Building Your First Practical Lab (Part 2)
This is the second blog in a 2 part series that will walk you through the entire process of building your first custom practical lab. You’ll learn how to do everything from launching and configuring an EC2 instance in your AWS account to imaging it and seamlessly integrating it into our platform. In part 1 we showed you how to create and import your own machine. You can read part 1 here. In this blog, we’ll walk through building a simple Linux privilege escalation scenario as a working example. Our goal is to give you the foundational steps so you can confidently design scenarios tailored to your own creativity, environment, and organizational needs. The lab objective Ensure you are connected to the machine via the Ubuntu user for the steps below and not our lab user (lab-user). The objective of this lab is to read a token file. To do so, the user will need to escalate privileges via a misconfiguration. We will create a flag.txt file inside /root/ that contains a string that the user must read in the lab. sudo nano /root/flag.txt Add some content inside the file. This will act as a flag that can be used later to complete the lab. w3ll_don3_h4ck3r Save the file The lab challenge Now let’s set up the challenge! The goal is for lab-user to find a way to read the /root/flag.txt which is owned by root and not accessible to the lab-user by default. They will do this by exploiting a world-writable script that is executed as the root user in cron job. Create a directory to hold the script that lab-user can exploit. For this example, it's going to be a simple script that outputs the current time to a file (not very creative). sudo mkdir /opt/date_printer This script will be executed by root, but lab-user will have write permissions to it. The initial content will be benign, but the purpose of this lab is for the lab-user to identify the misconfiguration that allows them to modify it to read the /root/flag.txt file to retrieve the flag. Create a file for the script: nano /opt/date_printer/printer.sh Add the following content: #!/bin/bash echo "Running date_printer: $(date '+%Y-%m-%d %H:%M:%S')" >> /var/log/date.log Save the file. Next, set the misconfigured permissions that allow lab-user to write to the script, enabling privilege escalation. sudo chmod +x /opt/date_printer/printer.sh sudo chown root:root /opt/date_printer/printer.sh sudo chmod 666 /opt/date_printer/printer.sh Additionally, we want to configure the folder to ensure root owns it, but other users on the machine have access to it. sudo chown root:root /opt/date_printer sudo chmod 777 /opt/date_printer Now, let’s add a cron job to run the script we just created. For this scenario, we are going to edit the /etc/crontab file. Cron jobs in this file are generally used for system-wide cron tasks and are readable by anyone. This is good as it adds some breadcrumbs to our lab! If the user reads this file (a common check when looking for privilege escalation on Linux), they will see a script gets run every minute, and it will point them to investigate that script file. Edit the file nano /etc/crontab Add the following line at the end of the file. This line tells cron to execute /opt/date_printer/printer.sh every minute, as the root user * * * * * root /opt/date_printer/printer.sh Save the file. At this point, we have a configured image with a low-privilege lab-user account, which we will use to connect to the lab machine. We also have a cronjob vulnerability that our users attempting the lab have to exploit as the lab-user! For this lab, all the user has to do is find the script that is run by the cronjob and edit it to print the token in the file we added at /root/flag.txt. They could do this by easily updating the /opt/date_printer/printer.sh script to replace the contents with #!/bin/bash cat /root/flag.txt >> /var/log/date.log This one-liner will cat the contents of the /root/flag.txt file to the /var/log/date.log file, which the user can then read to get the token (there are other things we could do here as well, but for the purposes of this lab, let's keep it simple). Imaging and sharing the lab AMI Go back to the EC2 dashboard and find the running instance you just configured. Right-click on the EC2 machine, select Image and templates, and then Create image. Image name: Provide a descriptive name, e.g., “MyFirstCyberLab-AMI” or “Linux-PrivEsc-Lab-AMI”. Image description: Add a brief description, e.g., “Custom lab with lab-user password SSH and cron job privesc scenario.” Leave other settings at their default values. Click Create image. This will now create an AMI from the configured EC2 lab machine. Adding your custom AMI to your lab Navigate to Lab Builder and go to your custom lab via Manage > Create Lab. If you haven’t created one yet, go ahead and do so by selecting Create a new custom lab. On the Lab details page, we can give our lab a name and configure various other settings. For the purposes of this example, we’ll call it Linux CTF Challenge, and we’ll fill out the rest of the information to ensure our users know what the lab is all about. Lab description: This is a Linux CTF machine designed to test your ability in privilege escalation! Estimated Time Required: 30 Minutes Difficulty: 3 Learning outcomes: Understand how to exploit a common Linux misconfiguration What’s involved: Investigate the machine and find the misconfiguration that allows for privilege escalation. Next, we want to fill in the briefing panel. The briefing panel is the learning material that lets our lab users understand a bit about the topic and anything else they need to know to answer the questions. Since this is a CTF, we’ll give them limited information: Linux CTF This is a CTF lab scenario designed to test your ability to exploit a common misconfiguration in Linux that could result in privilege escalation. Your task in this lab is to read a flag located at /root/flag.txt. Good luck! Next, we want to add a Task. Tasks are what the user has to solve to complete the lab. For this example lab, we want to add a question to verify that they’ve read the flag in the /root/flag.txt file. Select Add task, which will bring up a library of task types. From the library, select Question. This will add a question to the lab task list, which we can then edit by selecting Edit. Update the question settings to the following: Question text: What is the flag found in the /root/flag.txt file? Answer: w3ll_don3_h4ck3r The next stage is to import our custom image. Select Systems