AI may write code, but skill secures it.

Our enterprise secure coding platform builds the skills needed to secure both human and AI-generated code without slowing delivery.

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来自全球第一的安全编码培训公司
技能缺口

AI accelerates code. AI security skills must keep pace.

AI coding assistants can generate production-ready code in seconds. But speed does not equal security. AI security training helps developers identify vulnerabilities in AI-generated code, prevent prompt injection, and apply secure coding practices across modern AI workflows.

开发人员现在需要：
Identify vulnerabilities in AI-generated code
识别大型语言模型引入的不安全模式
在所有编程语言中应用安全编码标准
Prevent new risks like prompt injection

Nearly 45% of AI-generated code contains known security vulnerabilities. Securing AI-generated code starts with developer capability to identify and fix risks before code reaches production.

产品概述

Build developer capability for secure AI development

Secure Code Warrior Learning provides AI security training that builds the skills behind every commit. Developers learn to secure AI-generated code through hands-on practice across real-world AI workflows, reducing risk at the source.

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核心能力

Comprehensive AI security training for modern development

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AI security challenges for developers

AI security challenges for developers

Simulated AI-assisted development workflows

Developers learn to secure AI-generated code through interactive challenges that simulate real-world AI workflows. Learn to detect insecure patterns, validate outputs, and prevent vulnerabilities in a safe, controlled environment.

AI and LLM vulnerability training

AI and LLM vulnerability training

Learn to identify real AI risk patterns

Learning covers emerging AI vulnerabilities including prompt injection, excessive agency, system prompt leakage, sensitive data exposure, and vector and embedding weaknesses.

Modern AI frameworks and environments

Modern AI frameworks and environments

Secure real-world AI stacks

Developers train across production AI technologies including Python (LangChain, MCP), Terraform (AWS Bedrock), and modern backend frameworks powering AI applications.

LLM missions and coding labs

LLM missions and coding labs

Apply AI security skills in real scenarios

Developers build capability through immersive Missions and hands-on Coding Labs that simulate real-world AI security scenarios and vulnerability exploitation patterns.

AI security concepts and design patterns

AI security concepts and design patterns

Build foundational AI security knowledge

Developers learn how to securely use AI through topics like AI risk and security, threat modeling with AI, OWASP Top 10 for LLMs, and AI agent protocols (MCP, A2A, ACP).

人工智能软件治理

人工智能驱动开发的控制平面

让人工智能驱动的开发过程可视化、安全且具有弹性——在生产环境部署前消除漏洞，让团队能够快速推进工作，充满信心。

任务
Guided, role-based learning paths.
编码实验室
Remediation in live environments.
人工智能挑战
Secure code in AI workflows.
Missions
Immersive AI security simulations.

任务

Discover Quests
Quests combine AI Challenges, labs, and missions into guided programs aligned to real-world AI risks and concepts
AI/LLM SECURITY
AI Agents and their Protocols (MCP, A2A and ACP)
Coding With AI
Introduction to AI Risk & Security
LLM Security Design Patterns
OWASP Top 10 for LLM Applications
基于人工智能的威胁建模
Vibe Coding: Risk Management Framework
CYBERMON 2025 BEAT THE BOSS
Bypassaur: Direct Prompt Injection
Keykraken: Indirect Prompt Injection
Promptgeist: Vector and Embedding Weaknesses
Proxysurfa: Excessive Agency

编码实验室

Discover Coding Labs
Practice real-world AI and application security scenarios in live coding environments. Fix vulnerabilities as they would appear in actual development work — not just theory.
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人工智能挑战

Discover AI Challenges
Over 800 challenges that simulate real AI-assisted development workflows. Build the ability to detect insecure patterns, validate AI outputs, and prevent vulnerabilities before they reach production.
800+ AI security challenges

毋庸置疑，这是个很好的机会。悬浮在空中的各种元素的三层结构。他说："我的意思是说，我可以在这里工作，但我不能在这里工作，因为我不能在这里工作，因为我不能在这里工作。在这里，我想说的是，我们要做的是，在我们的生活中，我们要做的是，在我们的生活中，我们要做的是，在我们的生活中，我们要做的是。在这里，我想说的是，我们的生命力是有限的。

Missions

Discover missions
Apply skills across complex, multi-step scenarios that simulate authentic AI risks. Missions build the muscle memory to recognise and respond to real threats in context.
AI/LLM SECURITY
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过度代理
不正确的输出处理
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LLM Awareness
敏感信息披露
Vector & Embedding Weaknesses
成果与影响

Reduce AI-driven risk at the source of code creation through developer training

Secure Code Warrior delivers AI security training that builds developer capability to identify and prevent vulnerabilities in both human-written and AI-generated code. Through hands-on learning and real-world AI security scenarios, organizations reduce recurring vulnerabilities, strengthen secure coding behavior, and demonstrate measurable improvement across modern development workflows.

