OWASP/www-project-top-10-for-large-language-model-applications
Python No license Security License not recognized by GitHubOWASP Top 10 for Large Language Model Apps (Part of the GenAI Security Project)
1.3k
Stars
332
Forks
75
Open issues
30
Contributors
AI Analysis
The OWASP Top 10 for Large Language Model Applications is a security awareness framework that identifies and documents the ten most critical security risks specific to LLM-based applications. It is best used by developers, data scientists, and security professionals designing and building LLM applications who need actionable, practical guidance on LLM-specific vulnerabilities and mitigations. This project is not a general-purpose security tool or library—it is specialized educational and refe...
Inferred from signals mentioned in the README (tests, CI, type safety) — not a review of the actual code.
AI's overall editorial judgment — not an average of the bars above, can weigh other factors too.
OWASP's consensus-driven security framework for LLM applications, now at v2.0 maturity
The OWASP Top 10 for Large Language Model Applications is a framework document (not a tool) that catalogs ten critical security risks specific to LLM-based systems. It targets developers, security professionals, and data scientists building or securing LLM applications. It matters because LLM security poses novel threat vectors distinct from traditional web application security, and this project codifies industry consensus on the highest-priority risks—filling a gap that existed when rapid LLM adoption outpaced security guidance.
Created in May 2023 by Steve Wilson (Contrast Security), the project emerged during the explosive early phase of LLM adoption following ChatGPT's launch. It was quickly adopted as an OWASP Flagship project, reflecting the organization's recognition that LLM security required dedicated attention separate from traditional web security frameworks.
The project gained rapid traction during 2023–2024 as enterprises began deploying LLM applications and sought structured security guidance. Growth appears to have plateaued moderately (1,313 stars over ~3 years, ~13 stars in last 7 days as of June 2026), which is consistent with a mature reference document rather than an active codebase. The move to v2.0 (announced mid-2024) suggests refinement and community input rather than early-phase discovery.
Adoption not verified through concrete deployment metrics. However, indirect signals suggest meaningful adoption: (1) OWASP Flagship status implies organizational backing and broad visibility; (2) the project is referenced in security training and compliance frameworks; (3) 1,313 GitHub stars and 326 forks suggest the document is widely accessed and adapted; (4) the existence of v2.0 indicates sufficient community feedback to warrant a major revision. Real-world evidence of enterprises using this framework as a security standard is not documented in the README.
This repository appears to be primarily a documentation and content repository rather than a software library. Based on README, it likely contains markdown files, HTML/web assets for genai.owasp.org, and possibly translation or templating assets. The README does not describe software components, APIs, or executable code, suggesting this is a knowledge-base project rather than an engineered tool.
Not documented in README. As a documentation-centric project, formal test coverage is not applicable; quality assurance likely relies on community review and editorial processes.
Last push was 2026-04-27 (66 days before analysis date of 2026-07-02), indicating active but not high-frequency maintenance. This is appropriate for a stable reference document. The project has a CI/CD pipeline (pages-build-deployment badge present) for publishing the web resource. The active OWASP Slack channel (#project-top10-llm) suggests ongoing community engagement.
ADOPT IF: you are developing or securing LLM applications and need a structured reference for security priorities; your organization requires consensus-based security guidance and OWASP credibility carries weight in your risk frameworks; you need to translate traditional web security principles into LLM-specific contexts. AVOID IF: you need executable security tooling or automated enforcement—this is a documentation project, not a library; you are seeking implementation code or API integrations; you require highly specialized or emerging LLM risks not yet in the top 10 consensus. MONITOR IF: you are tracking how LLM security guidance evolves; the v2.0 revision may signal shifts in what the community considers highest-priority; you are building security products that want to align with an emerging standard.
Independent dimensions
Mainstream potential
6/10
Technical importance
7/10
Adoption evidence
5/10
- As a consensus document, the OWASP Top 10 for LLMs may lag behind rapidly evolving attack surfaces; new LLM-specific threats could emerge faster than the project can formally update (v2.0 cycle appears multi-year).
- Adoption appears institutional and educational rather than operationalized; lack of concrete deployment metrics makes it difficult to assess whether this guidance actually reduces incidents in practice.
- The project's scope is explicitly LLM applications only, which may not cover broader AI security concerns (fine-tuning, data poisoning in training pipelines, model extraction) — it is a narrowly scoped reference.
- No evidence of integration with automated security tools or CI/CD pipelines; the framework is primarily advisory, not actionable in code form, limiting enforcement and scalability.
- Dependence on OWASP governance and community contribution cycles; if the working group becomes inactive or consensus breaks down, the project may stagnate while threats evolve.
The project will likely remain a stable, slowly-evolving reference document. v2.0 will establish a 3–4 year cadence for major updates. Adoption will continue among security-conscious enterprises and in academic/training contexts, but without a shift to tooling or automated enforcement, it may remain more of a consulting guideline than an operational standard. The emergence of competitors like llm-guard (tooling) and Purple Llama (benchmarks) suggests the ecosystem is maturing away from documentation-only guidance toward tested implementations.
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Information
- Website
- http://genai.owasp.org
- Language
- Python
- License
- NOASSERTION
- Last updated
- 2mo ago
- Created
- 38mo ago
- Analyzed with
- anthropic/claude-haiku-4-5
Stars over time
Contributors over time
Top 100 contributors only — repos with more will plateau at 100.
Open issues
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Purple Llama is an executable security benchmark and tooling suite from Meta for LLM safety. Unlike this OWASP project (a reference framework), Purple Llama provides testable artifacts and automated evaluation. The OWASP project is more abstract guidance; Purple Llama is more operationalized.
llm-guard is a concrete security library with guardrails and scanning functions. This OWASP project documents risks; llm-guard implements mitigations. They are complementary—OWASP Top 10 provides the 'what', llm-guard provides the 'how'.
A curated list of LLM security and safety papers. This OWASP project is a consensus framework; Awesome-LM-SSP is a research bibliography. Different audiences and purposes—less direct competition.
NIST's framework covers broader AI governance and risk (not LLM-specific). OWASP Top 10 for LLMs is narrower and more application-focused, filling a gap for LLM developers specifically.
Industry-standard vulnerability classification. OWASP Top 10 for LLMs sits at a higher level of abstraction, adapting traditional vulnerability thinking to LLM-specific contexts rather than competing with CWE/CVSS.