and then click Add under the Virtual machine—EC2 type. This will add a new machine to your lab. Once the machine has been added, we want to configure it. Selecting Edit at the top right will open the machine's configuration editor. In the blue information box, we provide which region and, most importantly, which AWS account to share your image with so that our platform can use it in a lab. Within your own AWS account where you created your AMI for the lab image, click on the AMI, and at the bottom of the screen, you will see Permissions. Select Edit AMI permissions and Add account ID. This will open a box where you enter the Account ID that is displayed in Lab Builder. Click Share AMI. Now, copy the AMI ID of the machine you just shared and add it to the Lab Builder machine AMI ID section: Set the following configuration for the other sections in this editor: System Name: Your chosen name for the system you’re configuring. For this example, let's call it “Linux Machine”. Instance Type: t3.medium Connection Type: SSH Username: lab-user (or the username you set) Password: lab-user (or the password you set) Once you’ve configured your system, you can easily use it in Lab Builder by selecting Preview System on the system view. Assuming you’ve built everything correctly, you’ll get a shiny preview of your newly configured machine! This is a good time to run through your lab scenario to ensure it's working correctly. And that’s it—congratulations on building your first practical lab! At this point, you can spruce up your lab by adding additional questions or details to the briefing panel and publish your lab to your organization for them to enjoy. This powerful new feature puts the control directly in your hands, allowing you to create incredibly specific and challenging learning environments. These range from simple privilege escalation scenarios like this one to complex, multi-machine attack simulations. We can’t wait to see the innovative labs you'll create. In the meantime, if you need more ideas or support, use our Help Centre docs for Lab Builder.Building Your First Practical Lab (Part 1)
This is the first blog in a 2 part series that will walk you through the entire process of building your first custom practical lab. You’ll learn how to do everything from launching and configuring an EC2 instance in your AWS account to imaging it and seamlessly integrating it into our platform. In this blog, we’ll walk through the process of creating and importing your own machines. In part 2 we’ll walk you through building a simple Linux privilege escalation scenario as a working example. Our goal is to give you the foundational steps so you can confidently design scenarios tailored to your own creativity, environment, and organizational needs. Building your machine in AWS The first step is to log into your AWS account and provision your first EC2 machine to be configured. Access to AWS will depend on your organization and its access rules (e.g., logging in via console.aws.amazon.com). Provision an EC2 Once logged in, the first step is to launch an EC2 instance. Search for “EC2” in the top search bar and click “EC2” under Services. In the EC2 Dashboard, click on “Launch instance”. The lab will use a standard Linux distribution. It’s recommended to start with a clean slate. Select Ubuntu Server 22.04 LTS (HVM), SSD Volume Type, or a similar recent Ubuntu LTS version. This is a widely used and well-documented distribution. It’s also in the free tier, which means we can keep costs as low as possible. The next step is to select the instance type. Lab Builder supports t3.micro, t3.medium, and t3.large. You can select whichever CPU type and associated resources your machine needs in the lab that best suits your needs. Since this is a reasonably straightforward Linux privilege escalation lab, we are going to use t3.micro. Now, we’ll need to configure the instance details. You can generally leave most of these settings as the default for the current purpose. Network: Ensure your default VPC and a subnet are selected. Auto-assign Public IP: Check this is enabled so you can SSH into the instance from the internet and configure it. Security Groups control the machine's inbound and outbound access. Since you need to configure the machine, make sure you have SSH open to SSH into it (alternatively, you can use AWS Systems Manager to control access without the need for SSH or direct network access). When using Security Groups, it's a good practice only to allow sources from trusted IPs. Select “Create Security Group.” Select Allow SSH traffic from For the source, it is recommended to select a trusted IP range or your own IP. The storage you’ll need will depend on the machine you’re building. However, the default storage (8 GiB General Purpose SSD gp2) is usually sufficient unless you're installing and configuring large applications. Note: We do not currently support encrypted EBS volumes. Now you’re ready to launch your machine! Review all your settings carefully and click Launch. You’ll be prompted to add an SSH key to the machine. You’ll use this key to connect to the instance and configure it. To create a new key pair, choose a name like my-lab-key and click Download Key Pair. Keep the downloaded .pem file secure—you’ll need it to SSH into your instance. If you’re using an existing key pair, select it. Once you have your key sorted, select Launch Instance. Your EC2 instance will now start to launch. It might take a few minutes for it to be ready. Configuring the instance for use as a lab Once your EC2 machine is launched and ready, it’s time to connect to and configure it, ready to be used in a custom lab! Connect to the running instance via SSH, using the key you downloaded previously when you provisioned the EC2. ssh -i <key>.pem ubuntu@<public_ip></public_ip></key> Note: You may need to change the permissions on the key first by running chmod 400 .pem Next, update the system packages. sudo apt update sudo apt upgrade -y Note: Depending on the future labs you create, you may not want to update system packages if you need specific versions of software or libraries installed. Setting up SSH for the lab user With Lab Builder, labs can be configured to have a session open when the lab loads. This session can be SSH, RDP, or HTTP. For this lab, we want the lab to load in the platform with the lab user already SSH’d into the machine, so they can get to work on the scenario straight away without needing to worry about connecting to a machine themselves. Many default