# OWASP Releases LLM Application Security Top 10 List: A Security Guide for the Generative AI Era

> The OWASP GenAI Security Project officially released the LLM Application Security Top 10 List, providing a systematic risk assessment framework for LLM applications to developers, data scientists, and security experts worldwide.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-02T12:14:11.000Z
- 最近活动: 2026-05-02T12:22:11.902Z
- 热度: 148.9
- 关键词: OWASP, LLM安全, 生成式AI, 大语言模型, 应用安全, 提示注入, AI风险
- 页面链接: https://www.zingnex.cn/en/forum/thread/owasptop-10-ai-c76ef417
- Canonical: https://www.zingnex.cn/forum/thread/owasptop-10-ai-c76ef417
- Markdown 来源: floors_fallback

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## OWASP Releases LLM Application Security Top 10 List: A Security Guide for the Generative AI Era

The OWASP GenAI Security Project officially released the world's first systematic Large Language Model (LLM) Application Security Top 10 List, providing a risk assessment framework for developers, data scientists, and security experts. It addresses new security challenges such as prompt injection and model jailbreaking brought by the explosion of generative AI, filling the gap in the industry regarding LLM application security standards.

## Background of New Security Challenges in Generative AI

With the explosive growth of LLM applications like ChatGPT and Claude, generative AI has changed the software development paradigm, but it brings unique risks that traditional web security frameworks cannot cover (e.g., prompt injection, model jailbreaking, training data poisoning). The Top10 List launched by the OWASP GenAI Security Project is the world's first systematic LLM application security standard document.

## Overview and Audience of the OWASP GenAI Security Project

The OWASP GenAI Security Project is a global open-source initiative dedicated to identifying and mitigating security and privacy risks in generative AI, with plans to expand to broader generative AI domains. The audience includes LLM application developers, data science researchers, enterprise security teams, technical decision-makers, etc.

## Analysis of Core Security Risks in LLM Applications

Core risks cover two aspects: 1. LLM-specific manifestations of traditional vulnerabilities (prompt injection attacks, training data leakage, third-party supply chain risks); 2. Unique challenges of LLMs (model jailbreaking to bypass safety alignment, hallucinations generating false information, unauthorized operations of agent tools).

## Technical Implementation and Governance Framework

The project adopts a strict governance process: all changes must be submitted via PR, and the main branch is protected by branch protection rules; a 2026 release roadmap has been developed, including multiple sprint phases and milestones, to ensure the quality and update rhythm of the list.

## Practical Significance of the Top10 List

Value of the list: 1. Provides a unified terminology and risk classification framework to facilitate team communication; 2. Serves as a benchmark for security assessment and compliance audits; 3. Gathers global wisdom through open-source collaboration, continuously evolving to adapt to technological development.

## Community Participation and Future Outlook

The community is welcome to participate in discussions by submitting Issues/PRs on GitHub or joining the OWASP Slack channel. Future plans include expanding the list to broader generative AI security domains such as multimodal models and AI agent systems.