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What Is Web Scraping? | Scraper Tools and Bots

  • By Gcore
  • June 27, 2023
  • 10 min read
What Is Web Scraping? | Scraper Tools and Bots

Web scraping extracts valuable and often personal data from websites, web applications, and APIs, using either scraper tools or bots that crawl the web looking for data to capture. Once extracted, data can be used for either good or bad purposes. In this article, we’ll take a closer look at web scraping and the risks that malicious web scraping poses for your business. We’ll compare scraper tools and bots, look at detailed examples of malicious web scraping activities, and explain how to protect yourself against malicious web scraping.

What Is Web Scraping?

Web scraping is a type of data scraping that extracts data from websites using scraper tools and bots. It is also called website scraping, web content scraping, web harvesting, web data extraction, or web data mining. Web scraping can be performed either manually or via automation, or using a hybrid of the two.

Data—including text, images, video, and structured data (like tables)—can be extracted via web scraping. Such data can, with varying levels of difficulty, be scraped from any kind of website, including static and dynamic websites. The extracted data is then exported as structured data.

When used ethically, like for news or content aggregation, market research, or weather forecasting, web scraping can be beneficial. However, it can be malicious when used for harmful purposes, like price scraping and content scraping (more on these uses later.)

How Does Web Scraping Work?

Web scraping is carried out using a scraper tool or bot, and the basic process is the same for both:

  1. A person or bad actor deploys a scraper tool on a target website, or installs a bot.
  2. The scraper tool or bot sends automated requests to the website’s server requesting page-specific HTML code.
  3. The server responds with the HTML code as requested.
  4. The scraper tool or bot parses the supplied HTML code and extracts data—including databases—according to user-specific parameters.
  5. The scraper tool or bot then stores the extracted data in a structured format, such as a JSON or CSV file, for later use.

There are three scraping techniques: automated, manual, and hybrid. Manual scraping is the process of extracting data from websites manually, typically by copying and pasting or using web scraping tools that require human intervention. Automated scraping involves using software tools to extract data automatically from websites. Hybrid scraping combines both manual and automated techniques: manual methods are used to handle complex or dynamic elements of a website; automation is used for repetitive and simple tasks.

What Are Scraper Tools and Bots?

Scraper tools and bots are software programs designed to automatically extract data from websites by navigating through web pages and collecting the desired information. Scraper tools and bots can both facilitate large-scale, high-speed web scraping. They are easily confused because they can serve the same purpose—in this case, web scraping. However, scraper tools and bots are actually two different things.

Scraper tools are tools specifically developed for web scraping purposes. Bots are general-purpose software that can be designed to perform a variety of automated tasks, including web scraping. Let’s take a look at each in turn.

What Are Scraper Tools?

Scraper tools, also known as web scrapers, are programs, software, or pieces of code designed specifically to scrape or extract data. They feature a user interface and are typically built using programming languages such as Python, Ruby, Node.js, Golang, PHP, or Perl.

There are four classes of scraper tools:

  • Open-source/pre-built web scrapers (e.g., BeautifulSoup, Scrapy)
  • Off-the-shelf web scrapers (e.g., Import.io, ParseHub)
  • Cloud web scrapers (e.g., Apify, ScrapingBee)
  • Browser extension web scrapers (e.g., WebScraper.io, DataMiner)

As these tool classes suggest, scraper tools can be run as desktop applications or on a cloud server. They can be deployed using headless browsers, proxy servers, and mobile applications. Most options are free and do not require any coding or programming knowledge, making them easily accessible.

Scraper tools can also be categorized by their use case:

  • Search engine scrapers (e.g., Google Search API, SERP API, Scrapebox)
  • Social media scrapers (e.g., ScrapeStorm, PhantomBuster, Sociality.io)
  • Image scrapers (e.g., Image Scraper, Google Images Download, Bing Image Search API)
  • Ecommerce scrapers (e.g., Price2Spy, SellerSprite, Import.io)
  • Video scrapers (e.g., YouTube Data API, Vimeo API, Dailymotion API)
  • Web scraping frameworks or libraries (e.g., BeautifulSoup, Scrapy, Puppeteer)
  • Music lyrics scrapers (e.g., LyricsGenius, Lyric-Scraper)

What Are Bots?