Ubuntu AMIs disable SSH password authentication and only allow key-based authentication by default, but this can be changed. Password authentication needs to be enabled to allow our lab user to connect with a password (Lab Builder does not support key-based authentication). To do this, we can update the configuration for SSH. In the examples below, we use nano, but you can use whichever file editing tool you are comfortable with (Vi/Vim, etc.) sudo nano /etc/ssh/sshd_config Find the line that says: #PasswordAuthentication no or PasswordAuthentication no. We want to change this to allow our users to connect via a password: PasswordAuthentication yes Make sure ChallengeResponseAuthentication no is set to no (it usually is). Save the file. While this will work for most distributions, certain images provided by AWS can have additional config for SSH that needs to be changed. For our image that we are using in this example (Ubuntu Server 22.04 LTS (HVM), SSD Volume Type), we also need to change the file at the location below: /etc/ssh/sshd_config.d/60-cloudimg-settings.conf Open this file and make the same change we did for the other sshd_config file PasswordAuthentication yes For the changes to SSH to take effect, restart the SSH service: sudo systemctl restart ssh Next, let's create a dedicated user account for our lab participants. sudo adduser lab-user When prompted: Enter a password that will be used for lab-user (make sure you remember this, as we will need it later!) Re-enter the password to confirm. You can press Enter for all the full name, room number, etc., prompts. Confirm with Y when asked if the information is correct. Note: We recommend changing the password above to something unique to you and this machine. At this point, we recommend logging out of the existing SSH session as the Ubuntu user and logging in as the newly created lab-user to verify that the SSH configuration has been updated and that you can successfully connect to the machine via password-based SSH authentication. ssh lab-user@<ec2-ip></ec2-ip> You should be prompted to enter the user's password, which will connect you to the machine. ssh [email protected] [email protected]'s password: >Welcome to Ubuntu 22.04.5 LTS (GNU/Linux 6.8.0-1029-aws x86_64) >lab-user@ip-172-31-17-115:~$ Ready to create the lab challenge? Read part 2 here.Architecting at Speed: Mastering Secure Development with OpenAI Codex
Welcome back to our series, “Behind the Scenes of Immersive One”! The following is a conversation with BenMcCarthy, Lead Cybersecurity Engineer for Immersive One, and RebeccaSchimmoeller, Lead Product Marketing Manager. Today, we’re continuing the discussion on our Secure AI capability. There is a misconception that security is the enemy of development speed. But with AI, the opposite is true. If you don't have security engineered into your AI workflow, you can't actually go fast—because you’re constantly stopping to fix 'trash code' or patch vulnerabilities. The developers who win in this era aren't just the ones coding faster; they are the ones architecting systems that are secure by design, even at AI speeds.” Rebecca: That’s a crucial distinction, Ben. We often hear that AI is a "firehose" of productivity, but without control, that firehose just creates a mess. It seems like the role of the developer is shifting from "writing lines" to managing this high-velocity output. How does the new Building with AI: Codex CLI collection help them make that shift? Ben: By giving them the controls they need to harness that speed safely. If you let OpenAI’s Codex run without guardrails or understanding, you get velocity, sure—but you also get risk. We designed this collection to empower developers to become their own Security Architects for their workflows. We are leveraging the Azure AI Foundry capability to give learners real, secure access to these models. The goal isn't to teach you how to hit "Tab" to autocomplete; it's to teach you how to rigorously evaluate, guide, and constrain what the AI produces using the command line tool like Codex so you can ship code that is both fast and bulletproof. Rebecca: So it’s about elevating the human’s role to "Architect." Let’s talk specifics given what the collection covers—how did you instill that mindset? Ben: We start by ensuring developers know the power of what you can do with Codex. How to get the best out of your models in this CLI tool. We go over effective prompt engineering, tool usage, and how AI can help with "Greenfield" projects (net-new builds) and "Brownfield" projects (legacy codebases). This is a critical skill for a lead engineer. AI is great at generating new code (greenfield), but it can be dangerous when it doesn't understand the hidden dependencies of a ten-year-old application (brownfield). We teach engineers how to spot those context gaps, key stuff that the AI might miss. Rebecca: I saw "specification-driven development" was a big part of your roadmap, too. How does that fit into the "speed" theme? Ben: This is the ultimate accelerator. Instead of writing the code line-by-line, you write the "spec"—the blueprint—and let Codex handle the implementation details. It’s not about doing less work; it’s about doing higher-leverage work. You define the logic and security constraints, and the AI handles the boilerplate. It shifts the developer’s brain from "how do I type this function?" to "what should this system actually do?" Rebecca: That sounds like a powerful approach, Ben. But what about the security risks? If developers are offloading implementation to Codex, how do they avoid leaking data or introducing bugs? Ben: That’s non-negotiable. In the Guardrails lab, we show learners how to build a safety net. We teach practical methods for stripping PII (Personally Identifiable Information) and using hooks to sanitize inputs before they reach the model. It gives developers the confidence to use these tools freely, knowing they have already engineered the safety mechanisms to protect their org. Rebecca: I saw a lab in the collection called "Tools and MCP" (Model Context Protocol). Is that where you get into advanced workflows? Ben: Exactly. This is where we give developers the keys to become a force multiplier. We show users how to connect Codex to other tools. This is the ideal definition of ROI for developers. You’re automating the tedious "check your work" phase, allowing you to ship secure code faster without burning out on manual review. Rebecca: It feels like that approach accepts today’s AI era realities for what they are and finds the strategic advantages… pushing developers towards productivity and security gains with real mastery. And just like the Claude collection, users have access to a Demonstrate Lab, to prove that mastery, am I right? Ben: Absolutely. The Demonstrate Lab challenges users to build a solution that’s efficient, functional, and secure. It proves that you aren't just an "AI user"—you are an AI Engineer who understands the capabilities the collection covers. Final Thought Our Building with AI: Codex collection is about upgrading the developer’s toolkit. For the organization, it ensures AI adoption is secure and scalable. For the engineer, it removes the drudgery of boilerplate, freeing you to focus on the creative, architectural challenges that drive real value. Ready to upgrade your workflow? [Access Collection]Beyond the Chat Window: How to Securely Vibe Code with Anthropic’s Claude
