, the OWASP Top 10 updates every 3-4 years. Here is what had changed in the last review of the list in 2021. The dotted arrows illustrate vulnerabilities that became a part of another related category. Solid arrows indicate a risk’s shift in priority ranking position. There are also three new risk categories.

Recent changes to the OWASP Top 10

How Businesses Use OWASP Top 10

Businesses can use the OWASP Top 10 in a number of ways to improve their web app security stance.

  • Awareness: OWASP Top 10 informs a broad audience including non-technical staff and executives. It allows developers, security experts, and managers to be on the same page about web app security priorities.
  • Training: OWASP Top 10 can be a training core for developers, security professionals, and other technical staff involved in software development.
  • Prioritization: OWASP Top 10 highlights which application security areas a business should fix first. By identifying the top risks from the list for your company, you’ll be empowered to allocate your resources appropriately.
  • Software development: The OWASP Top 10 is used in almost every stage of the software development cycle:
    • During the design phase, to ensure a team implements baseline security practices
    • In the software-development supply chain, to ensure the security of third-party components and services used to develop an application
    • As a checklist for secure code review
    • As a reference for unit testing, integration testing, and penetration testing
  • Security compliance: Health Insurance Portability and Accountability Act (HIPAA) or Payment Card Industry Data Security Standard (PCI DSS)-compliant organizations use the OWASP Top 10 as a checklist to measure the application security level of their partners.
  • Contractor requirements. Some companies include the OWASP Top 10 list in contractor requirements. For example, you can set security expectations for a software development company.
  • Hiring. Companies may employ the OWASP Top 10 to assess the candidate’s security knowledge.

The OWASP Top 10 List Overview

To better understand how the OWASP Top 10 vulnerabilities relate to your web application and how they can impact your business, let’s walk through each category.

RiskRisk explanation ExampleFactorsSpecific consequences (general below)
1. Broken access controlIneligible users can access resources or perform actions for which they don’t have permission. In the worst case scenario, the threat actor can modify a website’s content or take over the entire website.Various WordPress plugins are susceptible to this type of attack. Successful exploitation allows a hacker to take over a site with this or that plugin enabled.– Inadequate implementation of authentication and authorization controls that doesn’t correspond with potential risks for your specific business case. – Adding unverified third-party components to your software.- Outdated access control mechanisms, such as static permissions or single authentication point.
2. Cryptographic failuresCryptography protects your data by converting it into an unreadable and secure form. When the cryptography process fails, attackers can access your data.An example of this is one of the LastPass 2022 attacks. Some of their source code and technical information was stolen from their development environment. The hacker used the information to target an employee, obtained credentials and keys to decrypt some storage volumes, and accessed basic customer account information.– Outdated cryptographic algorithms; for example, Triple DES (Data Encryption Algorithm.) You can check for cryptographic algorithms’ updates on the NIST (National Institute of Standards and Technology) website. – Implementation of outdated key management practices (encryption keys that are not securely generated, stored, or protected.)- Use of default or weak keys, meaning they aren’t long and complex enough to resist brute-force attacks.- Unprotected communication channels e.g., HTTP instead of HTTPS.
3. Injection vulnerabilitiesA web application fails to filter, sanitize, or validate users’ commands or added data. Threat actors use such attacks to access sensitive data, manipulate the application’s behavior, or perform unauthorized actions.In spring 2023 attackers were able to install a web shell on the MOVEit Transfer application via SQL injection vulnerability and extract all data contained on the app.Poor user input validation that doesn’t correspond to best practices. For example, an application that allows users to upload files without checking file types.
4. Insecure designThe usage of code patterns and features that are insecure by default. For example, lack of input validation or embedding credentials (e.g., usernames, passwords, API keys) directly within the source code.A cinema chain provides discounts for group bookings accommodating up to fifteen individuals. Exploiting this vulnerability, malicious individuals could attempt to secure all available cinema seats in just a few clicks. When a website allows numerous reservations without mandating a deposit or credit card details, it becomes susceptible to significant revenue loss over time.Not all of the necessary application’s security controls are implemented throughout the software development process.– Limited business growth. Potential clients and partners may have concerns about a company’s security posture.

– Higher cost of remediation. To fix a vulnerability you might need fundamental changes.
5. Security misconfigurationThese arise from an insecurely configured application stack. Default account passwords, unnecessary features, and insecure settings in application frameworks and libraries are all potential openings for an attack.Users of Apache Tomcat, an open-source Java application server, had default username and password credentials. Threat actors targeted this misconfiguration and gained control over the server to spread Marai botnet malware.
 