Unlike scraper tools that are specifically designed for web scraping, bots or robots are software/programs that can automate a wide range of tasks. They can gather weather updates, automate social media updates, generate content, process transactions—and also perform web scraping. Bots can be good or bad. Check out our article on good and bad bots and how to manage them for more information.

Bots don’t have a user interface, and are typically written in popular programming languages like Python, Java, C++, Lisp, Clojure, or PHP. Some bots can automate web scraping at scale and simultaneously cover their tracks by using different techniques like rotating proxies and CAPTCHA solving. Highly sophisticated bots can even scrape dynamic websites. Evidently, bots are powerful tools, whether for good or for bad.

Examples of good bots include:

  • Chatbots (e.g., Facebook Messenger, ChatGPT)
  • Voice bots (e.g., Siri, Alexa)
  • Aggregators or news bots (e.g., Google News, AP News)
  • Ecommerce bots (e.g., Keepa, Rakuten Slice)
  • Search engine crawlers (e.g., Googlebot, Bingbot)
  • Site monitoring bots (e.g., Uptime Robot, Pingdom)
  • Social media crawlers (e.g., Facebook crawler, Pinterest crawler)

Examples of bad bots include:

  • Content scrapers (more on these later)
  • Spam bots (e.g., email spam bots, comment spam bots, forum spam bots)
  • Account takeover bots (e.g., SentryMBA [credential stuffing], Medusa [brute-force bot], Spyrix Keylogger [credential harvesting bots])
  • Social media bots (e.g., bot followers, Like/Retweet bots, political bot squads)
  • Click fraud bots (e.g., Hummingbad, 3ve/Methuselah, Methbot)
  • DDoS bots (e.g., Reaper/IoTroop, LizardStresser, XOR DDoS)

Comparison of Scraper Tools vs Bots

Scraper tools and bots can both perform web scraping, but have important differences. Let’s check out the differences between scraper tools and bots.

CriteriaScraper ToolBot
PurposeAutomated web scrapingAutonomous task automation for web scraping or other purposes
User InterfaceUser interface (UI), command lineNo UI, standalone script
Technical skillsSome programming and web scraping know-how (no-code options available)Advanced programming and web scraping know-how
Programming languagePython, Ruby, Node.js, Golang, PHP, and PerlPython, Java, C++, Lisp, Clojure, and PHP
Good or badDepends on intent and approachGood bots and bad bots both exist
ExamplesBeautifulSoup, ScrapyGooglebot, BingBot, Botnet
Benign use caseWeather forecast, price recommendation, job listingsSearch engine indexing, ChatGPT, Siri/Alexa
Malicious use caseWeb content scraping, price scrapingSpamming, DoS/DDoS, botnets

What Is Malicious Web Scraping?

Malicious web scraping refers to any undesirable, unauthorized, or illegal use of web scraping. Examples include:

  • Any unauthorized web scraping
  • Web scraping that violates terms of service
  • Web scraping that is used to facilitate other types of malicious attacks
  • Any activity that causes severe negative effects to a server or service, including the one being scraped

This table will help you to determine if a particular web scraping activity is benign or malicious.

CriteriaConsiderationBenign web scrapingMalicious web scraping
AuthorizationWas approval granted before web scraping?YesNo
IntentWhat was the original purpose for this web scraping?GoodBad
ApproachHow was the web scraping carried out?Ethically, harmlessUnethically, harmful
ImpactWhat was the impact of the web scraping approach on the scraped server or site?None/slightSevere

Sometimes, even with authorization and good intent, the approach to carrying out web scraping may be inappropriate, resulting in a severe impact on the server or services being scraped.

Examples of Malicious Web Scraping

Malicious web scraping can seriously harm any business. It is important to know what to look out for so you can identify any cases of web scraping that could negatively affect your business. Here are some examples of malicious web scraping activities.

