⚡ Quick Summary
Published by OWASP, this report sets out the 2026 edition of the OWASP Top 10 for LLM Applications. It frames the list as a guide to security failures where a model is a component of an application, rather than an autonomous actor; deployments with tools, memory and downstream consequences are directed also to the OWASP Top 10 for Agentic Applications. The ten entries are Prompt Injection, Sensitive Information Disclosure, Excessive Agency, Supply Chain, Data and Model Poisoning, Unbounded Consumption, Misinformation, Hidden Context Exposure, Vector and Embedding Weaknesses, and Improper Output Handling.
The 2026 ranking combines a practitioner vote, weighted at three quarters, with incident evidence weighted at one quarter. The project leads state that they assembled 7,714 real incidents from public vulnerability databases and an AI-harm database, and classified the 6,639 incidents with enough detail to sort. This comparison informs rank changes: Excessive Agency rises to third, Unbounded Consumption rises four places, and Improper Output Handling falls from fifth to tenth. The report provides definitions, examples, prevention measures and scenarios for each risk, then consolidates mappings to nine external frameworks and taxonomies in an appendix.
🧩 What’s Covered
The available text covers the following parts of the guide:
- 2026 methodology and changes: The opening letter explains the weighting of practitioner judgement and incident evidence, including the reported 7,714-incident corpus. The update renames System Prompt Leakage as Hidden Context Exposure, expands Prompt Injection to cross-modal attacks, and describes the boundary between LLM applications and agentic applications.
- Prompt Injection: LLM01 defines direct and indirect injection across user input, retrieved content, tool output, images, audio, video and persistent memory. It introduces delivery surface, propagation behaviour and encoding as threat-modelling axes, then recommends layered measures including deterministic mediation, least privilege, output schemas, human approval, memory-write controls and adaptive red teaming.
- Sensitive disclosure and agency: LLM02 covers training-, inference-, pipeline- and observation-time disclosure, including embeddings, logs, reasoning traces and side channels. LLM03 identifies excessive functionality, permissions and autonomy as the roots of damaging tool use, with controls such as user-scoped authorization, complete mediation, approval and circuit breakers.
- Supply chain and poisoning: LLM04 addresses third-party models, datasets, adapters, conversion workflows, provenance and licensing. LLM05 covers durable corruption across pre-training, fine-tuning, RAG, embeddings, retraining and non-weight artifacts, recommending lineage, validation, signing, version control and trigger-focused testing.
- Resource consumption: LLM06 treats availability, cost and model-cloning exposure as consequences of uncontrolled inference. Its measures extend conventional rate limits with token-aware quotas, spending caps, tool-interaction monitoring, agentic step and recursion limits, and inference-infrastructure hardening.
- Misinformation and hidden context: LLM07 distinguishes credible but false, incomplete or unsupported output from its underlying causes, proposing claim-check-act patterns and runtime verification. LLM08 treats system prompts, tool schemas and operational context as potentially discoverable, and says secrets and authorization controls must remain outside model context.
- Embeddings and outputs: LLM09 addresses similarity-search-specific failures, including cross-tenant inference, inversion, retrieval poisoning, jamming and semantic-cache poisoning. LLM10 addresses downstream execution or rendering of unvalidated output, covering shells, SQL, browsers, terminals and automatic outbound fetches.
- Framework mappings: Appendix A supplies a coverage matrix and detailed mappings for the ten risks to the OWASP Agentic Applications Top 10, OWASP GenAI Data Security, MITRE ATLAS, MITRE ATT&CK and MITRE CWE in the available portion.
💡 Why it matters?
The guide helps teams separate related but distinct failure paths: for example, an injected input is treated differently from the excessive permissions that make its impact severe, while a misleading answer is distinguished from unsafe execution of that answer. That distinction supports more targeted controls at the input, retrieval, authorization, tool, output and monitoring layers.
Its incident-informed ranking and concrete scenarios are particularly relevant to teams operating RAG, tools, agent-like workflows, shared vector stores or pay-per-token inference. The framework mappings can also assist security teams in connecting LLM application risks to existing ATT&CK, ATLAS, CWE, NIST and OWASP-oriented assurance work.
❓ What’s Missing
The guide explicitly limits its scope to risks where the model is an application component. It directs risks arising when a model becomes an actor with tools, persistent memory and downstream consequences to the separate OWASP Agentic Top 10, and LLM08 excludes generic application-security issues such as server-side log leakage and client-side bundle inspection. It also acknowledges that prompt injection is intrinsic to current generative AI and that no reliable prevention mechanism exists, so the recommended posture is to constrain consequences rather than rely on a single filter. Likewise, the supply-chain discussion states that signing demonstrates integrity and origin, not a model's safety. The supplied text does not include the document's final 42 pages.
👥 Best For
Security architects, application-security teams and AI engineers assessing LLM-integrated products, especially systems that retrieve external content, invoke tools, use persistent memory, store embeddings or pass model outputs to downstream services. It is also useful to governance and assurance teams mapping technical risks to recognised security frameworks and taxonomies.
📄 Source Details
OWASP Top 10 for LLM Applications 2026, Version 2026, identifies OWASP and names Steve Wilson as Project Lead and Rock Lambros as Co-Lead. The cover leaves the publication date as “[Publication date to be set]”; page 2 records a CC BY-SA 4.0 licence. This analysis is based on the supplied text extraction of pages 1–80 of a 122-page PDF, not the complete document.
