The GenAI-LLM-Top10 repository is the official, community-maintained source for the OWASP Top 10 for Large Language Model Applications, serving developers and authorized security teams building defensible LLM systems.
| Tool | GenAI-Security-Project/GenAI-LLM-Top10 — official source repository for the OWASP Top 10 for Large Language Model Applications |
| Category | Security awareness documentation / risk taxonomy (Markdown, JavaScript tooling) |
| Primary Use | Referencing the 2026 LLM01–LLM10 risk taxonomy for threat modeling, secure design review, and training in LLM application development |
| Safe Use | Purely educational and documentary resource for authorized professionals: architects, developers, and security teams performing defensive design and assessment work on systems they own or are authorized to evaluate |
| Telemetry Note | Not executable software — a documentation corpus; its use leaves no runtime footprint and is observable only through repository access patterns such as git clone and citation activity |
Not every repository worth an operator's attention ships an exploit binary. GenAI-Security-Project/GenAI-LLM-Top10 is the official source repository for the OWASP Top 10 for Large Language Model Applications, maintained as a core initiative of the OWASP GenAI Security Project, and it carries OWASP flagship status. With roughly 135 stars, a CC BY-SA 4.0 license, and a DOI-archived 2026 edition, this is reference material rather than a scanner — but for anyone building, reviewing, or defending LLM-backed systems, it is the taxonomy the rest of the industry aligns against. Treat this writeup as a documentary walkthrough of what the repo actually contains and how to work it into an authorized security workflow.
The headline content is the 2026 release, published August 4, 2026, whose canonical Markdown lives in 2026/final/. The README is explicit that this edition combines community judgment with analysis of real-world incidents, updating the ordering, scope, examples, mitigations, and framework mappings across the list. That last phrase matters for practitioners: the mappings are what let you translate LLM-specific risks into the language of existing programs — control frameworks, threat model templates, and audit checklists — instead of treating generative AI as an exotic exception.
The 2026 list itself reads as follows: LLM01:2026 Prompt Injection, LLM02:2026 Sensitive Information Disclosure, LLM03:2026 Excessive Agency, LLM04:2026 Supply Chain, LLM05:2026 Data and Model Poisoning, LLM06:2026 Unbounded Consumption, LLM07:2026 Misinformation, LLM08:2026 Hidden Context Exposure, LLM09:2026 Vector and Embedding Weaknesses, and LLM10:2026 Improper Output Handling. Anyone who has run an authorized assessment against an LLM application in the past two years will recognize the top of the list immediately: prompt injection remains LLM01, which is a notable continuity signal given how much the ordering below it has shifted.
Two entries deserve particular attention because they reflect where the attack surface has actually moved. LLM08:2026 Hidden Context Exposure formalizes a class of problem that practitioners were encountering ad hoc — hidden instructions, system prompts, and concealed context leaking through model output or side channels. LLM09:2026 Vector and Embedding Weaknesses elevates retrieval infrastructure (RAG pipelines, vector stores, embedding models) to first-class risk status, acknowledging that most production LLM deployments are retrieval systems first and language models second.
The repository layout mirrors a mature publication pipeline rather than a weekend project. 2026/final/ holds the canonical published text; 2026/working/ retains historical working files and release-cycle tooling; 2025/ preserves the previous edition for anyone needing to diff the taxonomy's evolution; documentation/style/ contains editorial and branding guidance. A charter document — OWASP Top 10 for LLM Applications Charter.md — spells out mission, scope, governance, and operating principles, which is more process transparency than most security projects of this size offer.
Governance details in the README reveal a deliberately hardened contribution model. All changes must flow through a pull request; direct pushes to main are blocked by branch protection. Post-release corrections go through a dedicated release-errata.yml issue template, while broader feedback uses release-feedback.yml. The sprint plan and CONTRIBUTING.md from the 2026 cycle are retained as historical records but explicitly flagged as not the current workflow — a small but honest touch that prevents well-meaning contributors from following stale instructions.
For citation integrity, the project leans on Zenodo. A concept DOI (10.5281/zenodo.22109014) always resolves to the newest edition, while a version-specific DOI (10.5281/zenodo.22109015) pins the 2026 release. The repository ships a machine-readable CITATION.cff, which GitHub surfaces under its "Cite this repository" widget. If your assessment reports or internal standards documents cite the Top 10 — and they should — use the version DOI so your references remain stable when the 2027 edition lands.
In an authorized workflow, the practical use of this corpus is as a threat modeling checklist. When reviewing an LLM application you own or are contracted to assess, walking LLM01 through LLM10 against the architecture diagram produces a structured question set: where does untrusted input reach the model (LLM01), what does the system prompt conceal (LLM08), what tools and permissions has the agent been granted (LLM03), what happens to model output before it reaches a downstream interpreter (LLM10). None of this requires exploitation tooling; it requires reading the canonical text in 2026/final/ and applying it to your own design.
Defenders get as much value as attackers' red teams here. LLM06:2026 Unbounded Consumption is fundamentally an abuse-and-cost-control concern, and LLM04:2026 Supply Chain maps directly onto SBOM and dependency governance for model weights, plugins, and third-party API integrations. Security engineering teams can lift the 2026 entries directly into secure coding standards, design review gates, and AI governance policies, with the framework mappings doing the translation work into whatever control language the organization already speaks.
A few caveats worth noting from the metadata. The repository lists JavaScript as its language, which reflects the release-cycle tooling in 2026/working/ rather than any runtime product — do not expect a CLI or library. The license is CC BY-SA 4.0, so derivative internal guides are fine, but share-alike obligations apply if you republish adapted material. There are no GitHub topics populated and no separate license file beyond the Creative Commons badge, consistent with a documentation-first project.
To consume the material locally, the standard pattern is a plain git clone https://github.com/GenAI-Security-Project/GenAI-LLM-Top10 — after which 2026/final/ and 2025/ are fully readable offline. That is the entire installation story, and appropriately so. The value is entirely in the prose, the mappings, and the incident analysis that informed the ordering.
As a closing observation: the fact that prompt injection still occupies the top slot in 2026, three years into mainstream LLM deployment, is itself a finding. The ecosystem has not solved its most famous problem; it has instead built an increasingly elaborate retrieval and agency stack around it, which is precisely why LLM08 and LLM09 now exist as named categories. Reading this repository closely is one of the cheapest ways to calibrate your mental model of where generative AI risk actually concentrates — before your next authorized engagement forces you to learn it the hard way.
GenAI-Security-Project/GenAI-LLM-Top10.Educational analysis for authorized security professionals. Use only in controlled, authorized environments.
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