TypeActivityIntent
Social media user profile scrapingScraping social media platforms to extract user profiles or personal informationTargeted advertising, identity profiling, identity theft
Healthcare data extractionScraping healthcare provider websites to access patient records, SSN, and medical informationIdentity theft, blackmail, credit card fraud
API scrapingScraping web or mobile app APIsReverse engineering or maliciously cloning apps
Email/contact scrapingScraping email addresses and contact information from web pagesSpamming, phishing/smishing, malware distribution
Reviews/rating manipulationScraping reviews and rating sites or servicesPosting fake positive reviews for self or fake negative reviews against competitors
Personal data harvestingScraping personal information like SSN, date of birth, and credit card detailsIdentity theft, impersonation, credit card fraud
Ad fraud scrapingScraping advertising networks and platforms looking for ad placementsFalse ad impressions, click fraud
Protected content scrapingScraping protected or gated contentTargeting log-in credentials and credit card information
Web scraping for malware distributionScraping content to create spoofing/phishing sitesDistributing malware disguised as software downloads
Automated account creationCreating fake user accounts using web scraping techniques and credential stuffingSpamming, account fraud, social engineering
Price scrapingScraping ecommerce websites to gather pricing informationUndercutting competitors, scalping, anti-competitive practices

Malicious web scraping can have significant negative impacts on websites and businesses. It can lead to server overload, website downtime and outage, lost revenue, damaged reputation, and legal action, as in the case of Regal Health in 2023.

What Is Price Scraping?

Price scraping is a prime example of malicious web scraping, in which pricing information is harvested from a site—for instance, an ecommerce site, travel portal, or ticketing agency. This is usually done to undercut the competition and gain an unfair price advantage.

How Price Scraping Impacts Businesses

There are several ways that price scraping can harm businesses:

  1. Unscrupulous competitors deploy price scraping bots to monitor and extract real-time pricing and inventory data from the competition. This puts pressure on servers and can lead to service disruption or website outage, resulting in poor user experience, cart abandonment, and non-conversion. Crashes caused by price scraping may account for up to 13% of abandoned carts.
  2. If customers already visited your competitor’s sites, retargeting ads can offer them the same products, redirecting your customers to your competitor’s site.
  3. Competitors who scrape pricing information can lure buyers by setting their own prices lower than yours in a marketplace. They will then rank higher on price comparison websites.
  4. Competitors can use price-scraped data for scalping. Scalping is the practice of buying large quantities of a popular product—often through automated systems or bots—and reselling them at a higher price.
  5. Scraper bots can pull data from hidden but unsecured databases, like customer and email lists. If your customer list and email list are scraped, your customers can end up becoming targets of coordinated malicious attacks or direct advertising from your competitors.
  6. Scraped data can be used to create a knock-off, replica, or spoofing site with a similar name e.g., www.aliexpresss.com for www.aliexpress.com (this is called typosquatting.) The spoofing site can then be used for phishing, for example by capturing and stealing the login credentials of unsuspecting buyers who mistakenly enter the wrong URL.
  7. Spoofing sites can be used to steal credit card information from users who complete checkout. But these customers will either never get what they paid for, or instead receive a knock-off, low-quality version. This can damage seller credibility and reputation, generate negative reviews, and land your website in the Ripoff Report.

Some of the most spoofed brands include (in no particular order):

  • LinkedIn
  • DHL
  • FedEx
  • PayPal
  • Google
  1. A spoofing site impersonating your brand, armed with your pricing and product data, can field exorbitant prices and generate fake negative reviews. They can even flood the fake site with other malicious content to discredit your brand and misinform potential customers.

What Is Content Scraping?

Let’s look at another form of malicious web scraping. Content scraping is a form of web scraping where content is extracted from websites using specialized scraper tools and bots. For example, a website’s entire blog can be scraped and republished elsewhere without attribution or without using rel=canonical or noindex tags.

