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:
- A person or bad actor deploys a scraper tool on a target website, or installs a bot.
- The scraper tool or bot sends automated requests to the website’s server requesting page-specific HTML code.
- The server responds with the HTML code as requested.
- The scraper tool or bot parses the supplied HTML code and extracts data—including databases—according to user-specific parameters.
- 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.
| Criteria | Scraper Tool | Bot |
| Purpose | Automated web scraping | Autonomous task automation for web scraping or other purposes |
| User Interface | User interface (UI), command line | No UI, standalone script |
| Technical skills | Some programming and web scraping know-how (no-code options available) | Advanced programming and web scraping know-how |
| Programming language | Python, Ruby, Node.js, Golang, PHP, and Perl | Python, Java, C++, Lisp, Clojure, and PHP |
| Good or bad | Depends on intent and approach | Good bots and bad bots both exist |
| Examples | BeautifulSoup, Scrapy | Googlebot, BingBot, Botnet |
| Benign use case | Weather forecast, price recommendation, job listings | Search engine indexing, ChatGPT, Siri/Alexa |
| Malicious use case | Web content scraping, price scraping | Spamming, 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.
| Criteria | Consideration | Benign web scraping | Malicious web scraping |
| Authorization | Was approval granted before web scraping? | Yes | No |
| Intent | What was the original purpose for this web scraping? | Good | Bad |
| Approach | How was the web scraping carried out? | Ethically, harmless | Unethically, harmful |
| Impact | What was the impact of the web scraping approach on the scraped server or site? | None/slight | Severe |
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.
| Type | Activity | Intent |
| Social media user profile scraping | Scraping social media platforms to extract user profiles or personal information | Targeted advertising, identity profiling, identity theft |
| Healthcare data extraction | Scraping healthcare provider websites to access patient records, SSN, and medical information | Identity theft, blackmail, credit card fraud |
| API scraping | Scraping web or mobile app APIs | Reverse engineering or maliciously cloning apps |
| Email/contact scraping | Scraping email addresses and contact information from web pages | Spamming, phishing/smishing, malware distribution |
| Reviews/rating manipulation | Scraping reviews and rating sites or services | Posting fake positive reviews for self or fake negative reviews against competitors |
| Personal data harvesting | Scraping personal information like SSN, date of birth, and credit card details | Identity theft, impersonation, credit card fraud |
| Ad fraud scraping | Scraping advertising networks and platforms looking for ad placements | False ad impressions, click fraud |
| Protected content scraping | Scraping protected or gated content | Targeting log-in credentials and credit card information |
| Web scraping for malware distribution | Scraping content to create spoofing/phishing sites | Distributing malware disguised as software downloads |
| Automated account creation | Creating fake user accounts using web scraping techniques and credential stuffing | Spamming, account fraud, social engineering |
| Price scraping | Scraping ecommerce websites to gather pricing information | Undercutting 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:
- 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.
- If customers already visited your competitor’s sites, retargeting ads can offer them the same products, redirecting your customers to your competitor’s site.
- 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.
- 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.
- 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.
- 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.
- 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):
- DHL
- FedEx
- PayPal
- 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:
- Your content can be copy-pasted verbatim without credit, meaning that the scraper site takes credit for your hard work.
- Your entire website(s) could be cloned using content scraping techniques, which can be used maliciously to spoof users for phishing.
- 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.
- 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.
- 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.
- 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 number | Action | Description |
| 1 | Stay updated | Track the latest web scraping techniques by following blogs (like ScraperAPI or Octoparse) that teach them |
| 2 | Search for your own content | Search for phrases, sentences, or paragraphs in your post enclosed in quotes |
| 3 | Use plagiarism checkers | Copyscape lets you search for copies of your web pages by URL or by copy-pasting text |
| 4 | Check for typosquatting | Regularly check for misspellings of your domain name to prevent content theft and typo hijacking |
| 5 | Implement 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. |
| 6 | Set up notifications for pingbacks on WordPress sites | Pingback 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. |
| 7 | Set up Google Alerts | Get notified whenever phrases or terms that you’re using often get mentioned anywhere on the web. |
| 8 | Gate your content | Put content behind a paywall or form, requiring sign-in to gain access. Confirm new account sign-ups by email. |
| 9 | Monitor unusual activity | An 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. |
| 10 | Implement rate limiting | Allow users and verified scrapers a limited number of actions per time. This limits network traffic. |
| 11 | Block scraping services | Block access from IP addresses of known scraping services, but mask the real reason for the block. |
| 13 | Create a honeypot | Honeypots are virtual traps or decoys set up to distract or fool malicious bots and learn how they work. |
| 14 | Update your website/API | Dynamic websites and updated HTML/APIs make it harder for malicious bots to scrape content. |
| 15 | Disallow web scraping | Enact via your robots.txt file (e.g., www.yourURL.com/robots.txt), terms of service, or a legal warning. |
| 16 | Contact, then report offenders | Reach 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.