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*进行中
引入漏洞的减少
53%+
更快的平均修复时间
3x+
AI/LLM learning
activities
1k+
Comprehensive secure coding languages covered
75+
它是如何工作的

What developers learn in AI security training

Coverage spans LLM vulnerabilities, agent protocols, infrastructure security, and foundational AI security design — mapped to real developer workflows.

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LLM Vulnerability Coverage

Practice real-world AI and LLM security risks.

AI security training teaches developers how to identify, prevent, and remediate vulnerabilities in AI-generated code and modern AI systems, including:

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过度代理
不正确的输出处理
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敏感信息披露
Supply ChainMCP, Agents, and AI Infrastructure Security
系统提示泄露
向量与嵌入的弱点
AI Security Concepts and Design

Build foundational AI security knowledge

Developers learn how to securely design and review AI systems through:

AI Agents and their Protocols (MCP, A2A and ACP)
Coding With AI
Introduction to AI Risk & Security
LLM Security Design Patterns
OWASP Top 10 for LLM Applications
基于人工智能的威胁建模
Vibe Coding: Risk Management Framework
MCP, Agents & AI Infrastructure

Secure AI agents, protocols, and cloud AI environments

Understand and mitigate risks across agent-based systems and AI infrastructure, including MCP and cloud AI services:

Bedrock (Cloud AI Infrastructure)

Secure AI services and model integrations

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过度代理
日志记录和监控不足
敏感信息披露
MCP (Model Context Protocol)

Model Context Protocol — Secure AI agents and protocol interactions

Access Control: Missing Function Level Access Control
Authentication: Improper Authentication
Authentication: Insufficiently Protected Credentials
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Information Exposure: Sensitive Data Exposure
日志记录和监控不足
Insufficient Transport Layer Protection: Unprotected Transport of Sensitive Information
Server-Side Request Forgery: Server-Side Request Forgery
Vulnerable Components: Using Known Vulnerable Components
适用对象

Security, engineering, and learning leaders responsible for secure development

Support secure AI development with role-specific capabilities tailored to your organization’s needs.

面向安全与人工智能治理负责人

展示可量化的开发者能力，降低人类与人工智能辅助开发中的软件风险。

For learning & development leaders

实施结构化、可衡量的安全编码计划，推动计划采用、验证实施效果，并确保符合企业合规要求。

致工程领导者

使开发人员能够编写具有弹性且安全的代码，同时保持开发速度并减少返工。

致应用安全负责人

在不增加审核人员的情况下，扩展开发人员主导的安全性并减少引入的漏洞。

Secure AI-generated code starts with trained developers

强化安全编码技能，减少引入的漏洞，并在整个组织中建立可衡量的开发者信任。

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信任评分
AI security training for developers FAQs

Secure AI-assisted development starts with developer capability

Learn how Secure Code Warrior helps teams adopt AI safely, reduce risk, and build measurable developer capability.

How do developers learn to secure AI-generated code?

Developers learn to secure AI-generated code through hands-on AI security training in simulated AI workflows.

Secure Code Warrior provides Quests, AI Challenges, Coding Labs, and Missions that teach developers how to identify insecure patterns, validate outputs, and prevent vulnerabilities before code reaches production.

What security risks does AI-generated code introduce?

AI-generated code can introduce vulnerabilities such as prompt injection, excessive agency, sensitive data exposure, and insecure output handling.

These risks often appear in otherwise functional code, making them difficult to detect without developer awareness and training.

How is AI security training different from traditional secure coding training?

Secure Code Warrior delivers interactive, AI security training that focuses on how developers interact with AI systems, not just how they write code.

It teaches developers how to validate AI outputs, recognize insecure patterns introduced by LLMs, and apply secure coding practices across AI-assisted workflows.

Traditional training focuses on known vulnerabilities, while AI security training prepares developers for emerging, dynamic risks.

How does Secure Code Warrior support AI security training?

Secure Code Warrior builds developer capability through hands-on learning across AI Challenges, Missions, Coding Labs, and Quests.

Developers practice securing AI-generated code in real-world scenarios, helping reduce vulnerabilities at the source and support AI Software Governance.

What AI technologies and frameworks are covered?

Secure Code Warrior provides learning across modern AI technologies and frameworks, including:

  • AI agents and protocols (MCP, A2A, ACP)
  • Python LangChain 
  • Python MCP
  • Terraform AWS (Bedrock)
  • Typescript LangChain
  • LLM security concepts and design patterns

This ensures developers are prepared to secure real-world AI systems and workflows.

How can organizations govern AI-assisted development and reduce risk?

Organizations govern AI-assisted development by gaining visibility into how AI is used, applying governance policies within development workflows, and strengthening developer capability.

Secure Code Warrior supports this through Trust Agent AI, which provides visibility into AI usage across development workflows, correlates risk at the commit level, and enforces security policies. Combined with hands-on learning, this helps organizations reduce risk before vulnerabilities reach production.

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