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Keyloggers | How Keyloggers Work and How to Detect Them

  • By Gcore
  • September 7, 2023
  • 10 min read
Keyloggers | How Keyloggers Work and How to Detect Them

Keyloggers are hardware or software that record keyboard input by capturing all keystrokes typed on a keyboard, including usernames and passwords. Keyloggers can be used for helpful, legal reasons, or cause harm when used by malicious actors. In this article, we explain what keyloggers are and how they work, how to detect keylogger activity, and how to remove keyloggers. You’ll also learn personal and enterprise-level protective measures against malicious keyloggers.

What Are Keyloggers?

Keyloggers, also known as keystroke loggers or keyboard capturers, are specialized hardware or software that capture keyboard input in real time. They capture all keystrokes typed on a keyboard. This includes sensitive information such as usernames and passwords. Keyloggers can even record keystrokes from hardware and on-screen keyboards including numerical keys and special characters.

Individuals, businesses, and governments utilize keyloggers for various purposes, both legitimate and malicious. Parents employ keyloggers to monitor their children’s online activities, safeguarding them from potential dangers and tracking interactions on platforms like WhatsApp, call logs, and even their location. Businesses use keyloggers to enhance productivity by monitoring employee behavior, especially in remote work contexts, ensuring adherence to policies like cybersecurity and data leak prevention. Government agencies may deploy keyloggers for intelligence and cybersecurity, with law enforcement using them for surveillance and fraud prevention—although unauthorized keylogging without consent or a valid warrant is often considered unethical and possibly illegal.

How Keyloggers Work

Captured keystrokes are typically stored in a specific file or location called a keylog, and then transmitted as a logfile. This logfile provides a detailed record of the online and offline activities performed on the affected device, organized and formatted in a way that enables analysis or further action by whoever is overseeing the keylogging.

The information collected by keyloggers can include websites visited, as well as sensitive data like usernames, passwords, PINs, and credit card details that were entered on those websites. Keyloggers can also be configured to capture mouse clicks, microphone inputs, webcam or screen captures, network and Wi-Fi information, system details, clipboard contents, browser history, search engine queries, and instant messaging conversations.

Types of Keyloggers: Hardware and Software

There are two primary types of keyloggers: hardware keyloggers and software keyloggers. Hardware Keyloggers are physical devices used to record keystrokes. They come in various forms, shapes, and sizes, and are connected between the keyboard and the computer’s CPU. Some hardware keyloggers can even be built into keyboards or hardware circuitry. Physical access to the target device is usually required to install and retrieve hardware keyloggers. Wireless keyloggers are an exception to this rule, as they can transmit the captured log files wirelessly to a remote location.

Software keyloggers are software programs designed to record and monitor keyboard inputs. They can be installed by the user intentionally, added by third parties (including attackers,) or unknowingly installed as a result of downloading malware or spyware from malicious websites. Software keyloggers are generally easier to install compared to hardware keyloggers, and can be more challenging to detect or remove. Some software keyloggers, known as kernel-based keyloggers, operate at the core level of the operating system, making them particularly challenging to identify and eliminate as they can hide in root folders.

CriteriaHardware keyloggersSoftware keyloggers
InstallationPhysically installed either between a computer’s keyboard and the PS/2 or USB port, or within keyboard circuitryInstalled by user or third party physically, or in some malicious cases installed remotely via email attachments, downloads, or compromised websites
ExecutionPlug and play, no executable software required, independent of the operating system, and non-detectableRuns in the background without user knowledge, may disguise itself as a legitimate process or implement rootkit functionality to evade detection
LoggingCaptures keystrokes in real time by intercepting electrical signals sent from the keyboard to the computerCaptures keystrokes in real time by intercepting electrical signals sent from the keyboard to the computer
StorageCaptured keystrokes are stored within the keylogger’s built-in memory or removable SD card; some storage devices can store millions of keystrokesCaptured keystrokes may be encrypted and temporarily stored as keylogs/logfiles on the infected computer’s storage or in a hidden location before transmission
TimestampingSoftware keyloggers may incorporate timestamping functionality on keylogsSoftware keyloggers may incorporate timestamping functionality on keylogs
EncryptionRarely used, but a password is required to access logfiles after retrieval of keylogger; wireless hardware keylogger memory may be protected by hardware encryptionLogs may be compressed and encrypted to improve transmission privacy, efficiency, and frequency
RetrievalOften requires physical access to the victim’s device to retrieve the keylogger, except for wireless hardware keyloggersPhysical access to affected computer is not required for retrieval of keylogger and keylogs or logfiles
Transmission/ExfiltrationWireless hardware keyloggers act as Wi-Fi hotspots and can live-transmit keystroke data wirelessly or via emailKeystroke data is transmitted via email or uploaded to remote FTP server
AnalysisOperator/attacker analyzes keylogs/logfiles for specific dataOperator/attacker analyzes keylogs/logfiles for specific data

How Are Keyloggers Used?

Keyloggers are used by their operators to look for specific information in the data collected. Operators sift through the data looking for information that serves their specific purpose, whether benign or malicious.