A lack of knowledge about how to securely configure software used with an application. For example, failure to use configuration best practices for specific software.
6. Vulnerable and outdated componentsUsing outdated and/or unsupported software, tools, libraries, frameworks, and infrastructure. Hackers can use such components’ flaws to enter your perimeter.Open-source libraries are widespread because they increase go-to-market speed. However, they are often subject to attacks—hackers can reach multiple applications at once. Failing to timely update a library (which might be embedded in the core of your app) will result in a high risk of being hacked.A company doesn’t have a system and tools to monitor new versions of components they are using and those that are no longer updated.
7. Identification and authentication failuresWeak login and access control mechanisms. For instance, if a system accepts easy-to-guess passwords and lacks multi-factor authentication, it can lead to unauthorized access to users’ accounts and data breaches.In the 2023 Norton Life Lock case, over 6,000 accounts were breached due to staffing, meaning that users used the same passwords as in other systems with which they are registered. The company offered their users multi-factor authentication as an option, but it wasn’t obligatory.Insufficient identification and authentication mechanisms, like failing to implement specific authentication requirements for the relevant app type.– Users’ sensitive data stolen.

– Unauthorized transactions on the users’ behalf.
8. Software and data integrity failuresFailure to check the integrity and security of third-party components such as plugins, libraries, or modules. A threat actor can make unauthorized changes to the software or data, like introducing malicious code in an application.A high-profile example of the risk is SolarWinds case. Hackers added malicious code to one of their products used by 33,000 customers, including big corporations and government agencies. These malicious updates were sent to all clients.Failure to verify the source of software or data; failure to check new components for vulnerabilities.
 
9. Security logging and monitoring failuresFailure to track all activities within an application. Without proper monitoring, it becomes difficult to detect suspicious activities promptly or figure out the root cause of a breach and fix it. A hospital’s electronic health records system suffered a data breach because the security team failed to configure proper logging and monitoring. An insider accessed and stole patient records. The breach went unnoticed until the stolen data started surfacing on the dark web.– Auditable events not logged.

– Suspicious activity not monitored in application logs or API logs.

– Logs only stored locally, meaning it is easy to lose them and it’s impossible to gain a holistic view of the application’s security.
Prolonged data breaches, when attacks or malicious activities go undetected for an extended period.
10. Server side request forgery (SSRF) An application fetches data to the user without validating their URL request. A malicious user can send a crafted request to an unexpected destination in your system.A cloud security company Orca Security discovered numerous SSRF vulnerabilities in Azure with only minimal effort. Microsoft addressed the flaws.– Poor user input validation. For instance, an app doesn’t validate URL structure and restrict certain URL schemes; lacks URL whitelisting.

– Enabled HTTP redirections, so attackers may manipulate redirection URLs to access internal resources.

– A system allows raw responses from the server to the client.
Port scanning for vulnerable services or potential entry points into the network.

General OWASP Top 10 Causes and Implications

As well as the risk-specific implications in the table above, there are a number of causes and implications of the OWASP Top 10 that are relevant across the list.

General Causes

  • Application complexity: In complex apps, developers may unintentionally overlook some security risks. For example, multiple interdependencies and extensive functionality create such a risk.
  • Insufficient testing: Security testing needs to be sufficient or various vulnerabilities could be overlooked. Examples of insufficient testing include a company using only automated scanners without manual code review and penetration testing, or conducting testing on an app less than once a year.
  • Rapid development: If an app is developed unusually fast, certain vulnerabilities may not be sufficiently addressed.

General Business Consequences

  • Reputation and financial loss: Assuming they become public—as they almost certainly will—data breaches resulting from OWASP Top 10 vulnerabilities can damage a company’s reputation and lose customers’ trust. A natural result is financial loss, as customers take their business elsewhere.
  • Legal ramifications: Lawsuits may occur when affected users seek damages.
  • Compliance violations: If your business is subject to regulatory requirements such as HIPAA or PCI DSS, having OWASP Top 10 vulnerabilities in your app can lead to compliance violations. The Top 10 list intersects with these standards. For example, if personal health information is compromised, HIPAA compliance is violated, which means you are liable for financial penalties.
  • Downtime: System disruption and downtime may result when a breach occurs, and loss of service means loss of revenue and a damaged reputation as your customers turn to competitors. 

How to Test Your App for OWASP Top 10 Vulnerabilities

Let’s explore the most common ways to test your software for these top app security risks. To test for the whole OWASP Top 10 effectively, you should implement all three methods. Here, we list the testing methods from the most basic to the most advanced.

Automated Scanners: SAST and DAST

Both static application security testing (SAST) and dynamic application security testing (DAST) are automated vulnerability scanners. SAST is used during the development phase to review software code for common secure coding errors. Most SAST solutions can test your application for these OWASP Top 10 categories:

  • Injection flaws
  • Identification and authentication failures (limited vulnerabilities only)
  • Vulnerable and outdated components
  • Software and data integrity failures

DAST tools test a running application. Companies use DAST solutions in both testing and production environments. They attack software and document how it responds to malicious inputs. With DAST, you can test your app for:

  • Injection
  • Security misconfiguration (with limited capacity)
  • Identification and authentication failures
  • Broken access control

Secure Code Review

Secure code review is a small part of the code review process whereby reviewers manually inspect the source code to identify application vulnerabilities. A human reviewer can check for those OWASP Top 10 issues that aren’t fully testable automatically, like logical problems and design flaws. This method is most effective when used in combination with automated scanners (SAST and DAST.) A secure code review is conducted during the development phase and during major application updates—ideally, with every new feature and code change. 

