{"slug": "the-owasp-top-10-for-llm-applications-2026-what-changed-and-why-it-matters", "title": "The OWASP Top 10 for LLM Applications (2026): What Changed and Why It Matters", "summary": "OWASP's 2026 Top 10 for LLM Applications keeps prompt injection at No. 1 while moving excessive agency up three places to No. 3 and unbounded consumption up four places to No. 6, reflecting the group's focus on the consequences of giving LLM applications tool access, credentials and connections to enterprise systems. The 2026 edition, published by OWASP's GenAI security project and detailed in an Aembit blog post by Director of Content Dan Kaplan, retains all 10 risk areas from 2025 but renames system prompt leakage as hidden context exposure to cover tool schemas, policy logic and workflow rules. OWASP advises security teams to assume models will sometimes fail and to enforce identity-based, least-privilege access outside the model.", "body_md": "# The OWASP Top 10 for LLM Applications (2026): What Changed and Why It Matters\n\nPublished 10/21/2026\n\n[Originally published](https://aembit.io/blog/the-owasp-top-10-for-llm-applications-2026-what-changed-and-why-it-matters/)\n**by Aembit.**\n\n**Written by**\n\n**Dan Kaplan, Director of Content at Aembit**\n\n**.**\n\n**TL;DR:** OWASP’s 2026 rankings reflect the growing consequences of giving LLM applications access to tools and enterprise systems. Prompt injection remains No. 1, while excessive agency rises to No. 3 and several categories expand to cover newer deployment risks. Security teams should assume models will sometimes fail, then enforce identity-based, least-privilege access outside the model.\n\nIt didn’t take long for large language models to move beyond the chat window. Today’s deployments increasingly give them persistent memory, tool access, credentials, and connections to systems where companies keep their most valuable data. They can retrieve files, query databases, write code, send messages, and initiate business processes.\n\nThe recently released [OWASP Top 10 for LLM Applications 2026](https://genai.owasp.org/resource/owasp-genai-llm-top-10-2026/) reflects how quickly that reach has expanded and the importance of [agentic AI security.](https://aembit.io/blog/agentic-ai-security-starter-kit-where-autonomous-systems-fail-and-how-to-defend-against-it/)\n\nOWASP’s list gives security leaders, developers, and technology buyers a shared way to identify important security failures affecting LLM applications and connect them to practical controls. It helps organizations focus engineering effort and decide how much authority an AI system should receive before it touches real operations. As LLM applications move deeper into the business, those decisions increasingly affect data exposure, fraud, service availability, regulatory obligations, and the integrity and reliability of automated work.\n\nFirst published in 2023 and [revised for 2025](https://aembit.io/blog/owasp-top-10-llm-risks-explained/), the OWASP Top 10 has evolved alongside the technology it covers. The 2026 edition marks a shift from protecting the conversation to containing the consequences. Risks involving an application’s access and operational authority gained prominence, while several categories widened to account for reasoning models, multimodal inputs, persistent memory, generated code, and the context supplied to connected AI systems.\n\nThe 2026 iteration retains all 10 risk areas from 2025, although several have been renamed or materially reframed. The important changes lie in their ranking, scope, and the boundaries OWASP now draws between related failures.\n\nExcessive agency rose from sixth to third, signaling OWASP’s increased focus on the consequences of granting models operational authority. Unbounded consumption climbed four places as a single request became capable of setting off costly model calls and tool invocations. System prompt leakage became hidden context exposure, covering tool schemas, policy logic, workflow rules, and other operational details beyond the prompt itself.\n\nPrompt injection remains No. 1. The difference now is what may be waiting on the other side of a successful attack: A manipulated chatbot might produce a bad answer. A manipulated agent could reach private data, invoke an overprivileged tool, or take an irreversible action. That widening blast radius runs through OW\n\n## The OWASP Top 10 for LLM Applications 2026 at a Glance\n\n| **2026 rank** | **Risk** | **2025 rank** | **Change** | \n| LLM01 | Prompt Injection | 1 | No change; expanded scope | \n| LLM02 | Sensitive Information Disclosure | 2 | No change; expanded scope | \n| LLM03 | Excessive Agency | 6 | Up three places | \n| LLM04 | Supply Chain | 3 | Down one place; expanded scope | \n| LLM05 | Data and Model Poisoning | 4 | Down one place; expanded scope | \n| LLM06 | Unbounded Consumption | 10 | Up four places | \n| LLM07 | Misinformation | 9 | Up two places | \n| LLM08 | Hidden Context Exposure | 7 | Renamed and broadened from System Prompt Leakage | \n| LLM09 | Vector and Embedding Weaknesses | 8 | Down one place; refined scope | \n| LLM10 | Improper Output Handling | 5 | Down five places; expanded scope | \n\n## What Is Different About the 2026 List?\n\nPrevious editions relied primarily on practitioner judgment. For 2026, OWASP compared that judgment with a corpus of 7,714 publicly documented incidents, 6,639 of which contained enough information to classify. The community vote received 75% of the final weight, with incident data accounting for the remaining 25%.