Examples of abusive scraping include:

  • Copying and republishing content from other sites, without adding original content or value or citing the original source
  • Copying content from other sites, slightly modifying it, and republishing it without attribution
  • Reproducing content feeds from other sites
  • Embedding or compiling content from other sites

How Content Scraping Impacts Businesses

There are several ways that content scraping can harm businesses:

  1. Your content can be copy-pasted verbatim without credit, meaning that the scraper site takes credit for your hard work.
  2. Your entire website(s) could be cloned using content scraping techniques, which can be used maliciously to spoof users for phishing.
  3. Your customers into giving away personal information like credit card details or social security numbers (SSN) via typosquatting. This method was used by convicted felon, Hushpuppi, who engaged in widespread cyber fraud and business email compromise schemes.
  4. If your website is spoofed, fake bot traffic could commit click fraud and ad fraud. This strategy can make it look like your business itself is engaged in click or ad fraud.
  5. Your SEO rankings could be damaged if content scraping makes you compete for visibility and organic traffic against your own duplicate content. If you’re outranked by duplicate content, you may lose revenue to criminals profiting from your hard work. Google does countermeasures in place, but they are not 100% guaranteed.
  6. If content scraping on your website or online assets results in a data breach, you risk facing a class action lawsuit, paying damages, and losing hard-earned customer trust and loyalty.

How to Protect Against Web Scraping

To protect your website against web scraping, you can implement a number of robust security measures. We can sort these techniques into two categories: DIY and advanced. On the DIY end, you might already be familiar with CAPTCHA, rate limiting (limiting the number of requests a user can send to your server in a given time period), and user behavior analysis to detect and block suspicious activities.

More advanced techniques include server-side techniques such as regularly changing HTML structures, hiding or encrypting certain data, and ensuring you have a strong, updated robots.txt file that clearly states what bots are allowed to do on your website.

However, two major challenges to preventing web scraping exist. Firstly, some web scraping prevention methods can also impact real users and legitimate crawlers. Secondly, scraper tools and bots are becoming more sophisticated and better at evading detection, for example, by using rotating proxies or CAPTCHA solving to cover their tracks.

DIY Protection Measures Against Web Scraping

Below is a table of DIY protective measures that you can immediately take to prevent or minimize web scraping activities, especially price scraping and content scraping.

Step numberActionDescription
1Stay updatedTrack the latest web scraping techniques by following blogs (like ScraperAPI or Octoparse) that teach them
2Search for your own contentSearch for phrases, sentences, or paragraphs in your post enclosed in quotes
3Use plagiarism checkersCopyscape lets you search for copies of your web pages by URL or by copy-pasting text
4Check for typosquattingRegularly check for misspellings of your domain name to prevent content theft and typo hijacking
5Implement CAPTCHA (but don’t include the solution in the HTML markup)CAPTCHA differentiates humans from bots using puzzles bots can’t ordinarily solve. Google’s reCAPTCHA is a good option.
6Set up notifications for pingbacks on WordPress sitesPingback notifications alert you to use of your published backlinks and allow you to manually approve which of those sites can link to yours. This helps to prevent link spam and low-quality backlinks.
7Set up Google AlertsGet notified whenever phrases or terms that you’re using often get mentioned anywhere on the web.
8Gate your contentPut content behind a paywall or form, requiring sign-in to gain access. Confirm new account sign-ups by email.
9Monitor unusual activityAn excessive number of requests, page views, or searches from one IP address might indicate bot activity. Monitor this via network requests to your site or using integrated web analytics tools like Google Analytics.
10Implement rate limitingAllow users and verified scrapers a limited number of actions per time. This limits network traffic.
11Block scraping servicesBlock access from IP addresses of known scraping services, but mask the real reason for the block.
13Create a honeypotHoneypots are virtual traps or decoys set up to distract or fool malicious bots and learn how they work.
14Update your website/APIDynamic websites and updated HTML/APIs make it harder for malicious bots to scrape content.
15Disallow web scrapingEnact via your robots.txt file (e.g., www.yourURL.com/robots.txt), terms of service, or a legal warning.
16Contact, then report offendersReach out to the content thief letting them know they’re in violation of your terms of service. You can also file a DMCA takedown request.