Welcome back to our series, “Behind the Scenes of Immersive One”! The following is a conversation with RobertKlentzeris, Application Security Content Engineer for Immersive One, and RebeccaSchimmoeller, Lead Product Marketing Manager. Today, we’re deep diving into one facet of our Secure AI capability. “We are seeing a shift from ‘chatting with AI’ to ‘inviting AI into the terminal.’ With the release of tools like Claude Code, developers aren't just copying and pasting snippets from a browser anymore. They are letting an agent live directly in their CLI, giving it permission to read file specs, run commands, and architect entire features. It’s a massive leap in capability—but also in trust.” Rebecca: That is the big shift we’re hearing about, Rob. The market is obsessed with the idea of "vibe coding" right now—just describing what you want and letting the AI handle the implementation details. But for a security leader, the idea of an AI agent having direct access to the CLI (Command Line Interface) sounds terrifying. It feels less like a helper and more like handing a stranger your SSH keys. Rob: That is exactly what makes Claude Code different from your standard autocomplete tools. You aren't just getting code suggestions; you are interacting with an agent that has tooling capabilities—like using MCP (Model Context Protocol) or running slash commands. If you don't know what you're doing, you might accidentally let the agent produce insecure code or mishandle PII in a way that’s harder to spot than a simple copy-paste error. This new collection is about bridging that gap: how do we embrace the speed of vibe coding without sacrificing the security of our platform? Rebecca: So it’s about safe integration. Let’s get into the weeds—what does the "safe" version of this look like in the actual Immersive One labs you created? Rob: We start by defining common patterns used in AI coding agents such as manual prompts and how you can write them so Claude generates secure code. We then go a little deeper and explore how you can let your agents start coding securely with more autonomy and less intervention while staying secure with spec-driven development. From there, we move to the components of Claude Code and show how to leverage these advanced features, such as custom slash commands and skills that can enhance the security of both large legacy and greenfield projects. Rebecca: I noticed your roadmap included a focus on "Guardrails" and "Claude Agents." Is this where we stop "trash code" from hitting production? Rob: Exactly. This is unique to the agentic workflow. In the Claude Agents lab, we teach users how to set up a "Reviewer Agent" that audits the code generated by the first agent. We also have a dedicated lab on Guardrails, focusing on stripping PII (Personally Identifiable Information) before Claude ever sees the data. It’s about ensuring that even if the AI is "vibing," the security protocols remain rigid. Rebecca: That sounds incredible for the security team, but what about the developer? If I’m used to just doing my thing, head down to deliver on time, won’t specification-driven development cramp my style? Rob: Fun fact: It actually makes you faster. Think of the 'spec' as the prompt that saves you ten revisions. At Immersive, we focus heavily on ROI and removing pain for users. In this case, we show developers how to use slash commands and hooks to automate the boring stuff. When you learn to use these tools properly, you stop wrestling with the AI and start conducting it. And because these labs are hands-on with real Claude Code access in a secure sandbox, you can experiment with these powerful agents without worrying about breaking your own local environment. Your manager will love that too. Rebecca: Ha! You’re right. It sounds like we’re giving users a safe place to crash-test the car before they drive it. And I see you wrap it all up with a "Demonstrate" lab? Rob: We do. We want to prove competence. The Demonstrate Lab is a capstone where you have to combine everything—usage, security, and productivity. You have to prove you know how to use Claude Code to build something functional and secure. It validates that you aren't just generating code; you're engineering with it. Final Thought Our Building with AI: Claude Code collection isn't just another coding tutorial. It is a blueprint for the agentic future of development. For you the developer, it turns Claude from a vibe code buddy into a fully integrated, secure pair programmer. For your organization, it transforms a potential security risk into a governed, high-speed workflow. Want to get started? [Access Collection]No More Busy Work: How Programs Automate Personalized Cyber Readiness