Keylogger Use by Malicious Attackers

For a malicious attacker, the primary objective is to harvest sensitive information from the captured keylogs. This may include usernames, passwords, credit card details, and PINs. Armed with this information, attackers can carry out identity theft, make unauthorized financial transactions, or gain unauthorized access to various accounts and systems. They might target online banking accounts, email accounts, or social media profiles—any platform that requires login credentials. The use of keyloggers for malicious purposes is illegal and unethical.

Keylogger Use by Benign Operators

In a legitimate context, keylog data may be used by employers to ensure employee compliance with company policies, analyzing the sites visited, time spent on websites, browser history, and application usage. This information helps employers gauge productivity, adherence to cybersecurity policies, and adherence to work-related tasks. It is most commonly used for remote workers, when there is no physical oversight of the employee. Keylogger use in a legitimate context should be transparent, legal, and respect the privacy and rights of individuals being monitored.

How to Detect Keyloggers

Learning how to detect keyloggers is a crucial step in preventing potential damage from keylogging attacks. If legitimate keylogging is in place, you should have been informed about it and it should not cause the red flags mentioned in this section. These detection procedures are intended for malicious keyloggers.

Hardware keyloggers are easiest to detect by simple physical inspection of keyboard circuitry or USB and PS/2 ports. Pay particular attention to hardware additions on the computer, such as unknown memory sticks/dongles.

Some software keyloggers, especially those with rootkit functionality, can be challenging to detect. A powerful antivirus scan can discover keyloggers or other malware. You can also use command line (CMD) prompts or perform a quick scan on Windows to check for software loggers. Go to Windows key > Settings > Update and Security > Windows Security > Virus and threat protection > Quick scan.

Figure 1: Windows Security menu

In addition, the following computer behaviors can help to alert you to possible software keylogger activity regardless of your operating system.

1. Unknown programs: If you don’t recognize or remember installing a program/software/app that is on your device, it could be a keylogger installed without your knowledge.

2. Slow performance: Keylogger activity in the background may result in abnormally slow performance. Check background processes on Windows Task Manager (Windows) or Activity Monitor (macOS.)

3. Constant crashes: You may experience frequent freezes and programs crashing unexpectedly if keylogger software is in use.

4. Unusual keyboard behavior: Watch out for unusual keyboard behaviors such as automatic typing or mistyping. Though this can be caused by incorrect keyboard layout, Num Lock, outdated drivers, or a faulty keyboard—but such actions can also be caused by keyloggers.

5. Pop-ups and site redirects: Annoying pop-ups and site redirects can be indicative of either keylogging attacks or adware activity.

6. Changed browser settings: A browser-based keylogger from malicious sites can change your browser settings. These attacks use CSS scripts, Man-In-The-Browser (MITB) attacks, or web-form-based keyloggers.

7. Strange log-in notifications: Strange log-in notifications may be the result of a hacker who has obtained your login credentials via keylogging and is attempting to access your account.

8. Unsolicited authentication prompts: Unexpected or unsolicited one-time passwords (OTPs) and two-factor authentication (2FA) prompts can indicate that a threat actor has accessed your credentials using a keylogger.

9. Unknown online activity: If you log in to an online service and notice unknown activity or changed settings, someone may have accessed your account using data gathered by a keylogger.

10. Unusual network traffic: In some cases, unusual network activity may also be indicative of keylogger presence. This can usually be spotted via network traffic monitoring.

Combining several of these keylogger detection techniques maximizes the likelihood of detecting malicious keylogger activity. The next step is to put a stop to keylogging by removing the keylogger.

How to Remove Keyloggers

Once keyloggers have been detected, it is recommended to immediately take the following countermeasures to protect your business and prevent future keylogging attacks.

Physical Intervention

Remove any unknown devices from the computer, such as a USB dongle or memory stick, especially those near the keyboard. If you suspect keylogging hardware, it’s best to have a technician disassemble the keyboard and check for embedded hardware keyloggers, especially if no external devices are visible and other responses, outlined below, have been exhausted. This gives you the best chance of uncovering any malicious devices without damaging your computer.

Uninstall Unknown Programs

Remove programs, software, or apps that you don’t recognize or don’t remember installing. You may first want to use a search engine to check if those programs, software, or apps are legitimate software that you downloaded but are not in use.

To uninstall an app on Windows, press Windows key > Settings > Apps > select app > Uninstall.

Figure 2: Windows Settings menu

To uninstall a program on Windows via the control panel, go to Control Panel > Programs > Uninstall a program > right-click program > Uninstall.

Use Task Manager

If a known keylogger app or program doesn’t reflect on your apps or programs list, it could mean that the app is hidden. Keyloggers with rootkit functionality operate this way. In such instances, the Task Manager can be helpful in locating and removing the keylogger.

To find and remove a keylogger using the Windows Task Manager app, right-click on the taskbar to go to Task Manager or press Ctrl + Shift + Esc. Then, locate the keylogger by name, logo, or icon and right-click. This can be difficult as most keyloggers will disguise their real names. Double check app legitimacy via a search engine or ask IT personnel for clarification.