- 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.
- 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.
Related articles
3 underestimated security risks of AI workloads and how to overcome them
Artificial intelligence workloads introduce a fundamentally different security landscape for engineering and security teams. Unlike traditional applications, AI systems must protect not just endpoints and networks, but also training data pipelines, feature stores, model repositories, and inference APIs. Each phase of the AI life cycle presents distinct attack vectors that adversaries can exploit to corrupt model behavior, extract proprietary logic, or manipulate downstream outputs.In this article, we uncover three security vulnerabilities of AI workloads and explain how developers and MLOps teams can overcome them. We also look at how investing in your AI security can save time and money, explore the challenges that lie ahead for AI security, and offer a simplified way to protect your AI workloads with Gcore.Risk #1: data poisoningData poisoning is a targeted attack on the integrity of AI systems, where malicious actors subtly inject corrupted or manipulated data into training pipelines. The result is a model that behaves unpredictably, generates biased or false outputs, or embeds hidden logic that can be triggered post-deployment. This can undermine business-critical applications—from fraud detection and medical diagnostics to content moderation and autonomous decision-making.For developers, the stakes are high: poisoned models are hard to detect once deployed, and even small perturbations in training data can have system-wide consequences. Luckily, you can take a few steps to mitigate against data poisoning and then implement zero-trust AI to further protect your workloads.Mitigation and hardeningRestrict dataset access using IAM, RBAC, or identity-aware proxies.Store all datasets in versioned, signed, and hashed formats.Validate datasets with automated schema checks, label distribution scans, and statistical outlier detection before training.Track data provenance with metadata logs and checksums.Block training runs if datasets fail predefined data quality gates.Integrate data validation scripts into CI/CD pipelines pre-training.Enforce zero-trust access policies for data ingestion services.Solution integration: zero-trust AIImplement continuous authentication and authorization for each component interacting with data (e.g., preprocessing scripts, training jobs).Enable real-time threat detection during training using runtime security tools.Automate incident response triggers for unexpected file access or data source changes.Risk #2: adversarial attacksAdversarial attacks manipulate model inputs in subtle ways that trick AI systems into making incorrect or dangerous decisions. These perturbations—often imperceptible to humans—can cause models to misclassify images, misinterpret speech, or misread sensor data. In high-stakes environments like facial recognition, autonomous vehicles, or fraud detection, these failures can result in security breaches, legal liabilities, or physical harm.For developers, the threat is real: even state-of-the-art models can be easily fooled without adversarial hardening. The good news? You can make your models more robust by combining defensive training techniques, input sanitization, and secure API practices. While encrypted inference doesn’t directly block adversarial manipulation, it ensures that sensitive inference data stays protected even if attackers attempt to probe the system.Mitigation and hardeningUse adversarial training frameworks like CleverHans or IBM ART to expose models to perturbed inputs during training.Apply input sanitization layers (e.g., JPEG re-encoding, blurring, or noise filters) before data reaches the model.Implement rate limiting and authentication on inference APIs to block automated adversarial probing.Use model ensembles or randomized smoothing to improve resilience to small input perturbations.Log and analyze input-output patterns to detect high-variance or abnormal responses.Test models regularly against known attack vectors using robustness evaluation tools.Solution integration: encrypted inferenceWhile encryption doesn't prevent adversarial inputs, it does mean that input data and model responses remain confidential and protected from observation or tampering during inference.Run inference in trusted environments like Intel SGX or AWS Nitro Enclaves to protect model and data integrity.Use homomorphic encryption or SMPC to process encrypted data without exposing sensitive input.Ensure that all intermediate and output data is encrypted at rest and in transit.Deploy access policies that restrict inference to verified users and approved applications.Risk #3: model leakage of intellectual assetsModel leakage—or model extraction—happens when an attacker interacts with a deployed model in ways that allow them to reverse-engineer its structure, logic, or parameters. Once leaked, a model can be cloned, monetized, or used to bypass the very defenses it was meant to enforce. For businesses, this means losing competitive IP, compromising user privacy, or enabling downstream attacks.For developers and MLOps teams, the challenge is securing deployed models in a way that balances performance and privacy. If you're exposing inference APIs, you’re exposing potential entry points—but with the right controls and architecture, you can drastically reduce the risk of model theft.Mitigation