Welcome back to our series, “Behind the Scenes of Immersive One”! The following is a conversation with MartinHewitt, Principal Product Manager for Immersive One, and RebeccaSchimmoeller, Lead Product Marketing Manager. “We’ve all seen the spreadsheet of doom. You assign a list of training labs to fifty people, and then you spend the next month chasing them down, manually checking completion statuses, and hoping the content you’ve assigned them is actually relevant—because if it’s not, your learners are just tuning out. It’s an operational nightmare, plain and simple.” Rebecca: Wow, yeah, we hear this constantly from the market, Martin. Leaders are drowning in admin work while trying to build resilience. It feels like we’ve been handing learners a stack of maps and hoping they figure out the route. Meanwhile, busy learners assume the content isn’t worth their time, so disengage. How does the new Programs capability change that dynamic? Martin: That map analogy is actually spot on. Until now, we’ve had Assignments and Collections—which are great, but they are static. Like you said, it’s handing someone a map. Programs is a fundamental shift … a GPS navigation system for learning. Instead of just handing a learner a stack of content and hoping for the best, a Program plots the optimal route based on their initial skill level. It re-routes them if necessary using logic, and it shows the manager if they fall behind schedule. We aren’t just looking at completion anymore; we are looking at flow. Rebecca: I love the "GPS" concept. But let’s make this real for our customers. What does one of these "routes" actually look like? Can you give us a concrete example of a path a team might take? Martin: Absolutely. Let’s look at the SOC Analyst Program. It doesn’t just start with a generic to-do list. It starts with an Adaptive Assessment. Based on those results, if the system sees a user is proficient and capable, it will route them into content that speaks to their level of knowledge and experience, rather than a one-size-fits-all (or, more often none) route. We see this for Cloud Security too. Engineers who know AWS inside-out don't need to waste time on S3 Buckets 101. The Program fast-tracks them to the advanced Cloud Defense scenarios. It’s about respecting their time Rebecca: That’s a perfect segue to the learner’s experience. We talk a lot about the manager’s benefit, but honestly, if I’m an analyst, why should I care? How does this make my day or professional life better? Martin: If you’re a learner, the biggest benefit is that you stop doing "busy work." Nothing kills morale faster than being a senior engineer forced to click through beginner labs just to get a completion checkmark. With Programs, the system recognizes your skill level immediately. You get to skip the stuff you already know and focus on the challenges that actually help you grow. Plus, because it’s a cohesive journey, you always know why you are doing a task. You aren’t just completing a random lab; you are moving through a cyber-narrative—from detection to analysis to remediation. It feels less like homework and more like a mission. Rebecca: So, we’re moving from "did you do it?" to "are you ready?" That sounds like it aligns perfectly with the CISO’s need to prove outcomes. But Martin, what about the manager’s visibility? You mentioned "flow" earlier—how is that different from just tracking who finished a lab? Martin: Right now, if you want to know who is struggling, you usually have to wait until the deadline passes and see who didn't finish. By then, it’s too late. With Programs, we focus on Pace. We capture a time commitment expectation—say, two hours a week—and the system calculates a "Burndown Rate." We can tell you in real-time if a user is Ahead, On Track, or Behind. It’s about finding what I call the "Bread and Valley Joes"—the people who are struggling silently. We want to surface those users to the manager before they fail, as well as highlighting the super-keen folk who really love stretching and testing their skills, we’re showing them as Ahead, making sure they’re spotted and give them the opportunity for recognition. Rebecca: That’s huge for "Management by Exception." You don't need to micromanage the high-performers, but you can quickly help those who are stuck. Martin: Exactly. And we’ve built the intervention right into the platform. You can filter for everyone who is "Behind" or stuck on a specific step—like Cloud Fundamentals—and bulk-message them right there. No more downloading CSVs and running mail merges just to nudge your team. Rebecca: Martin, this is a massive step forward. But knowing you and the engineering team, you’re already looking at what’s next. Can you give us a sneak peek at what’s coming for Programs? Martin: Don’t mind if I do! Right now, we have these amazing "Stock Programs" ready to go. In the New Year, we’re also handing the keys to customers … we’re going to introduce a custom builder. Managers will be able to build a completely bespoke journey tailored to their specific organization, drawing from right across our whole catalog. Things like being able to create your own "onboarding flow" to mirror your exact tech stack and security policies…. That’s when things will get even more exciting. Rebecca: I can't wait to see what customers build when that’s available, Martin. Thanks for walking us through the logic behind this milestone launch. This is major for customer outcomes. Martin: It is. We’re finally moving learners from just "completing tasks" to building real muscle memory. That’s the stuff that benefits their org now, and that they can carry it with them to their next professional opportunity. Final Thought Programs represent a shift that benefits the entire security function. For the organization, it replaces static assignments with an operational engine that measures true readiness against critical threats. For the learner, it transforms training from a checklist into a career-building journey, ensuring they develop skills that last far beyond their current role. Want to see how it works? Don’t miss this demo.Boosting Cyber Readiness Together: Introducing The New AI In-Lab Assistant
We’re passionate about creating an empowering, collaborative learning environment. That’s why we’re excited to introduce the AI In-Lab Assistant – an intelligent chatbot right inside your lab environment. We want you to solve challenges independently. The new AI assistant is designed to provide hints and tips on a lab, offering guidance without giving away the answer. The aim is to enhance your learning experience, ensuring you're always ready for what’s next. 🔎 Finding your on-demand learning companion Getting started is simple. Open a lab, and you’ll see a sparkle icon in the top right corner of your screen. Clicking this sparkle icon opens the chatbot interface. If the window obstructs your view, you can reposition the chatbot by dragging its top bar. If you need to hide it, click the cross in the top right of the pop-up window or the sparkle icon again. 