\n\nThat comparison exposed gaps between what practitioners fear and what gets documented in the field. Prompt injection ranked first in the community vote but fell outside the top 10 when OWASP considered the incident record alone. In its methodology, the report describes this gap as a defense effect: Organizations invest heavily in preventing and containing prompt injection, while public incident records understate a risk that mature security teams are already spending considerable resources to manage.\n\nMisinformation showed the opposite pattern. Practitioners placed it near the bottom, while the incident record put it near the top. OWASP ultimately moved it from ninth to seventh, reflecting the growing consequences of letting plausible but incorrect output influence people and automated processes.\n\nThis mixed methodology makes the list more useful, but it does not turn the rankings into a statistical measure of likelihood. Public incident data remains incomplete, reporting practices vary, and the categories sometimes overlap. The list is best read as a prioritized threat-modeling guide rather than a universal risk score for every LLM application.\n\nOWASP also draws a sharper line between an LLM used as an application component and an agent that can use tools, retain memory, collaborate with other agents, or cause downstream effects. Once the model becomes an actor, OWASP recommends reading this list alongside its Top 10 for Agentic Applications. Most enterprise agent deployments will need both frameworks.\n\n## LLM01: Prompt Injection\n\nPrompt injection remains the leading LLM application risk because models process trusted instructions and untrusted data in the same context. A malicious instruction may come directly from a user or arrive through a document, image, tool output, or persistent memory. In MCP-connected systems, content returned by a server can enter the model’s context and influence what it does next.\n\nOWASP says prompting and filtering cannot reliably eliminate the risk on their own. Those measures may reduce exposure, but consequential operations still need authorization outside the model. Permissions should be limited to the request at hand, with human approval reserved for actions that are high-impact or difficult to reverse.\n\nOur assessment: Prompt injection is usually the entry point rather than the entire failure. The damage depends on what the application allows the manipulated model to access or change. An agent without sensitive access presents a far smaller risk than one operating with long-lived administrator credentials.\n\n## LLM02: Sensitive Information Disclosure\n\nSensitive information can escape through the model’s answer, but it can also surface in tool-call records, logs, embeddings, or other artifacts created by the application. Disclosure may involve memorized training data, live information retrieved from connected systems, or derived data produced during fine-tuning and related processes.\n\nThe broader scope draws attention to authorization failures in RAG systems. If an application retrieves information that a user should not be able to access, the model may be operating as intended. The access problem occurred before the information entered its context.\n\nOur assessment: Authentication at the application boundary is insufficient when retrieval occurs through a shared, overprivileged identity. Access should be evaluated using the agent’s identity and, when applicable, the identity and authority of the user it represents.\n\n## LLM03: Excessive Agency\n\nExcessive agency rose from sixth to third as LLM applications gained more influence over external systems. The risk appears when erroneous or manipulated model output can trigger a damaging action. It applies to any LLM-enabled workflow with privileged capabilities, even if the application is not described as an agent.\n\nOWASP traces the problem to excessive functionality, overly broad permissions, or too much autonomy. Limiting the tools available to the application reduces exposure, while narrow downstream permissions contain the damage when something goes wrong. Each action should also pass through authorization logic that does not depend on the model’s judgment.\n\nOur assessment: OWASP gets this exactly right, and we’d expect this one to keep climbing the list. A verified agent can still be overprivileged or act outside the conditions that made an earlier request acceptable. Access decisions need to account for the agent’s identity and requested action at runtime. If the agent is acting for a person, that user’s identity and authority must inform the decision as well.\n\n## LLM04: Supply Chain\n\nThe LLM supply chain includes models, datasets, adapters, repositories, and the systems used to move those components into production. The 2026 entry gives more attention to substituted weights, unsafe file formats, tampered templates, and weak artifact provenance. It also covers “slopsquatting,” in which attackers register package names invented by coding assistants.\n\nDocumentation can describe an artifact without proving that it is authentic or unchanged. Organizations need integrity verification and controlled promotion processes for model components, just as they do for software.\n\nOur assessment: Supply chain reviews should also consider what a trusted model is connected to. Artifact verification does not govern which agents may access an MCP server or external resource, while access enforcement does not establish that a model or tool is safe. Organizations need both controls.\n\n## LLM05: Data and Model Poisoning\n\nData and model poisoning changes what an AI system learns, retains, or retrieves. It can occur during training, fine-tuning, or RAG ingestion, as well as through compromised adapters and other model components. The system may continue to behave normally until a particular trigger activates the poisoned behavior.