and hardeningEnforce rate limits and usage quotas on all inference endpoints.Monitor for suspicious or repeated queries that indicate model extraction attempts.Implement model watermarking or fingerprinting to trace unauthorized model use.Obfuscate models before deployment using quantization, pruning, or graph rewriting.Disable or tightly control any model export functionality in your platform.Sign and verify inference requests and responses to ensure authenticity.Integrate security checks into CI/CD pipelines to detect risky configurations—such as public model endpoints, export-enabled containers, or missing inference authentication—before they reach production.Solution integration: native security integrationIntegrate model validation, packaging, and signing into CI/CD pipelines.Serve models from encrypted containers or TEEs, with minimal runtime exposure.Use container and image scanning tools to catch misconfigurations before deployment.Centralize monitoring and protection with tools like Gcore WAAP for real-time anomaly detection and automated response.How investing in AI security can save your business moneyFrom a financial point of view, the use of AI and machine learning in cybersecurity can lead to massive cost savings. Organizations that utilize AI and automation in cybersecurity have saved an average of $2.22 million per data breach compared to organizations that do not have these protections in place. This is because the necessity for manual oversight is reduced, lowering the total cost of ownership, and averting costly security breaches. The initial investment in advanced security technologies yields returns through decreased downtime, fewer false positives, and an enhanced overall security posture.Challenges aheadWhile securing the AI lifecycle is essential, it’s still difficult to balance robust security with a positive user experience. Rigid scrutiny can add additional latency or false positives that can stop operations, but AI-powered security can avoid such incidents.Another concern organizations must contend with is how to maintain current AI models. With threats changing so rapidly, today's newest model could easily become outdated by tomorrow’s. Solutions must have an ongoing learning ability so that security detection parameters can be revised.Operational maturity is also a concern, especially for companies that operate in multiple geographies. Well-thought-out strategies and sound governance processes must accompany the integration of complex AI/ML tools with existing infrastructure, but automation still offers the most benefits by reducing the overhead on security teams and helping ensure consistent deployment of security policies.Get ahead of AI security with GcoreAI workloads introduce new and often overlooked security risks that can compromise data integrity, model behavior, and intellectual property. By implementing practices like zero-trust architecture, encrypted inference, and native security integration, developers can build more resilient and trustworthy AI systems. As threats evolve, staying ahead means embedding security at every phase of the AI lifecycle.Gcore helps teams apply these principles at scale, offering native support for zero-trust AI, encrypted inference, and intelligent API protection. As an experienced AI and security solutions provider, our DDoS Protection and AI-enabled WAAP solutions integrate natively with Everywhere Inference and GPU Cloud across 210+ global points of presence. That means low latency, high performance, and proven, robust security, no matter where your customers are located.Talk with our AI security experts and secure your workloads today
Flexible DDoS mitigation with BGP Flowspec
For customers who understand their own network traffic patterns, rigid DDoS protection can be more of a limitation than a safeguard. That’s why Gcore supports BGP Flowspec: a flexible, standards-based method for defining granular filters that block or rate-limit malicious traffic in real time…before it reaches your infrastructure.In this article, we’ll walk through:What Flowspec is and how it worksThe specific filters and actions Gcore supportsCommon use cases, with example rule definitionsHow to activate and monitor Flowspec in your environmentWhat is the BGP Flowspec?BGP Flowspec (RFC 8955) extends Border Gateway Protocol to distribute traffic filtering rules alongside routing updates. Instead of static ACLs or reactive blackholing, Flowspec enables near-instantaneous propagation of mitigation rules across networks.BGP tells routers how to reach IP prefixes across the internet. With Flowspec, those same BGP announcements can now carry rules, not just routes. Each rule describes a pattern of traffic (e.g., TCP SYN packets >1000 bytes from a specific subnet) and what action to take (drop, rate-limit, mark, or redirect).What are the benefits of the BGP Flowspec?Most traditional DDoS protection services react to threats after they start, whether by blackholing traffic to a target IP, redirecting flows to a scrubbing center, or applying rigid, static filters. These approaches can block legitimate traffic, introduce latency, or be too slow to respond to fast-evolving attacks.Flowspec offers a more flexible alternative.Proactive mitigation: Instead of waiting for attacks, you can define known-bad traffic patterns ahead of time and block them instantly. Flowspec lets experienced operators prevent incidents before they start.Granular filtering: You’re not limited to blocking by IP or port. With