Thanks to advances in AI, new security vulnerabilities have emerged that didn’t exist a few years ago. Just recently, a GitHub Copilot vulnerability was caused by crafted prompt injections, allowing attackers to achieve remote code execution and potentially compromise systems.
When you move from traditional AI assistants to agentic AI systems that can act autonomously, the potential impact of a single vulnerability grows even bigger.
SecureFlag’s new labs on agentic AI and Model Context Protocol (MCP) give teams the hands-on experience they need to identify and mitigate these emerging threats.
What Is Agentic AI?
Agentic AI differs from typical generative AI-based applications that simply generate replies. Instead, these systems, or “agents,” leverage large language models (LLMs) to autonomously plan, make decisions, and execute tasks without constant human input.
While such autonomy is helpful for automation and innovation, it also introduces new attack surfaces that organizations need to defend against.
Why It’s Risky
Agentic AI can be manipulated into running malicious code and overloaded to cause outages, among many other potential vulnerabilities. It requires a different security mindset; one that the labs on our training platform help establish.
Today’s AI agents also connect to external data sources and services, vastly expanding what AI can do. An example of this is MCP, launched by Anthropic last year, which provides a standardized way for AI agents to interact with different data sources and tools.
MCP simplifies connectivity, but along with it comes new security risks. Misconfigurations or outdated components can leave endpoints open or give attackers access to internal tools and sensitive data.
Interactive Learning with Realistic Attack Scenarios
SecureFlag’s labs provide realistic scenarios where the use of agentic AI and MCP can lead to serious vulnerabilities.
Here’s some of what you’ll find in these new labs:
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Injections (command, SQL, HTML): Understand how manipulated inputs can compromise systems, databases, or web interfaces through various injection attacks.
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Prompt Injections: Learn how hidden or malicious input in prompts, including indirect prompts, can manipulate AI responses or cause unintended actions.
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Token Smuggling: Discover how attackers may exploit or improperly use authentication tokens to bypass security controls and gain access to protected resources.
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Server-Side Request Forgery (SSRF): Analyze how crafted requests can cause servers to interact with internal systems or expose sensitive information.
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Credentials Mismanagement: Identify common mistakes in storing, transmitting, or using credentials that attackers can exploit.
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Misconfigured MCP Servers: Find out how improperly configured MCP servers can be abused to expose internal tools and data.
Why AI Labs Are Essential for Development Teams
AI agents, along with MCP, are changing how organizations operate, but they also call for a new perspective on security.
Traditional security testing isn’t enough to uncover the risks posed by AI systems that can act on their own and interact with complex environments.
SecureFlag’s AI labs help teams:
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Recognize and mitigate AI-specific vulnerabilities.
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Fix dangerous misconfigurations before attackers can exploit them.
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Approach AI security with an attacker’s mindset.
About SecureFlag Labs
With SecureFlag’s secure coding training, learners get to solve realistic challenges instead of memorizing theory and answering multiple-choice questions. It’s a fun and engaging way to train, rather than something that’s a chore.
Our platform covers over 50 technologies and thousands of labs, helping teams detect and resolve vulnerabilities in environments that mirror production systems.
If you’re onboarding new hires or running advanced team training, SecureFlag integrates seamlessly into workflows and builds lasting, practical skills.