Figure 3: Task Manager Processes menu

From the menu options, click Open file location. Locate and double-click the application file marked Setup/Uninstall. You can locate the particular application file by hovering your mouse pointer over all files labeled Application. When dealing with unknown files in the Windows Program folder exercise caution and double check with a simple online search.

Figure 4: Keylogger setup-uninstall file

Click Yes to grant administrative permission to make changes, then click Yes to uninstall. This will completely remove the keylogger from your computer.

Clear Temporary Files

Keyloggers can hide in your temporary files folder. Temporary files are created to temporarily store information and free memory for other tasks. They’re also safety nets that prevent data loss when programs run. It is also important to clear temporary files on mobile devices.

To clear temporary files on Windows, press Windows key > Settings > System > Storage > Temporary files > select files > Remove files.

Figure 5: Windows Temporary Files menu

Restore Default Browser Settings

Browser-based keyloggers can be removed by restoring default browser settings. It is also important to restore the browser’s default settings on mobile devices if keylogger activity is suspected.

Reset Computer

You can reset your computer to a point (date and time) before the keylogger infection. This action will give you a fresh start, but will remove most of your apps, pre-installed desktop apps, antivirus software, digital licenses, and associated digital content. It is essential to ensure that important files and programs are backed up before resetting—and to be certain that a keylogger is not disguised as one of these.

To reset your Windows computer, press Windows key > Settings > Update and security > Recovery > Reset this PC > Get started > Keep my files or Remove everything.

Figure 6: Windows Reset PC menu

Use Windows Security

The most popular operating systems, Windows and macOS, offer some level of threat protection. For instance, Windows Security (Defender) can scan your PC and remove viruses and other threats such as keyloggers. This is one of the best free antivirus programs.

To remove keyloggers using Windows Security, press the Windows key > Settings > Update and Security > Windows Security > Virus and threat protection > Scan options > Full scan > Scan now. Then, remove any keyloggers or malware that is found.

Figure 7: Windows Security scan menu

Enable Microsoft Defender Offline Scan

Microsoft Defender Offline Scan scans your PC for malicious software such as keyloggers and automatically removes them even when you’re offline. It is quick and easy to set up.

To enable Microsoft Defender Offline Scan, press the Windows key > Settings > Update and Security > Windows Security > Virus and threat protection > Scan options > Microsoft Defender Offline Scan > Scan now.

Figure 8: Windows Security menu

Use Dedicated Antivirus/Spyware Remover

If all else fails, a dedicated and robust antivirus or spyware removal tool can help to remove keyloggers from your device. There are many options to choose from. Some of the most popular include Bitdefender, Kaspersky, Norton, and McAfee.

How to Protect Yourself From Keyloggers

As the saying goes, an ounce of prevention is worth a pound of cure. Proactively preventing keylogger infection is much easier than removing keyloggers. Consider taking the following steps to protect yourself from keylogging attacks.

ActionDescription
Stay informedUnderstand and stay up to date on keyloggers, their characteristics, the ways they operate, symptoms of keylogger attacks, and necessary countermeasures.
Monitor devicesNever leave personal or company-issued devices unattended or in places where unauthorized persons can access them and introduce keyloggers.
Move devices away from CCTVMove devices away from CCTV, as threat actors can replay footage to capture keyboard input or keystroke patterns entered by users.
Lock devicesLock your devices when not in use. Set up a PIN/password and minimize Sleep/Hibernate intervals.
Supervise contractorsAlways supervise external IT contractors who have access to your company’s IT infrastructure to prevent malicious actors from installing keyloggers.
Inspect hardwareRoutinely inspect your devices for hardware keyloggers, paying attention to USB-PS/2 ports and keyboard internals.
Download with cautionMost software keyloggers are contracted through malicious downloads. Avoid cracked or pirated software. Only download software or files from trusted sources.
Beware of email links and attachmentsEmail links and attachments from unknown sources are red flags, they can contain keyloggers. Double-check suspicious messages from known contacts to eliminate phishing, spear-phishing, or impersonation.
Use ad blockers and pop-up blockersAd-blockers and pop-up blockers do not directly prevent keylogging. However, they can block malicious scripts and prevent site redirects to malicious websites distributing keyloggers.
Try keystroke encryptionKeystroke encryption prevents keyboard inputs from being intercepted by keyloggers. It encrypts keystrokes with a military-grade cryptographic algorithm that can only be decrypted by the operating system or the receiving application.
Use auto-fillEnable auto-fill in your browser settings so web forms fill automatically with a single click, without typing or tapping the keyboard.
Practice good password hygieneUse password managers to skip typing usernames and passwords for keyloggers to steal, but be wary of browser extension versions. Do not reuse or repeat passwords, and update your passwords regularly to invalidate any stolen passwords.
Enable authentication and log-in alertsEnable two-factor authentication (2FA), one-time passwords (OTP) and log-in alerts. They can alert you to unauthorized access by hackers using details obtained via keylogging.
Update operating systemRegularly updating your operating system, software, apps, and programs patches vulnerabilities protecting you from existing, evolving, and emerging threats, including keylogging.
Enable Virus and threat protection in WindowsPress Windows key > Settings > Update and Security > Windows Security > Virus and threat protection > Virus and threat protection settings > Manage settings, then turn on real-time protection, cloud-delivered protection, automatic sample submission, and tamper protection.
Use strong antivirusA robust antivirus, anti-keylogger, or anti-spyware can detect, remove, and protect against most keyloggers.