\n\nOWASP distinguishes durable poisoning from malicious instructions encountered during inference, which fall under prompt injection. Attacks that depend on the geometry of an embedding space are addressed separately under vector and embedding weaknesses.\n\nOur assessment: Provenance establishes where a model or dataset came from, but organizations must also control which workloads can modify the repositories and data stores that shape model behavior. Identity-based access can restrict those operations and record who reached the affected resource. Detecting poisoned content and restoring trusted data require separate controls.\n\n## LLM06: Unbounded Consumption\n\nUnbounded consumption rose from 10th to sixth as newer models made resource abuse more costly. Extended reasoning can consume large token budgets from a short prompt, while agentic workflows may turn one request into repeated model calls and tool activity. The category also includes denial of wallet, service disruption, and model extraction.\n\nTraditional request limits cannot measure the full cost of these operations. Effective controls need to account for the resources consumed across the workflow and stop activity when it exceeds defined limits.\n\nOur assessment: Consumption is partly an identity and attribution problem. Teams need to know which agent, user, or workload generated the activity before they can apply meaningful limits without disrupting unrelated services. The application and model platform remain responsible for token budgets and spending controls.\n\n## LLM07: Misinformation\n\nMisinformation rose from ninth to seventh because incorrect model output increasingly influences human decisions and automated actions. The problem may take the form of fabricated evidence, an inaccurate summary, or a false assessment of system state. It becomes security-relevant when that output is trusted and used.\n\nIndependent verification should reflect the potential impact of the resulting action. An incorrect draft is an inconvenience, while a false conclusion that triggers a payment or changes production infrastructure can become a security incident.\n\nOur assessment: A secure design assumes that an uncompromised model will still make mistakes. The system evaluating a proposed action should not rely on the same model to decide whether that action is safe.\n\n## LLM08: Hidden Context Exposure\n\nHidden context exposure replaces system prompt leakage and covers more than the prompt itself. Developer instructions, tool schemas, retrieved policy text, and workflow criteria may all become visible to an attacker. The disclosure matters when it reveals credentials or information that makes other attacks easier.\n\nAnything placed in the model’s context should be treated as potentially discoverable. Security controls should therefore operate independently from instructions the model can reveal or disregard.\n\nOur assessment: The new name better describes the underlying problem. A prompt can guide model behavior, but it cannot reliably enforce authorization or protect a secret placed inside the model’s context.\n\n## LLM09: Vector and Embedding Weaknesses\n\nEmbeddings support RAG, semantic caches, and vector-backed memory by converting source material into representations used for similarity search. The OWASP category covers risks that depend on that layer, including cross-tenant leakage, retrieval manipulation, and the possibility of inferring sensitive information from embeddings.\n\nAuthentication alone does not guarantee properly authorized retrieval. A shared index may expose information through search results, scores, or timing if access is evaluated only after the search occurs.\n\nOur assessment: Retrieval authorization should occur before information enters the model’s context and, where possible, within the system that understands the underlying data entitlements. Identity controls can govern access to the retrieval service, while the application or data platform enforces document-level permissions. Embeddings should be protected as potentially sensitive derived data.\n\n## LLM10: Improper Output Handling\n\nImproper output handling fell from fifth to 10th, although its scope expanded. The risk arises when model output reaches another component without the validation appropriate to its destination. Generated SQL may be passed to a database, for example, while model-produced code may reach a shell or unsafe markup may be rendered in a browser.\n\nThe 2026 version also addresses terminal control characters, automatic external resource fetching, and untrusted tool output that returns to the application. Each destination requires its own deterministic safeguards rather than trust in the model’s response.\n\nOur assessment: Its No. 10 placement does not make this a low-impact risk for a particular deployment. Any application that executes or renders model output without appropriate controls remains directly exposed.\n\n## The Larger Lesson: Design for a Model That Will Fail\n\nThe 2026 list challenges a common AI security strategy: trying to build a model that never follows a hostile instruction, reveals hidden context, hallucinates, or produces unsafe output. Defenses against those failures remain valuable, but they do not provide a dependable security boundary.\n\nA stronger architecture assumes the model will eventually fail and limits what can happen next. Credentials and security decisions remain outside the model, while each agent receives only the access required for its current task. When an agent acts on a person’s behalf, the user’s identity and authorization context should also inform the decision. Unattended agents and workflows should operate through their own narrowly scoped workload identities.