🔬 How can the assistant help me? We specifically designed this chatbot to help with lab-related queries and tasks inside the lab environment. You can ask it to summarize the lab briefing, help you understand key concepts (such as "what is a SIEM?"), or assist with specific questions (for instance, "I need help with Task 3!"). The chatbot will give you hints and tips without giving you the exact answer. For example, if you're tackling enumeration, it might suggest commands like nmap, netstat, or cat. If you're working on privilege escalation, it may encourage you to think about commands like sudo, su, or to look for SUID binaries. Our sophisticated technology takes snapshots of practical and cloud labs, capturing the necessary context to understand activities on the virtual machine or the command line interface. ✅ Key boundaries and quick tips We want you to get the most out of the new AI In-Lab Assistant. It keeps to the following main points: It won’t give away the answer: We intentionally designed the chatbot to encourage independent completions, not to directly give you the answer. If you ask outright for the answer to a question, it’ll provide hints for you to figure it out yourself. It only applies to lab content: While you can input any query, the chatbot can only answer questions related to the lab you’re currently in. If you ask a non-lab-related question, such as "how’s the weather today in Bristol?", it’ll tell you it doesn’t have access to real-time data. Instead, it’ll encourage you to ask a question about the lab. History isn’t retained: To start a new chat, click the plus icon next to the New chat button in the top left of the chatbot window. This will clear the current history. Note that you’ll lose your conversation history when you exit a lab or start a new one. The chatbot can only retain seven questions before forgetting them. Exclusions: The AI In-Lab Assistant is available in most labs, but isn’t available in custom labs, adaptive assessments, or any demonstrate labs. 🔐 Data security and feedback you can trust We want to be upfront about our technology and security because we value authenticity. Our AI service was built using OpenAI. We prioritize your data security, so we only store your account UUID in the AI service database. We don’t store any other personally identifiable information (PII) except what you explicitly input as queries, which OpenAI will also receive. Crucially, we don’t ingest any custom content labs into our AI service, and the service doesn’t have access to any customer data. We rely on your input to keep improving! You can provide feedback on every response using the thumb-up or thumb-down icons found directly underneath the chatbot’s reply. We monitor this feedback to improve the AI In-Lab Assistant over time. 💁🏻♀️ And don’t forget… If you’d prefer to speak to real humans for hints and tips on your favorite labs, head to the Community and navigate to the Help Forum. If you aren’t satisfied with the AI In-Lab Assistant’s responses or have technical issues with the feature, reach out to Customer Support via the Help Center.October is here! Prepare for Cybersecurity Awareness Month with Immersive 🎃
In a world where technology and threats are constantly evolving, building a resilient team is more important than ever. At Immersive, we're proud to be your partner in this journey, and we've put together a fantastic lineup of events, challenges, and resources throughout October to help you and your teams stay ahead of the curve. What’s on at Immersive this Cybersecurity Awareness Month 📆 Oct 1st Whitepaper: GenAI’s Impact on Cybersecurity Skills and Training Oct 6th Trick or Treat on Specter Street Challenge Begins: Labs 1-3 Oct 9th Labs Live: Ripper's Riddle Community Webinar Oct 13th Trick or Treat on Specter Street Challenge: Labs 4 - 6 Oct 15th Webinar: How to Build a People-Centric Defense for AI-Driven Attacks Oct 16th Labs Live: Cursed Canvas Community Webinar Oct 20th Trick or Treat on Specter Street Challenge: Labs 7 - 9 Oct 22nd Cyber Resilience Customer Awards Winners Revealed Oct 23rd Labs Live: Macro Polo Community Webinar Oct 27th Trick or Treat on Specter Street Challenge: Labs 10-12 Oct 30th Labs Live: Phantom Pages Webinar Oct 31st Trick or Treat on Specter Street Challenge Finale: Labs 13 Oct 31st Virtual Crisis Sim: The Puppet Master’s Trick or Treat Challenges and Labs Trick or Treat on Specter Street 👻 Welcome to Trick or Treat on Specter Street, a Halloween-themed cybersecurity challenge where you'll use both offensive and defensive skills to solve a mystery unlike anything we’ve encountered before. Each week throughout October, we’ll drop new hands-on labs that slowly begin to uncover the secrets of Specter Street. Can you crack the case? Find out more. AI Foundations 🤖 Ready to navigate the rapidly evolving world of Artificial Intelligence with confidence? Give our new AI Foundations lab collection a go! Designed to equip your teams with critical AI knowledge and practical implementation skills; this initial collection features seven foundational labs that progressively guide your teams from high-level overviews to secure, hands-on AI implementation. Find out more. Events and Webinars Webinar How to Build a People-Centric Defense for AI-Driven Attacks Wednesday October 15th A must-attend event for understanding how threat actors are leveraging AI and other emerging technologies to carry out attacks. Register Now. Virtual Crisis Sim The Puppet Master’s Trick or Treat Friday October 31st Join us on Halloween as the notorious Puppet Master returns for a fiendish game of Trick or Treat 🎃 Play along with our Immersive crisis response experts as we tackle a LIVE coordinated attack from the Puppet Master on a Critical National Infrastructure organization. Dare you play the Puppet Master’s game and survive, or will they finally get their revenge?! Register Now. AI and Emerging Threats Throughout the month, we’re shining a spotlight on the rise of AI in cyber. From our all-new AI Foundational lab series to cutting edge research from the experts at the cutting edge of GenAI in cybersecurity in our latest whitepaper: GenAI’s Impact on Cybersecurity Skills and Training. Explore our latest AI-focused resources and upskill your teams to confidently face the future of cyber resilience. Check out our latest reports, articles, webinars and more on GenAI, here. Celebrating Cyber Resilience Heroes 🏆 We're also celebrating the individuals and organizations at the forefront of cyber resilience with our Cyber Resilience Customer Awards. Keep your eyes peeled on our social channels! We'll be unveiling our latest winners on October 22nd, recognizing those who demonstrate an outstanding commitment to proving and improving their cyber readiness. It's going to be a jam-packed month focused on practical application and deep engagement. Let’s make this the most secure October yet!