Protect Yourself With Gcore’s Web Application Security

Gcore protects against zero-day attacks and the OWASP Top 10. Zero-day attacks exploit previously unknown software vulnerabilities that may be used to deliver malware such as keyloggers. The same applies to OWASP Top 10 vulnerabilities, such as injecting or cross-site scripting.

Gcore’s advanced Web Application Security solution utilizes machine learning and real-time monitoring to scan and filter incoming traffic. It protects your assets against zero-day attacks and OWASP Top 10 attacks that can be used by attackers to deliver malware which may contain keyloggers.

Conclusion

When used ethically, as in parental or employee monitoring, keyloggers can be helpful. However, when used maliciously to steal sensitive data, keylogging attacks represent a major vulnerability, especially for enterprises that enable remote work from a range of devices. You can take steps to protect against and remove keyloggers yourself, but sometimes extra help is required.

With Gcore’s Web Application Security solution, your critical assets are protected against all forms of OWASP Top 10 and zero-day attacks that can be exploited to deliver malware which may include keyloggers.

Try for free

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

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

What if you could ask your infrastructure questions and get real answers?With Gcore’s open-source implementation of the Model Context Protocol (MCP), now you can. MCP turns generative AI into an agent that understands your infrastructure, responds to your queries, and takes action when you need it to.In this post, we’ll demo how to use MCP to explore and inspect your Gcore environment just by prompting, to list resources, check audit logs, and generate cost reports. We’ll also walk through a fun bonus use case: provisioning infrastructure and exporting it to Terraform.What is MCP and why do devs love it?Originally developed by Anthropic, the Model Context Protocol (MCP) is an open standard that turns language models into agents that interact with structured tools: APIs, CLIs, or internal systems. Gcore’s implementation makes this protocol real for our customers.With MCP, you can:Ask questions about your infrastructureList, inspect, or filter cloud resourcesView cost data, audit logs, or deployment metadataExport configs to TerraformChain multi-step operations via natural languageGcore MCP removes friction from interacting with your infrastructure. Instead of wiring together scripts or context-switching across dashboards and CLIs, you can just…ask.That means:Faster debugging and auditsMore accessible infra visibilityFewer repetitive setup tasksBetter team collaborationBecause it’s open source, backed by the Gcore Python SDK, you can plug it into other APIs, extend tool definitions, or even create internal agents tailored to your stack. Explore the GitHub repo for yourself.What can you do with it?This isn’t just a cute chatbot. Gcore MCP connects your cloud to real-time insights. Here are some practical prompts you can use right away.Infrastructure inspection“List all VMs running in the Frankfurt region”“Which projects have over 80% GPU utilization?”“Show all volumes not attached to any instance”Audit and cost analysis“Get me the API usage for the last 24 hours”“Which users deployed resources in the last 7 days?”“Give a cost breakdown by region for this month”Security and governance“Show me firewall rules with open ports”“List all active API tokens and their scopes”Experimental automation“Create a secure network in Tokyo, export to Terraform, then delete it”We’ll walk through that last one in the full demo below.Full video demoWatch Gcore’s AI Software Engineer, Algis Dumbris, walk through setting up MCP on your machine and show off some use cases. If you prefer reading, we’ve broken down the process step-by-step below.Step-by-step walkthroughThis section maps to the video and shows exactly how to replicate the workflow locally.1. Install MCP locally (0:00–1:28)We use uv to isolate the environment and pull the project directly from GitHub.curl -Ls https://astral.sh/uv/install.sh | sh uvx add gcore-mcp-server https://github.com/G-Core/gcore-mcp-server Requirements:PythonGcore account + API keyTool config file (from the repo)2. Set up your environment (1:28–2:47)Configure two environment variables:GCORE_API_KEY for authGCORE_TOOLS to define what the agent can access (e.g., regions, instances, costs, etc.)Soon, tool selection will be automatic, but today you can define your toolset in YAML or JSON.3. Run a basic query (3:19–4:11)Prompt:“Find the Gcore region closest to Antalya.”The agent maps this to a regions.list call and returns: IstanbulNo need to dig through docs or write an API request.4. Provision, export, and clean up (4:19–5:32)This one’s powerful if you’re experimenting with CI/CD or infrastructure-as-code.Prompt:“Create a secure network in Tokyo. Export to Terraform. Then clean up.”The agent:Provisions the networkExports it to Terraform formatDestroys the resources afterwardYou get usable .tf output with no manual scripting. Perfect for testing, prototyping, or onboarding.Gcore: always building for developersTry it now:Clone the repoInstall UVX + configure your environmentStart prompting your infrastructureOpen issues, contribute tools, or share your use casesThis is early-stage software, and we’re just getting started. Expect more tools, better UX, and deeper integrations soon.Watch how easy it is to deploy an inference instance with Gcore