Flowspec, you can match on packet size, TCP flags, ICMP codes, and more, enabling fine-tuned control that traditional ACLs or RTBH don’t support.Edge offloading: Filtering happens directly on Gcore’s routers, offloading your infrastructure and avoiding scrubbing latency.Real-time updates: Changes to rules are distributed across the network via BGP and take effect immediately, faster than manual intervention or standard blackholing.You still have the option to block traffic during an active attack, but with Flowspec, you gain the flexibility to protect services with minimal disruption and greater precision than conventional tools allow.Which parts of the Flowspec does Gcore implement?Gcore supports twelve filter types and four actions of the Flowspec.Supported filter typesGcore supports all 12 standard Flowspec match components.Filter FieldDescriptionDestination prefixTarget subnet (usually your service or app)Source prefixSource of traffic (e.g., attacker IP range)IP protocolTCP, UDP, ICMP, etc.Port / Source portMatch specific client or server portsDestination portMatch destination-side service portsICMP type/codeFilter echo requests, errors, etc.TCP flagsFilter packets by SYN, ACK, RST, FIN, combinationsPacket lengthFilter based on payload sizeDSCPQuality of service code pointFragmentMatch on packet fragmentation characteristicsSupported actionsGcore DDoS Protection supports the following Flowspec actions, which can be triggered when traffic matches a specific filter:ActionDescriptionTraffic-rate (0x8006)Throttle/rate limit traffic by byte-per-second rateredirectRedirect traffic to alternate location (e.g., scrubbing)traffic-markingApply DSCP marks for downstream classificationno-action (drop)Drop packets (rate-limit 0)Rule orderingRFC 5575 defines the implicit order of Flowspec rules. The crucial point is that more specific announcements take preference, not the order in which the rules are propagated.Gcore also respects Flowspec rule ordering per RFC 5575. More specific filters override broader ones. Future support for Flowspec v2 (with explicit ordering) is under consideration, pending vendor adoption.Blackholing and extended blackholing (eBH)Remote-triggered blackhole (RTBH) is a standardized protection method that the client manages via BGP by analyzing traffic, identifying the direction of the attack (i.e., the destination IP address). This method protects against volumetric attacks.Customers using Gcore IP Transit can trigger immediate blackholing for attacked prefixes via BGP, using the well-known blackhole community tag 65000:666. All traffic to that destination IP is dropped at Gcore’s edge.The list of supported BGP communities is available here.BGP extended blackholeExtended blackhole (eBH) allows for more granular blackholing that does not affect legitimate traffic. For customers unable to implement Flowspec directly, Gcore supports eBH. You announce target prefixes with pre-agreed BGP communities, and Gcore translates them into Flowspec mitigations.To configure this option, contact our NOC at noc@gcore.lu.Monitoring and limitationsGcore can support several logging transports, including mail and Slack.If the number of Flowspec prefixes exceeds the configured limit, Gcore DDoS Protection stops accepting new announcements, but BGP sessions and existing prefixes will stay active. Gcore will receive a notification that you reached the limit.How to activateActivation takes just two steps:Define rules on your edge router using Flowspec NLRI formatAnnounce rules via BGP to Gcore’s intermediate control planeThen, Gcore validates and propagates the filters to border routers. Filters are installed on edge devices and take effect immediately.If attack patterns are unknown, you’ll first need to detect anomalies using your existing monitoring stack, then define the appropriate Flowspec rules.Need help activating Flowspec? Get in touch via our 24/7 support channels and our experts will be glad to assist.Set up GRE and benefit from Flowspec today
Securing AI from the ground up: defense across the lifecycle
As more AI workloads shift to the edge for lower latency and localized processing, the attack surface expands. Defending a data center is old news. Now, you’re securing distributed training pipelines, mobile inference APIs, and storage environments that may operate independently of centralized infrastructure, especially in edge or federated learning contexts. Every stage introduces unique risks. Each one needs its own defenses.Let’s walk through the key security challenges across each phase of the AI lifecycle, and the hardening strategies that actually work.PhaseTop threatsHardening stepsTrainingData poisoning, leaksValidation, dataset integrity tracking, RBAC, adversarial trainingDevelopmentModel extraction, inversionRate limits, obfuscation, watermarking, penetration testingInferenceAdversarial inputs, spoofed accessInput filtering, endpoint auth, encryption, TEEsStorage and deploymentModel theft, tamperingEncrypted containers, signed builds, MFA, anomaly monitoringTraining: your model is only as good as its dataThe training phase sets the foundation. If the data going in is poisoned, biased, or tampered with, the model will learn all the wrong lessons and carry those flaws into production.Why it mattersData poisoning is subtle. You won’t see a red flag during training logs or a catastrophic failure at launch. These attacks don’t break training, they bend it.A poisoned model may appear functional, but behaves unpredictably, embeds