Secure code review allows you to check for these OWASP Top 10 flaws: 

  • Broken access control
  • Injection flaws
  • Cryptographic failures
  • Identification and authentication failures

Penetration Testing

Web application penetration testing (pen testing) involves simulating an attack on an application. Pen testing allows you to test for all OWASP Top 10 issues. The method isn’t a substitute for the methods discussed above. However, it allows you to get the most comprehensive and accurate information about OWASP Top 10 flaws in your application.
Ideally, you should invest in a web application penetration testing service during the testing stage of your software development to identify all vulnerabilities early on. After a critical application is in production, we recommend pen testing it with every major update or even more often, depending on your risk tolerance.

How to Mitigate OWASP Top 10 Risks

To decrease OWASP Top 10 risks, start with these general application security best practices that apply to the list as a whole.

Educate Your Development Team

Many OWASP Top 10 vulnerabilities arise because the development team hasn’t implemented certain security measures in an app code. Security isn’t developers’ main focus, so they may consider it extra work that will go unnoticed, or they might not be up to date on best practices.

Upskilling your team with an OWASP-specific course is a good first step on the way to diminishing OWASP Top 10 risks. When choosing a training solution make sure that the OWASP Top 10 course:

  • Is engaging, relevant to your specific projects, and actionable, so that your developers can implement the learnings right away.
  • Clearly and credibly explains the importance of secure coding for developers’ work.

Conduct training regularly, since new vulnerabilities, secure coding practices, and tools continuously arise.

Motivate Developers To Implement Secure Coding Practices

After training, your goal as a manager is to enable change in your development team’s working routine. Here is what you can do about it:

  • Involve internal or external security experts to help developers prioritize which vulnerabilities to fix first.
  • Develop and support security champions—team members who are application security enthusiasts.
  • Make application security a factor in assessing the development team’s performance.
  • Integrate security testing into developer workflows.

Prioritize The Risks For Your Specific Case

OWASP Top 10 is listed in risk-based order. However, each company’s security priorities may differ. You want to fix flaws that are most critical specifically to you. For example, despite broken access control being a top-ranking risk based on OWASP data, it will have lower priority for internal-only applications. Here’s how to prioritize OWASP Top 10 flaws for your company:

  1. Take the list of vulnerabilities you found during testing for OWASP Top 10.
  2. Identify vulnerabilities that affect key business processes, mission-critical systems, or sensitive data.
  3. Rank them based on how easy it is to break through your controls.
  4. Take into account a vulnerability category position in the OWASP Top 10 list.

Use Application Security Tools

Using security tools decreases human errors and makes it easier for your development team to achieve a good level of application security. Here are the tool types we recommend using.

  • A web application firewall (WAF) filters software traffic and stops suspicious requests or potential attacks. For example, a WAF can prevent injection risks by blocking malicious inputs. Here is how it works, using Gcore WAF as an example:

  • Security libraries help developers implement secure coding practices without reinventing the wheel. They are prebuilt software components with built-in security measures. They include input validation libraries, access control libraries, and authentication libraries.
  • Dependency scanners check for common vulnerabilities in open-source components of your application. Such solutions review your application for vulnerable components based on updated threats and scan newly added components for known vulnerabilities.
  • Configuration management (CM) tools help to ensure that machines, software packages, and updates are configured and installed correctly. CM tools may also provide version control and change control.

Continuously Test Your Software

Test your application at multiple points during the development cycle and after each major software update as a minimum. The precise testing frequency depends on your business’ risk tolerance, development practices, application complexity, regulatory requirements, and the evolving threat landscape.

Conclusion

Compliance with OWASP Top 10 (and other security frameworks) is the basis for application security. However, the OWASP Top 10 doesn’t cover all existing vulnerabilities and is updated only every 3–4 years, while new attack techniques come through far more frequently. Your business and application specifics will determine exactly how you ensure compliance with the OWASP Top 10, and this guide is a good starting point to ensuring your web app’s security corresponds with the list.

To protect your business fully, save money, and effectively prioritize risks, consider outsourcing your web application security to experts. The Gcore penetration testing team can help you identify priority risks for your organization including and beyond the OWASP Top 10, and our WAAP product (Web Application Firewall and API Protection) mitigates any and all security threats to your app.

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The breach typically involves exploiting misconfigurations, compromised credentials, or vulnerabilities to access cloud infrastructure, applications, or data stores.

Query your cloud with natural language: A developer’s guide to Gcore MCP

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

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 cover image

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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