How to protect login pages with Gcore WAAP

Exposed login pages are a common vulnerability across web applications. Attackers often use automated tools to guess credentials in brute-force or credential-stuffing attacks, probe for login behavior to exploit session or authentication logic, or overload your infrastructure with fake requests.Without specific rules for login-related traffic, your application might miss these threats or apply overly broad protections that disrupt real users. Fortunately, Gcore WAAP makes it easy to defend these sensitive endpoints without touching your application code.In this guide, we’ll show you how to use WAAP’s custom rule engine to identify login traffic and apply protections like CAPTCHA to reduce risk, block automated abuse, and maintain a smooth experience for legitimate users. We’ve also included a complete video walkthrough from Gcore’s Security Presales Engineer, Michal Zalewski.Video walkthroughHere’s Gcore’s Michal Zalewski giving a full walkthrough of the steps in this article.Step 1: Access your WAAP configurationGo to portal.gcore.com and log in.Navigate to WAAP in the sidebar. If you’re not yet a WAAP user, it costs just $26/month.Select the resource that hosts your login form; for example, gcore.zalewski.cloud.Step 2: Create a custom ruleIn the main panel of your selected resource, go to WAAP Rules.Click Add Custom Rule in the upper-right corner.Step 3: Define the login page URLIdentify the login endpoint you want to protect:Use tools like Burp Suite or the "Inspect" feature in your browser to verify the login page URL.In Burp Suite, use the Proxy tab, or in the browser, check the Network tab to inspect a login request.Look for the path (e.g., /login.php) and HTTP method (POST).In the custom rule setup:Enter the URL (e.g., /login.php).Tag the request using a predefined tag. Select Login Page.Step 4: Name and save the ruleProvide a name for the rule, such as “Login Page URL”, and save it.Step 5: Add a CAPTCHA challenge ruleTo protect the login page from automated abuse:Create a new custom rule.Name it something like “Login Page Challenge”.Under Conditions, select the previously created Login Page tag.Set the Action to CAPTCHA.Save the rule.Step 6: Test the protectionReturn to your browser and turn off any proxy tools.Refresh the login page.You should now be challenged with a CAPTCHA each time the login page loads.Once the CAPTCHA is completed successfully, users can log in as usual.Monitor, adapt, and alertAfter deployment:Track rate limit trigger frequencyMonitor WAAP logs for anomaly detectionRotate exemptions or thresholds based on live behaviorFor analytics, refer to the WAAP analytics documentation.Bonus tips for hardened protectionCombine with bot protection: Enable WAAP’s bot mitigation to identify headless browsers and automation tools like Puppeteer or Selenium. See our bot protection docs for setup instructions.Customize 429 responses: Replace default error pages with branded messages or a fallback action. Consider including a support link or CAPTCHA challenge. Check out our response pages documentation for more details.Use geo or ASN exceptions: Whitelist trusted locations or block known bot-heavy ASNs if your audience is localized.Automate it: optional API and Terraform supportTeams with IaC pipelines or security automation workflows might want to automate login page protection with rate limiting. This keeps your WAAP config version-controlled and repeatable.You can use the WAAP API or Terraform to:Create or update rulesRotate session keys or thresholdsExport logs for auditingExplore the WAAP API documentation and WAAP Terraform provider documentation for more details.Stop abuse before it starts with GcoreLogin pages are high-value targets, but they don’t have to be high risk. With Gcore WAAP, setting up robust defenses takes just a few minutes. By tagging login traffic and applying challenge rules like CAPTCHA, you can reduce automated attack risk without sacrificing user experience.As your application grows, revisit your WAAP rules regularly to adapt to new threats, add behavior-based detection, and fine-tune your protective layers. For more advanced configurations, check out our documentation or reach out to Gcore support.Get WAAP today for just $26/month