logic triggers, or amplifies harmful bias. The impact is serious later in the AI workflow: compromised outputs, unexpected behavior, or regulatory non-compliance…not due to drift, but due to training-time manipulation.How to protect itValidate datasets with schema checks, label audits, and outlier detection.Version, sign, and hash all training data to verify integrity and trace changes.Apply RBAC and identity-aware proxies (like OPA or SPIFFE) to limit who can alter or inject data.Use adversarial training to improve model robustness against manipulated inputs.Development and testing: guard the logicOnce you’ve got a trained model, the next challenge is protecting the logic itself: what it knows and how it works. The goal here is to make attacks economically unfeasible.Why it mattersModels encode proprietary logic. When exposed via poorly secured APIs or unprotected inference endpoints, they’re vulnerable to:Model inversion: Extracting training dataExtraction: Reconstructing logicMembership inference: Revealing whether a datapoint was in trainingHow to protect itApply rate limits, logging, and anomaly detection to monitor usage patterns.Disable model export by default. Only enable with approval and logging.Use quantization, pruning, or graph obfuscation to reduce extractability.Explore output fingerprinting or watermarking to trace unauthorized use in high-value inference scenarios.Run white-box and black-box adversarial evaluations during testing.Integrate these security checks into your CI/CD pipeline as part of your MLOps workflow.Inference: real-time, real riskInference doesn’t get a free pass because it’s fast. Security needs to be just as real-time as the insights your AI delivers.Why it mattersAdversarial attacks exploit the way models generalize. A single pixel change or word swap can flip the classification.When inference powers fraud detection or autonomous systems, a small change can have a big impact.How to protect itSanitize input using JPEG compression, denoising, or frequency filtering.Train on adversarial examples to improve robustness.Enforce authentication and access control for all inference APIs—no open ports.Encrypt inference traffic with TLS. For added privacy, use trusted execution environments (TEEs).For highly sensitive cases, consider homomorphic encryption or SMPC—strong but compute-intensive solutions.Check out our free white paper on inference optimization.Storage and deployment: don’t let your model leakOnce your model’s trained and tested, you’ve still got to deploy and store it securely—often across multiple locations.Why it mattersUnsecured storage is a goldmine for attackers. With access to the model binary, they can reverse-engineer, clone, or rehost your IP.How to protect itStore models on encrypted volumes or within enclaves.Sign and verify builds before deployment.Enforce MFA, RBAC, and immutable logging on deployment pipelines.Monitor for anomalous access patterns—rate, volume, or source-based.Edge strategy: security that moves with your AIAs AI moves to the edge, centralized security breaks down. You need protection that operates as close to the data as your inference does.That’s why we at Gcore integrate protection into AI workflows from start to finish:WAAP and DDoS mitigation at edge nodes—not just centralized DCs.Encrypted transport (TLS 1.3) and in-node processing reduce exposure.Inline detection of API abuse and L7 attacks with auto-mitigation.180+ global PoPs to maintain consistency across regions.AI security is lifecycle securityNo single firewall, model tweak, or security plugin can secure AI workloads in isolation. You need defense in depth: layered, lifecycle-wide protections that work at the data layer, the API surface, and the edge.Ready to secure your AI stack from data to edge inference?Talk to our AI security experts
3 ways to safeguard your website against DDoS attacks—and why it matters
DDoS (distributed denial-of-service) attacks are a type of cyberattack in which a hacker overwhelms a server with an excessive number of requests, causing the server to stop functioning correctly and denying access to legitimate users. The volume of these types of attacks is increasing, with a 56% year-on-year rise recorded in late 2024, driven by factors including the growing availability of AI-powered tools, poorly secured IoT devices, and geopolitical tensions worldwide.Fortunately, there are effective ways to defend against DDoS attacks. Because these threats can target different layers of your network, a single tool isn’t enough, and a multi-layered approach is necessary. Businesses need to protect both the website itself and the infrastructure behind it. This article explores the three key security solutions that work together to protect your website—and the costly consequences of failing to prepare.The consequences of not protecting your website against DDoS attacksIf your website isn’t sufficiently protected, DDoS attacks can have severe and far-reaching impacts on your website, business, and reputation. They not only disrupt the user experience but can spiral into complex, costly recovery efforts. Safeguarding your website against DDoS attacks is essential to preventing the following serious outcomes:Downtime: DDoS attacks can exhaust server resources (CPU, RAM, throughput), taking websites offline and making them unavailable to end users.Loss of business/customers: Frustrated users will leave, and many won’t return after failed checkouts or broken sessions.Financial losses: By