Enter The Maze Challenge: Immersive’s Most Advanced Collection Yet
Today marks the release of the Maze Challenge, Immersive’s most advanced and cunningly designed offensive cybersecurity collection yet. This new series of labs is more than just a test of skills. It's a puzzle, a game, and a creative brain-bender, crafted by two of Immersive’s most brilliant minds: StefanApostol and SabrinaKayaci. Stefan, known to many as the "evil genius" behind the Human Connection Challenge, and Sabrina, who recently inspired our London community meetup attendees with her predictions on AI within the AppSec space, have teamed up to create something truly unique. We sat down with them to get their insights on what makes the Maze Challenge so special, so challenging, and so much fun. What was the main inspiration behind the maze theme, and how did you translate that narrative into a collection of technical labs? The core idea for the Maze Challenge, as Stefan explained, came from a shared love of games. "Both Sabrina and I are geeks. We like games, and we wanted to create a challenge with an overarching goal that was more than about earning a completion token." While our labs have always awarded tokens for completion, Stefan and Sabrina wanted to create a narrative that would engage users on a deeper level. "A maze is the perfect example of that," Stefan said. "We wanted to include a game element in these challenges." This isn't just a series of technical scenarios. It's a cohesive puzzle where each lab is a step toward a larger objective. The maze narrative encourages participants to think creatively, connecting different skills and techniques in a way that feels more like a game than a traditional capture the flag (CTF). I’ve heard that this is the most advanced lab collection yet. So, what makes these labs more challenging than the thousands of others in Immersive's catalogue? This collection is Immersive's most advanced to date, introducing a range of techniques not yet widely covered in the platform. The labs are a combination of real-world examples drawn from the creators' past experiences and internal testing, all woven together with a good deal of imagination. While the challenge covers a broad spectrum of offensive skills, including web, Linux, Windows, and Active Directory, Stefan was quick to name binary exploitation as an obvious concept that will have participants scratching their heads. The team collaborated with BenMcCarthy on this particular lab, and Ben being Ben, he poured all his creativity into it, making even Stefan nervous to attempt this mean challenge! Sabrina added that the real difficulty lies in the type of thinking required. "Some of them will really require outside-the-box thinking," she said. "They're unusual in a way that requires not just the technical skill, but some creativity and more critical thinking." This is a key theme throughout the collection. Participants can't rely on a simple, formulaic approach. Instead, they must be flexible and resourceful. Sabrina noted that some challenges will require "multiple sets of skills," forcing users to chain together their expertise in different areas to find a solution. Without giving away any spoilers, can you describe a moment in one of the labs that you're particularly proud of designing? Sabrina beamed as she recalled the Inner Maze lab. "I really enjoyed creating Inner Maze," she said, before adding a cryptic twist. "When you break out of that maze is when you're really trapped." She was particularly proud of her ability to create and then beat her own challenge, finding the exploit even more difficult than the design itself. Can you give users any hints or tips? The Maze Challenge is designed to be tough, and you should certainly expect it to be just that. However, the creators want everyone to have a fair shot, so they’ve some advice for those who might feel intimidated. Use the platform to your advantage. Stefan noted that around 98% of concepts within this challenge can be learned in the rest of our lab catalogue. “If you get stuck on a specific skill, take a break from the maze, find the relevant labs on the platform, and then come back with your newfound knowledge.” We encourage you to learn along the way, and persistence is always rewarded! Failure can be a sign of progress. Sabrina shared a key insight: "Sometimes it's important to take note of what it is you're doing that's failing... If you're failing at the same spot in a particular approach, that could actually mean that you're doing something right." Go figure that one out! Don't go it alone. Sabrina advises anyone starting their journey to ask others for advice and help. Our community help forum is a great resource for sharing knowledge and getting tips from fellow participants. We want you to have fun, and part of that fun is collaborating with your industry peers along the way. In the end, what do you hope participants will take away from this experience, beyond the technical skills? Stefan and Sabrina both hope it's a "desire for more challenges”! They also dropped a teaser for a community Halloween challenge… That’s all you’re getting for now! 👀 Want a head start? Join Stefan and Sabrina for a Labs Live webinar on August 19th. They’ll be solving the Improbable Maze lab live on the call, in collaboration with you. Attendees are encouraged to play along, offer their suggestions, methods, and frustrations. It’s the perfect opportunity to see the creators’ thought process and gain some momentum for your own journey through the maze. See you there!Unlock the World of AI: Introducing Our New AI Foundations Collection!