3 underestimated security risks of AI workloads and how to overcome them

3 underestimated security risks of AI workloads and how to overcome them

Artificial intelligence workloads introduce a fundamentally different security landscape for engineering and security teams. Unlike traditional applications, AI systems must protect not just endpoints and networks, but also training data pipelines, feature stores, model repositories, and inference APIs. Each phase of the AI life cycle presents distinct attack vectors that adversaries can exploit to corrupt model behavior, extract proprietary logic, or manipulate downstream outputs.In this article, we uncover three security vulnerabilities of AI workloads and explain how developers and MLOps teams can overcome them. We also look at how investing in your AI security can save time and money, explore the challenges that lie ahead for AI security, and offer a simplified way to protect your AI workloads with Gcore.Risk #1: data poisoningData poisoning is a targeted attack on the integrity of AI systems, where malicious actors subtly inject corrupted or manipulated data into training pipelines. The result is a model that behaves unpredictably, generates biased or false outputs, or embeds hidden logic that can be triggered post-deployment. This can undermine business-critical applications—from fraud detection and medical diagnostics to content moderation and autonomous decision-making.For developers, the stakes are high: poisoned models are hard to detect once deployed, and even small perturbations in training data can have system-wide consequences. Luckily, you can take a few steps to mitigate against data poisoning and then implement zero-trust AI to further protect your workloads.Mitigation and hardeningRestrict dataset access using IAM, RBAC, or identity-aware proxies.Store all datasets in versioned, signed, and hashed formats.Validate datasets with automated schema checks, label distribution scans, and statistical outlier detection before training.Track data provenance with metadata logs and checksums.Block training runs if datasets fail predefined data quality gates.Integrate data validation scripts into CI/CD pipelines pre-training.Enforce zero-trust access policies for data ingestion services.Solution integration: zero-trust AIImplement continuous authentication and authorization for each component interacting with data (e.g., preprocessing scripts, training jobs).Enable real-time threat detection during training using runtime security tools.Automate incident response triggers for unexpected file access or data source changes.Risk #2: adversarial attacksAdversarial attacks manipulate model inputs in subtle ways that trick AI systems into making incorrect or dangerous decisions. These perturbations—often imperceptible to humans—can cause models to misclassify images, misinterpret speech, or misread sensor data. In high-stakes environments like facial recognition, autonomous vehicles, or fraud detection, these failures can result in security breaches, legal liabilities, or physical harm.For developers, the threat is real: even state-of-the-art models can be easily fooled without adversarial hardening. The good news? You can make your models more robust by combining defensive training techniques, input sanitization, and secure API practices. While encrypted inference doesn’t directly block adversarial manipulation, it ensures that sensitive inference data stays protected even if attackers attempt to probe the system.Mitigation and hardeningUse adversarial training frameworks like CleverHans or IBM ART to expose models to perturbed inputs during training.Apply input sanitization layers (e.g., JPEG re-encoding, blurring, or noise filters) before data reaches the model.Implement rate limiting and authentication on inference APIs to block automated adversarial probing.Use model ensembles or randomized smoothing to improve resilience to small input perturbations.Log and analyze input-output patterns to detect high-variance or abnormal responses.Test models regularly against known attack vectors using robustness evaluation tools.Solution integration: encrypted inferenceWhile encryption doesn't prevent adversarial inputs, it does mean that input data and model responses remain confidential and protected from observation or tampering during inference.Run inference in trusted environments like Intel SGX or AWS Nitro Enclaves to protect model and data integrity.Use homomorphic encryption or SMPC to process encrypted data without exposing sensitive input.Ensure that all intermediate and output data is encrypted at rest and in transit.Deploy access policies that restrict inference to verified users and approved applications.Risk #3: model leakage of intellectual assetsModel leakage—or model extraction—happens when an attacker interacts with a deployed model in ways that allow them to reverse-engineer its structure, logic, or parameters. Once leaked, a model can be cloned, monetized, or used to bypass the very defenses it was meant to enforce. For businesses, this means losing competitive IP, compromising user privacy, or enabling downstream attacks.For developers and MLOps teams, the challenge is securing deployed models in a way that balances performance and privacy. If you're exposing inference APIs, you’re exposing potential entry points—but with the right controls and architecture, you can drastically reduce the risk of model theft.Mitigation and hardeningEnforce rate limits and usage quotas on all inference endpoints.Monitor for suspicious or repeated queries that indicate model extraction attempts.Implement model watermarking or fingerprinting to trace unauthorized model use.Obfuscate models before deployment using quantization, pruning, or graph rewriting.Disable or tightly control any model export functionality in your platform.Sign and verify inference requests and responses to ensure authenticity.Integrate security checks into CI/CD pipelines to detect risky configurations—such as public model endpoints, export-enabled containers, or missing inference authentication—before they reach production.Solution integration: native security integrationIntegrate model validation, packaging, and signing into CI/CD pipelines.Serve models from encrypted containers or TEEs, with minimal runtime exposure.Use container and image scanning tools to catch misconfigurations before deployment.Centralize monitoring and protection with tools like Gcore WAAP for real-time anomaly detection and automated response.How investing in AI security can save your business moneyFrom a financial point of view, the use of AI and machine learning in cybersecurity can lead to massive cost savings. Organizations that utilize AI and automation in cybersecurity have saved an average of $2.22 million per data breach compared to organizations that do not have these protections in place. This is because the necessity for manual oversight is reduced, lowering the total cost of ownership, and averting costly security breaches. The initial investment in advanced security technologies yields returns through decreased downtime, fewer false positives, and an enhanced overall security posture.Challenges aheadWhile securing the AI lifecycle is essential, it’s still difficult to balance robust security with a positive user experience. Rigid scrutiny can add additional latency or false positives that can stop operations, but AI-powered security can avoid such incidents.Another concern organizations must contend with is how to maintain current AI models. With threats changing so rapidly, today's newest model could easily become outdated by tomorrow’s. Solutions must have an ongoing learning ability so that security detection parameters can be revised.Operational maturity is also a concern, especially for companies that operate in multiple geographies. Well-thought-out strategies and sound governance processes must accompany the integration of complex AI/ML tools with existing infrastructure, but automation still offers the most benefits by reducing the overhead on security teams and helping ensure consistent deployment of security policies.Get ahead of AI security with GcoreAI workloads introduce new and often overlooked security risks that can compromise data integrity, model behavior, and intellectual property. By implementing practices like zero-trust architecture, encrypted inference, and native security integration, developers can build more resilient and trustworthy AI systems. As threats evolve, staying ahead means embedding security at every phase of the AI lifecycle.Gcore helps teams apply these principles at scale, offering native support for zero-trust AI, encrypted inference, and intelligent API protection. As an experienced AI and security solutions provider, our DDoS Protection and AI-enabled WAAP solutions integrate natively with Everywhere Inference and GPU Cloud across 210+ global points of presence. That means low latency, high performance, and proven, robust security, no matter where your customers are located.Talk with our AI security experts and secure your workloads today