obstructing online sales, DDoS attacks can cause businesses to suffer substantial loss of revenue.Reputational damage: Websites or businesses that suffer repeated unmitigated DDoS attacks may cause customers to lose trust in them.Loss of SEO rankings: A website could lose its hard-won SEO ranking if it experiences extended downtime due to DDoS attacks.Disaster recovery costs: DDoS disaster recovery costs can escalate quickly, encompassing hardware replacement, software upgrades, and the need to hire external specialists.Solution #1: Implement dedicated DDoS protection to safeguard your infrastructureAdvanced DDoS protection measures are customized solutions designed to protect your servers and infrastructure against DDoS attacks. DDoS protection helps defend against malicious traffic designed to crash servers and interrupt service.Solutions like Gcore DDoS Protection continuously monitor incoming traffic for suspicious patterns, allowing them to automatically detect and mitigate attacks in real time. If your resources are attacked, the system filters out harmful traffic before it reaches your servers. This means that real users can access your website without interruption, even during an attack.For example, a financial services provider could be targeted by cybercriminals attempting to disrupt services with a large-scale volumetric DDoS attack. With dedicated DDoS protection, the provider can automatically detect and filter out malicious traffic before it impacts users. Customers can continue to log in, check balances, and complete transactions, while the system adapts to the evolving nature of the attack in the background, maintaining uninterrupted service.The protection scales with your business needs, automatically adapting to higher traffic loads or more complex attacks. Up-to-date reports and round-the-clock technical support allow you to keep track of your website status at all times.Solution #2: Enable WAAP to protect your websiteGcore WAAP (web application and API protection) is a comprehensive solution that monitors, detects, and mitigates cyber threats, including DDoS layer 7 attacks. WAAP uses AI-driven algorithms to monitor, detect, and mitigate threats in real time, offering an additional layer of defense against sophisticated attackers. Once set up, the system provides powerful tools to create custom rules and set specific triggers. For example, you can specify the conditions under which certain requests should be blocked, such as sudden spikes in API calls or specific malicious patterns common in DDoS attacks.For instance, an e-commerce platform during a major sale like Black Friday could be targeted by bots attempting to flood the site with fake login or checkout requests. WAAP can differentiate between genuine users and malicious bots by analyzing traffic patterns, rate of requests, and attack behaviors. It blocks malicious requests so that real customers can continue to complete transactions without disruption.Solution #3: Connect to a CDN to strengthen defenses furtherA trustworthy content delivery network (CDN) is another valuable addition to your security stack. A CDN is a globally distributed server network that ensures efficient content delivery. CDNs spread traffic across multiple global edge servers, reducing the load on the origin server. During a DDoS attack, a CDN with DDoS protection can protect servers and end users. It filters traffic at the edge, blocking threats before they ever reach your infrastructure. Caching servers within the CDN network then deliver the requested content to legitimate users, preventing network congestion and denial of service to end users.For instance, a gaming company launching a highly anticipated multiplayer title could face a massive surge in traffic as players around the world attempt to download and access the game simultaneously. This critical moment also makes the platform a prime target for DDoS attacks aimed at disrupting the launch. A CDN with integrated DDoS protection can absorb and filter out malicious traffic at the edge before it reaches the core infrastructure. Legitimate players continue to enjoy fast downloads and seamless gameplay, while the origin servers remain stable and protected from overload or downtime.In addition, Super Transit intelligently routes your traffic via Gcore’s 180+ point-of-presence global network, proactively detecting, mitigating, and filtering DDoS attacks. Even mid-attack, users experience seamless access with no interruptions. They also benefit from an enhanced end-user experience, thanks to shorter routes between users and servers that reduce latency.Taking the next steps to protect your websiteDDoS attacks pose significant threats to websites, but a proactive approach is the best way to keep your site online, secure, and resilient. Regardless of your industry or location, it’s crucial to take action to safeguard your website and maintain its uninterrupted availability.Enabling Gcore DDoS protection is a simple and proven way to boost your digital infrastructure’s resiliency against different types of DDoS attacks. Gcore DDoS protection also integrates with other security solutions, including Gcore WAAP, which protects your website and CDNs. These tools work seamlessly together to provide advanced website protection, offering improved security and performance in one intuitive platform.If you’re ready to try Gcore Edge Security, fill in the form below and one of our security experts will be in touch for a personalized consultation.