While these DIY measures can help, their impact is limited in the face of ever-evolving threats like web scraping. Advanced, enterprise-grade web scraping protections are more effective, ensuring the security, integrity, and competitive edge that your site offers customers.

Advanced Protection Measures Against Web Scraping

Advanced web scraping solutions like WAF and bot protection provide enterprise-grade web scraping protection. They help to further protect your assets against unethical web scraping and can be used in conjunction with bot management best practices and other DIY anti-scraping measures.

  1. Web application firewall (WAF): A comprehensive WAF protects your web applications and APIs against OWASP Top 10 and zero-day attacks. A web application firewall acts as an intermediary, detecting and scanning malicious requests before web applications and servers accept them and respond to them. This helps to protect your web servers and users.

As a Layer 7 defense, Gcore’s WAF employs real-time monitoring and advanced machine-learning techniques to secure your web applications and APIs against cyber threats such as credentials theft, unauthorized access, data leaks, and web scraping.

Figure 1: Gcore web application firewall
  1. Bot protection: Effective bot protection prevents server overload resulting from aggressive bot traffic/activity. A bot protection service uses a set of algorithms to isolate and remove unwanted bot traffic that has already infiltrated your perimeter. This is essential for preventing attacks like web scraping, account takeover, and API data scraping.

Gcore’s comprehensive bot protection service offers clients best-in-class L3/L4/L7 protection across their networks, transports, and application layers. Users can also choose between low-level or high-level bot protection. Low-level bot protection uses quantitative analytics to detect and block suspicious sessions while high-level bot protection utilizes a rate limiter and additional checks to safeguard your servers.

Bot protection is highly effective against web scraping, account takeover, form submission abuse, API data scraping, and TLS session attacks. It helps you to maintain uninterrupted service even during intense attacks, allowing you to focus on running your business while mitigating the threats. Bot protection is customizable, quick to deploy, and cost effective.

Conclusion

Web scraping protection is essential for all businesses because it ensures the confidentiality, integrity, and availability of your business and customer data. Unethical web scraping poses a serious threat to this ideal by using malicious scraper tools and bots to access and extract data without permission.

Gcore’s advanced WAF and bot protection solutions offer advanced protection against web scraping. Try our advanced web scraping protection services for free today and protect your web resources and customers from malicious web scraping activities of any size and complexity.

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This unified approach typically reduces security management overhead by 40-60% compared to multi-vendor solutions, while maintaining the continuous monitoring capabilities.Explore how Gcore's integrated cloud security infrastructure can strengthen your defense plan at gcore.com/cloud.Frequently asked questionsWhat's the difference between cloud security and traditional approaches?Cloud security differs from traditional approaches by protecting distributed resources through shared responsibility models and cloud-native tools, while traditional security relies on perimeter-based defenses around centralized infrastructure. Traditional security assumes a clear network boundary with firewalls and intrusion detection systems protecting internal resources. In contrast, cloud security secures individual workloads, data, and identities across multiple environments without relying on network perimeters.What is cloud security posture management?Cloud security posture management (CSPM) is a set of tools and processes that continuously monitor cloud environments to identify misconfigurations, compliance violations, and security risks across cloud infrastructure. CSPM platforms automatically scan cloud resources, assess security policies, and provide remediation guidance to maintain proper security configurations.How does Zero Trust apply to cloud security?Zero Trust applies to cloud security by treating every user, device, and connection as untrusted and requiring verification before granting access to cloud resources. This approach replaces traditional perimeter-based security with continuous authentication, micro-segmentation, and least-privilege access controls across cloud environments.What compliance standards apply?Cloud security must comply with industry-specific regulations like SOC 2, ISO 27001, GDPR, HIPAA, PCI DSS, and FedRAMP, depending on your business sector and geographic location. Organizations typically need to meet multiple standards simultaneously, with financial services requiring PCI DSS compliance, healthcare needing HIPAA certification, and EU operations mandating GDPR adherence.What happens during a cloud security breach?During a cloud security breach, attackers gain unauthorized access to cloud resources, potentially exposing sensitive data, disrupting services, and causing financial damage averaging $5 million per incident, according to IBM. 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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