Flexible DDoS mitigation with BGP Flowspec cover image

Flexible DDoS mitigation with BGP Flowspec

For customers who understand their own network traffic patterns, rigid DDoS protection can be more of a limitation than a safeguard. That’s why Gcore supports BGP Flowspec: a flexible, standards-based method for defining granular filters that block or rate-limit malicious traffic in real time…before it reaches your infrastructure.In this article, we’ll walk through:What Flowspec is and how it worksThe specific filters and actions Gcore supportsCommon use cases, with example rule definitionsHow to activate and monitor Flowspec in your environmentWhat is the BGP Flowspec?BGP Flowspec (RFC 8955) extends Border Gateway Protocol to distribute traffic filtering rules alongside routing updates. Instead of static ACLs or reactive blackholing, Flowspec enables near-instantaneous propagation of mitigation rules across networks.BGP tells routers how to reach IP prefixes across the internet. With Flowspec, those same BGP announcements can now carry rules, not just routes. Each rule describes a pattern of traffic (e.g., TCP SYN packets >1000 bytes from a specific subnet) and what action to take (drop, rate-limit, mark, or redirect).What are the benefits of the BGP Flowspec?Most traditional DDoS protection services react to threats after they start, whether by blackholing traffic to a target IP, redirecting flows to a scrubbing center, or applying rigid, static filters. These approaches can block legitimate traffic, introduce latency, or be too slow to respond to fast-evolving attacks.Flowspec offers a more flexible alternative.Proactive mitigation: Instead of waiting for attacks, you can define known-bad traffic patterns ahead of time and block them instantly. Flowspec lets experienced operators prevent incidents before they start.Granular filtering: You’re not limited to blocking by IP or port. With Flowspec, you can match on packet size, TCP flags, ICMP codes, and more, enabling fine-tuned control that traditional ACLs or RTBH don’t support.Edge offloading: Filtering happens directly on Gcore’s routers, offloading your infrastructure and avoiding scrubbing latency.Real-time updates: Changes to rules are distributed across the network via BGP and take effect immediately, faster than manual intervention or standard blackholing.You still have the option to block traffic during an active attack, but with Flowspec, you gain the flexibility to protect services with minimal disruption and greater precision than conventional tools allow.Which parts of the Flowspec does Gcore implement?Gcore supports twelve filter types and four actions of the Flowspec.Supported filter typesGcore supports all 12 standard Flowspec match components.Filter FieldDescriptionDestination prefixTarget subnet (usually your service or app)Source prefixSource of traffic (e.g., attacker IP range)IP protocolTCP, UDP, ICMP, etc.Port / Source portMatch specific client or server portsDestination portMatch destination-side service portsICMP type/codeFilter echo requests, errors, etc.TCP flagsFilter packets by SYN, ACK, RST, FIN, combinationsPacket lengthFilter based on payload sizeDSCPQuality of service code pointFragmentMatch on packet fragmentation characteristicsSupported actionsGcore DDoS Protection supports the following Flowspec actions, which can be triggered when traffic matches a specific filter:ActionDescriptionTraffic-rate (0x8006)Throttle/rate limit traffic by byte-per-second rateredirectRedirect traffic to alternate location (e.g., scrubbing)traffic-markingApply DSCP marks for downstream classificationno-action (drop)Drop packets (rate-limit 0)Rule orderingRFC 5575 defines the implicit order of Flowspec rules. The crucial point is that more specific announcements take preference, not the order in which the rules are propagated.Gcore also respects Flowspec rule ordering per RFC 5575. More specific filters override broader ones. Future support for Flowspec v2 (with explicit ordering) is under consideration, pending vendor adoption.Blackholing and extended blackholing (eBH)Remote-triggered blackhole (RTBH) is a standardized protection method that the client manages via BGP by analyzing traffic, identifying the direction of the attack (i.e., the destination IP address). This method protects against volumetric attacks.Customers using Gcore IP Transit can trigger immediate blackholing for attacked prefixes via BGP, using the well-known blackhole community tag 65000:666. All traffic to that destination IP is dropped at Gcore’s edge.The list of supported BGP communities is available here.BGP extended blackholeExtended blackhole (eBH) allows for more granular blackholing that does not affect legitimate traffic. For customers unable to implement Flowspec directly, Gcore supports eBH. You announce target prefixes with pre-agreed BGP communities, and Gcore translates them into Flowspec mitigations.To configure this option, contact our NOC at noc@gcore.lu.Monitoring and limitationsGcore can support several logging transports, including mail and Slack.If the number of Flowspec prefixes exceeds the configured limit, Gcore DDoS Protection stops accepting new announcements, but BGP sessions and existing prefixes will stay active. Gcore will receive a notification that you reached the limit.How to activateActivation takes just two steps:Define rules on your edge router using Flowspec NLRI formatAnnounce rules via BGP to Gcore’s intermediate control planeThen, Gcore validates and propagates the filters to border routers. Filters are installed on edge devices and take effect immediately.If attack patterns are unknown, you’ll first need to detect anomalies using your existing monitoring stack, then define the appropriate Flowspec rules.Need help activating Flowspec? Get in touch via our 24/7 support channels and our experts will be glad to assist.Set up GRE and benefit from Flowspec today