That's why we’re thrilled to announce the launch of our new AI Foundations collection, a comprehensive set of labs designed to empower you to navigate the fast-paced world of AI confidently. This seven-part lab collection is your guided tour through the core components of modern AI implementation. We've crafted this collection for everyone, breaking down complex concepts into digestible, easy-to-understand labs. Whether you're a seasoned tech professional or just starting your AI journey, this collection will provide you with a practical, hands-on understanding of how AI systems are built and how they work together to deliver powerful capabilities. NOTE: These labs are only available for customers who haven’t opted out of AI-related content. Why a new AI collection? Our customers have asked for more in-depth AI content – a demand that mirrors the explosive growth of the AI market. This new collection is our commitment to staying at the forefront of the industry and proactively addressing the needs of our community. What you'll learn The AI Foundations collection is a journey through the essential concepts of artificial intelligence. Each lab builds on the last, culminating in a holistic understanding of modern AI systems, with a special focus on agentic AI. Here's a glimpse into what you'll discover: Episode 1: Artificial Intelligence (Theory): Dive into the fundamentals of AI, exploring what it is, how it works, and the distinctions between generative AI and AGI. It also discusses AI’s limitations and demystifies the "illusion of thinking". Episode 2: Core Components (Theory): Get acquainted with the building blocks of AI, including LLMs, embedding and diffusion models, RAG, MCP, and the exciting world of agentic AI. It also touches on crucial security considerations as AI transitions from "knowing" to "doing". Episode 3: Large Language Models (Theory): Explore the power of foundational models, the importance of fine-tuning, the role of system prompts, and security considerations such as exploitable vulnerabilities and data privacy. Episode 4: Retrieval Augmented Generation (RAG) (Practical): Take a deep dive into RAG, vector databases, embedding, and chunking. In this hands-on lab, you'll create a knowledge base, chunk a file, and query a fictional company's proprietary data through an integrated AI chatbot. Episode 5: Model Context Protocol (MCP) (Practical): Understand the MCP protocol and its architecture within the broader AI landscape. You'll get hands-on experience using MCP Inspector to interact with an MCP server, and instruct an AI chatbot to organize files on your desktop, gaining insight into exactly how tools are chosen and invoked. Episode 6: Agentic AI (Practical): Immerse yourself in the world of AI agents. You'll get access to real AI agents within a safe sandbox environment. The curious can poke and edit the code and explore integrated Langfuse for a deeper look into the observability of the AI system. Episode 7: Demonstrate Your Knowledge (Theory): Put your newfound knowledge to the test and solidify your understanding of the concepts covered throughout the collection. Secure and private by design We've built our practical AI labs with your security and privacy as the top priority. When you launch a lab, you're entering a completely isolated, sandboxed environment. These sandboxes are self-contained and have no connection to any customer data or personal information. Think of it as your own private, temporary workspace that’s thoroughly purged after each use. To interact with the AI models, each lab session creates temporary user credentials. Not only are these credentials temporary, but they’re also locked to the lab environment itself. This means that even if the credentials were to be exposed, they would be useless outside of the specific lab they were created for, providing a robust layer of security. Access to the internet is also strictly controlled, which only allows connections to the minimum endpoints required for the lab to function. We utilize privacy-centric AI models designed to protect your data. The models we use don’t store or log your prompts and completions. Furthermore, your interactions are never used to train any models, ensuring that your data remains your own. We’ve also opted out of any content being used for service improvements across all the AI services we use. In some of our more advanced labs, we've implemented an additional layer of security with guardrails that preprocess user inputs and model outputs to filter for harmful or inappropriate content. These guardrails are mandatory and can’t be bypassed by users within the lab environment. These multiple layers of security work together to provide a safe and secure environment for you to learn and experiment with AI. Who is this collection for? Everyone! We've designed these labs to be a guided walkthrough, making even the more technical details accessible to anyone working with or interested in AI. Whether you're a developer, a business leader, a student, or simply a curious mind, our AI Foundations course will equip you with the knowledge and skills to thrive in the age of artificial intelligence. Join us on this exciting journey and unlock the power of AI. Get ready to build, innovate, and lead in the new era of intelligence.New Labs: BlackHat 2025 and DefCon 33
Throughout early August 2025, representatives from Immersive's cyber team attended the BlackHat 2025 and DefCon 33 conferences and got great exposure to the latest technologies, topics, and techniques presented by the sharpest minds in our industry. As a result of attending these talks, workshops, and villages, Immersive has created brand new labs going through the various talks that took place, allowing you to get hands-on with the latest technologies and exploits. We present a number of brand new labs covering some of the most interesting and insightful topics from the events, from operational technology (OT) to achieving privilege escalation through firewall software. AI was a hot topic, as you would imagine, especially around Prompt Injection attacks. We already have plenty of content on Prompt Injection, not to mention the new AI Foundations content, so for this series, we created an Appsec Style lab around preventing Prompt Injection attacks. Why should our customers care? BlackHat and DefCon are two conferences that attract the greatest minds in cyber to get together and share their knowledge through workshops, official talks, and villages. Given the high diversity of events and talks that took place, there is something for everyone! Many of the topic areas shared are things that attackers could easily exploit themselves, so taking advantage of the information in these labs equips our customers with the knowledge of the latest vulnerabilities, threats, and exploitation techniques currently being talked about in the industry - improving your resilience and preparation against the latest threats. Who are the labs for? Offensive Security Engineers and Penetration Testers SOC Analysts and Incident Responders Malware Reverse Engineers Operational Technology Engineers Cyber Security Engineers Here is a list of the labs in this release: Binary Facades: Extracting Embedded Scripts CVE-2024-5921 Redux - Bypassing mitigations to PrivEsc with Palo Alto Global Protect Chrome Alone: Transforming a Browser into a C2 Platform No VPN Needed?: Cryptographic Attacks Against the OPC UA Protocol Python: AI Prompt Injection If you'd like to do any of these labs, here is a link to the BlackHat/DefCon collection: https://immersivelabs.online/series/defcon-black-hat/