From reactive to proactive: how AI is transforming WAF cybersecurity solutions
While digital transformation in recent years has driven great innovation, cyber threats have changed in parallel, evolving to target the very applications businesses rely on to thrive. Traditional web application security measures, foundational as they may be, are no longer effective in combating sophisticated attacks in time. Enter the next generation of WAFs (web application firewalls) powered by artificial intelligence.Next-generation WAFs, often incorporated into WAAP solutions, do much more than respond to threats; instead, they will use AI and ML-powered techniques to predict and neutralize threats in real time. This helps businesses to stay ahead of bad actors by securing applications, keeping valuable data safe, and protecting hard-earned brand reputations against ever-present dangers in an expanding digital world.From static to AI-powered web application firewallsTraditional WAFs were relied on to protect web applications against known threats, such as SQL injection and cross-site scripting. They’ve done a great job as the first line of defense, but their reliance on static rules and signature-based detection means they struggle to keep up with today’s fast-evolving cyber threats. To understand in depth why traditional WAFs are no longer sufficient in today’s threat landscape, read our ebook.AI and ML have already revolutionized what a WAF can do. AI/ML-driven WAFs can examine vast streams of traffic data and detect patterns, including new threats, right at the emergence stage. The real-time adaptability that this allows is effective even against zero-day attacks and complex new hacking techniques.How AI-powered WAP proactively stops threatsOne of the most significant advantages of AI/ML-powered WAFs is proactive identification and prevention capabilities. Here's how this works:Traffic pattern analysis: AI systems monitor both incoming and outgoing traffic to set up baselines for normal behavior. This can then allow for the detection of anomalies that could show a zero-day attack or malicious activity.Real-time decision making: Machine learning models keep learning from live traffic and detect suspicious activities on the go sans waiting for any updates in the rule set. This proactive approach ensures that businesses are guarded from emerging threats before they escalate.Heuristic tagging and behavioral insights: Advanced heuristics used by AI-driven systems tag everything from sessionless clients to unusual request frequencies. It helps administrators classify potential bots or automated attacks much faster.Ability to counter zero-day attacks: Traditional WAF solutions can only mitigate attacks that are already in the process of accessing sensitive areas. AI/ML-powered WAFs, on the other hand, can use data to identify and detect patterns indicative of future attacks, stopping attackers in their tracks and preventing future damage.Intelligent policy management: Adaptive WAFs detect suspicious activity and alert users to misconfigured security policies accordingly. They reduce the need for manual configuration while assuring better protection.Integrated defense layers: One of the strongest features of AI/ML-powered systems is the ease with which they integrate other layers of security, including bot protection and DDoS mitigation, into a connected architecture that protects several attack surfaces.User experience and operational impactAI-driven WAFs improve the day-to-day operations of security teams by transforming how they approach threat management. With intuitive dashboards and clearly presented analytics, as offered by Gcore WAAP, these tools empower security professionals to quickly interpret complex data, streamline decision-making, and respond proactively to threats.Instead of manually analyzing vast amounts of traffic data, teams now receive immediate alerts highlighting critical security events, such as abnormal IP behaviors or unusual session activity. Each alert includes actionable recommendations, enabling rapid adjustments to security policies without guesswork or delay.By automating the identification of sophisticated threats such as credential stuffing, scraping, and DDoS attacks, AI-powered solutions significantly reduce manual workloads. Advanced behavioral profiling and heuristic tagging pinpoint genuine threats with high accuracy, allowing security teams to concentrate their efforts where they're most needed.Embracing intelligent security with Gcore’s AI-driven WAAPOur AI-powered WAAP solution provides intelligent, interrelated protection to empower companies to actively outperform even the most sophisticated, ever-changing threats by applying advanced traffic analysis, heuristic tagging, and adaptive learning. With its cross-domain functionality and actionable security insights, this solution stands out as an invaluable tool for both security architects and strategic decision-makers. It combines innovation and practicality to address the needs of modern businesses.Curious to learn more about WAAP? Check out our ebook for cybersecurity best practices, the most common threats to look out for, and how WAAP can safeguard your businesses’ digital assets. Or, get in touch with our team to learn more about Gcore WAAP.Learn why WAAP is essential for modern businesses with a free ebook