Securing AI from the ground up: defense across the lifecycle

As more AI workloads shift to the edge for lower latency and localized processing, the attack surface expands. Defending a data center is old news. Now, you’re securing distributed training pipelines, mobile inference APIs, and storage environments that may operate independently of centralized infrastructure, especially in edge or federated learning contexts. Every stage introduces unique risks. Each one needs its own defenses.Let’s walk through the key security challenges across each phase of the AI lifecycle, and the hardening strategies that actually work.PhaseTop threatsHardening stepsTrainingData poisoning, leaksValidation, dataset integrity tracking, RBAC, adversarial trainingDevelopmentModel extraction, inversionRate limits, obfuscation, watermarking, penetration testingInferenceAdversarial inputs, spoofed accessInput filtering, endpoint auth, encryption, TEEsStorage and deploymentModel theft, tamperingEncrypted containers, signed builds, MFA, anomaly monitoringTraining: your model is only as good as its dataThe training phase sets the foundation. If the data going in is poisoned, biased, or tampered with, the model will learn all the wrong lessons and carry those flaws into production.Why it mattersData poisoning is subtle. You won’t see a red flag during training logs or a catastrophic failure at launch. These attacks don’t break training, they bend it.A poisoned model may appear functional, but behaves unpredictably, embeds logic triggers, or amplifies harmful bias. The impact is serious later in the AI workflow: compromised outputs, unexpected behavior, or regulatory non-compliance…not due to drift, but due to training-time manipulation.How to protect itValidate datasets with schema checks, label audits, and outlier detection.Version, sign, and hash all training data to verify integrity and trace changes.Apply RBAC and identity-aware proxies (like OPA or SPIFFE) to limit who can alter or inject data.Use adversarial training to improve model robustness against manipulated inputs.Development and testing: guard the logicOnce you’ve got a trained model, the next challenge is protecting the logic itself: what it knows and how it works. The goal here is to make attacks economically unfeasible.Why it mattersModels encode proprietary logic. When exposed via poorly secured APIs or unprotected inference endpoints, they’re vulnerable to:Model inversion: Extracting training dataExtraction: Reconstructing logicMembership inference: Revealing whether a datapoint was in trainingHow to protect itApply rate limits, logging, and anomaly detection to monitor usage patterns.Disable model export by default. Only enable with approval and logging.Use quantization, pruning, or graph obfuscation to reduce extractability.Explore output fingerprinting or watermarking to trace unauthorized use in high-value inference scenarios.Run white-box and black-box adversarial evaluations during testing.Integrate these security checks into your CI/CD pipeline as part of your MLOps workflow.Inference: real-time, real riskInference doesn’t get a free pass because it’s fast. Security needs to be just as real-time as the insights your AI delivers.Why it mattersAdversarial attacks exploit the way models generalize. A single pixel change or word swap can flip the classification.When inference powers fraud detection or autonomous systems, a small change can have a big impact.How to protect itSanitize input using JPEG compression, denoising, or frequency filtering.Train on adversarial examples to improve robustness.Enforce authentication and access control for all inference APIs—no open ports.Encrypt inference traffic with TLS. For added privacy, use trusted execution environments (TEEs).For highly sensitive cases, consider homomorphic encryption or SMPC—strong but compute-intensive solutions.Check out our free white paper on inference optimization.Storage and deployment: don’t let your model leakOnce your model’s trained and tested, you’ve still got to deploy and store it securely—often across multiple locations.Why it mattersUnsecured storage is a goldmine for attackers. With access to the model binary, they can reverse-engineer, clone, or rehost your IP.How to protect itStore models on encrypted volumes or within enclaves.Sign and verify builds before deployment.Enforce MFA, RBAC, and immutable logging on deployment pipelines.Monitor for anomalous access patterns—rate, volume, or source-based.Edge strategy: security that moves with your AIAs AI moves to the edge, centralized security breaks down. You need protection that operates as close to the data as your inference does.That’s why we at Gcore integrate protection into AI workflows from start to finish:WAAP and DDoS mitigation at edge nodes—not just centralized DCs.Encrypted transport (TLS 1.3) and in-node processing reduce exposure.Inline detection of API abuse and L7 attacks with auto-mitigation.180+ global PoPs to maintain consistency across regions.AI security is lifecycle securityNo single firewall, model tweak, or security plugin can secure AI workloads in isolation. You need defense in depth: layered, lifecycle-wide protections that work at the data layer, the API surface, and the edge.Ready to secure your AI stack from data to edge inference?Talk to our AI security experts

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