How AI helps prevent API attacks
APIs have become an integral part of modern digital infrastructure, and it can be easy to take their security for granted. But, unfortunately, APIs are a popular target for attackers. Hackers can use APIs to access crucial data and services, and breaching APIs allows attackers to bypass traditional security controls.Most companies focus on speed of development and deployment ahead of security when crafting APIs, making them vulnerable to issues like insecure authentication, poor validation, or misconfigured endpoints, which attackers can abuse. Additionally, the interconnected nature of APIs creates multiple endpoints, widening the attack surface and creating additional points of entry that attackers can exploit.As threats evolve and the attack surface grows to include more API endpoints, integrating AI threat detection and mitigation is an absolute must for businesses to take serious, deliberate action against API cyberattacks. Let’s find out why.Staying ahead of zero-day API attacksOf all the cyber attacks that commonly threaten APIs, zero-day attacks, leveraging unknown vulnerabilities, are probably the toughest to defeat. Traditional solutions rely more on the existence of preconfigured rules or signatures along with human interference to detect and block such attacks. This approach often fails against novel threats and can block legitimate traffic, leaving applications vulnerable and making APIs inaccessible to users.APIs must balance between allowing legitimate users access and maintaining security. AI and ML technologies excel at identifying zero-day attacks based on pattern and behavior analysis rather than known signatures. For instance, heuristic algorithms can detect anomalies, such as sudden spikes in unusual traffic or behaviors indicative of malicious intent.Consider the following example: A certain IP address makes an abnormally large number of requests to a rarely accessed endpoint. Even without prior knowledge of the IP or attack vector, an AI/ML-enhanced solution can flag the activity as suspicious and block it proactively. Using minimal indicators, such as frequency patterns or traffic anomalies, AI can stop attackers before they fully exploit vulnerabilities. Additionally, this means that only suspicious IPs are blocked, and legitimate users can continue to access APIs unimpeded.The risks of shadow APIsOne of the biggest risks is shadow APIs, which are endpoints that exist but aren't documented or monitored. These can arise from configuration mistakes, forgotten updates, or even rogue development practices. These unknown APIs are the ideal target for Layer 7 attacks, as they are often left undefended, making them easy targets.AI-powered API discovery tools map both known and unknown API endpoints, enabling the grouping and management of these endpoints so sensitive APIs can be properly secured. This level of visibility is critical to securing systems against API-targeting attacks; without it, businesses are left in the dark.API discovery as a critical security practiceWAAP with AI/ML capabilities excels in API security because it accurately checks and analyzes API traffic. The Gcore API discovery engine offers 97 to 99 percent accuracy, mapping APIs in users’ domains and using data to recommend policies to help secure APIs.How heuristics enhance WAAP AI capabilities to protect APIsWhile AI and ML form the backbone of modern WAAPs, heuristic methods complement them in enhancing detection accuracy. Heuristics allow the system to inspect granular behaviors, such as mouse clicks or scrolling patterns, which distinguish legitimate users from bots.For example, most scraping attacks involve automated scripts that interact with APIs in predictable and repetitive manners. In those cases, WAAP can use request patterns or user action monitoring to identify the script with high accuracy. Heuristics may define bots by checking how users interact with page elements, such as buttons or forms, and flagging those that behave unnaturally.This layered approach ensures that the most sophisticated automated attack attempts are caught in the net and mitigated without affecting legitimate traffic.Protect your APIs with the click of a button using Gcore WAAPAI offers proactive, intelligent solutions that can address the modern complexities of cybersecurity. These technologies empower organizations to secure APIs against even the most sophisticated threats, including zero-day vulnerabilities and undiscovered APIs.Interested in protecting your APIs with WAAP? Download our ebook to discover cybersecurity best practices, the most prevalent threats, and how WAAP can protect your business’s digital infrastructure, including APIs. Or, reach out to our team to learn more about Gcore WAAP.Discover why WAAP is a must-have for API protection
Subscribe to our newsletter
Get the latest industry trends, exclusive insights, and Gcore updates delivered straight to your inbox.