\n\nAuthorization should be enforced on each request, including access to tools and resources exposed through MCP servers. High-impact or irreversible actions may require human approval when meaningful review is possible. Routine automation is better served by deterministic policy checks and appropriate limits, with a viable rollback path when automated actions go wrong.\n\nApplications must also account for the varying trust levels of retrieved content, tool responses, memory, and model output. Activity should be attributed to the identities and workflows involved, with audit records showing who acted, which policy was applied, and what decision followed.\n\nThis is where the LLM and agentic risk frameworks meet. Prompt injection may compromise a model’s instructions, while misinformation can corrupt its conclusions. Excessive agency often determines whether those failures reach sensitive data or produce a consequential action.\n\nFor enterprises, the practical dividing line is enforcement. Prompts describe desired behavior, while identity, policy, and authorization constrain what the system can actually do.\n\n## Related Reading\n\n- [The Cybersecurity Risks of Agentic AI: What Security Teams Need to Know](https://aembit.io/blog/agentic-ai-cybersecurity-risks-security-guide/)\n- [Human vs. AI Identity: Why AI Agents Are Breaking Identity](https://aembit.io/blog/human-vs-ai-identity-why-ai-agents-are-breaking-identity/)\n- [How AI Agents Are Creating a New Class of Identity Risk](https://aembit.io/blog/ai-agent-identity-security/)\n- [MCP Authentication and Authorization Patterns](https://aembit.io/blog/mcp-authentication-and-authorization-patterns/)\n\n## Frequently Asked Questions About the OWASP LLM Top 10 for 2026\n\n### What is the OWASP Top 10 for LLM Applications 2026?\n\nThe OWASP Top 10 for LLM Applications 2026 identifies the leading security risks affecting applications built with large language models. It gives security and development teams a common framework for threat modeling, control selection, and application reviews.\n\n### What changed in the 2026 OWASP LLM Top 10?\n\nThe 2026 list retains the same 10 broad risk areas but changes their order and scope. Excessive agency rose from sixth to third, unbounded consumption moved from 10th to sixth, and system prompt leakage was broadened and renamed hidden context exposure. OWASP also incorporated incident data into its ranking process for the first time.\n\n### Why does prompt injection remain the No. 1 LLM risk?\n\nPrompt injection remains No. 1 because LLMs process instructions and untrusted content within the same context. Prompting and filtering may reduce successful attacks, but they cannot reliably prevent a malicious input from influencing the model. Applications must limit what a manipulated model can access or change.\n\n### Why did excessive agency rise in the 2026 rankings?\n\nExcessive agency rose because more LLM applications can now use tools and act on external systems. When models receive broader access than their tasks require, an injected instruction or incorrect output can expose data or trigger an unauthorized action.\n\n### How should organizations use the OWASP LLM Top 10 to secure AI agents?\n\nOrganizations should use the LLM Top 10 to assess risks created by the model and its surrounding application. For agents that use tools or act across systems, OWASP recommends pairing it with the Top 10 for Agentic Applications. Security controls should verify the agent’s identity, enforce least-privilege access at runtime, and account for the user when an agent acts on someone’s behalf.\n\n#### About the Author\n\nDan Kaplan is the director of content at Aembit. Based in New York, he tells stories about agentic identity security that are meant to educate, inspire and, if he’s lucky, even entertain. Before joining Aembit, Dan held a similar role at Google Cloud, following stints at Siemplify and Trustwave, where he led content initiatives. He planted his roots in cybersecurity as a reporter and editor at SC Media.\n\n###### Unlock Cloud Security Insights\n\n*Subscribe to our newsletter for the latest expert trends and updates*\n\n###### Related Articles:\n\n###### [PQC is a Stress Test for Enterprise DNS](https://cloudsecurityalliance.org/articles/pqc-is-a-stress-test-for-enterprise-dns)\n\n**Published:** 10/19/2026\n\n###### [The Agent Kill Switch is No Longer Optional](https://cloudsecurityalliance.org/articles/the-agent-kill-switch-is-no-longer-optional)\n\n**Published:** 10/16/2026\n\n###### [AI Agents - You Can’t Govern What You Can’t Discover](https://cloudsecurityalliance.org/articles/ai-agents-you-can-t-govern-what-you-can-t-discover)\n\n**Published:** 10/16/2026", "url": "https://wpnews.pro/news/the-owasp-top-10-for-llm-applications-2026-what-changed-and-why-it-matters", "canonical_source": "https://cloudsecurityalliance.org/articles/the-owasp-top-10-for-llm-applications-2026-what-changed-and-why-it-matters", "published_at": "2026-10-08 22:29:37+00:00", "updated_at": "2026-10-21 11:40:56.652410+00:00", "lang": "en", "topics": ["ai-safety", "ai-agents", "large-language-models", "ai-policy"], "entities": ["OWASP", "Aembit", "Dan Kaplan", "OWASP Top 10 for LLM Applications 2026"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/the-owasp-top-10-for-llm-applications-2026-what-changed-and-why-it-matters", "markdown": "https://wpnews.pro/news/the-owasp-top-10-for-llm-applications-2026-what-changed-and-why-it-matters.md", "text": "https://wpnews.pro/news/the-owasp-top-10-for-llm-applications-2026-what-changed-and-why-it-matters.txt", "jsonld": "https://wpnews.pro/news/the-owasp-top-10-for-llm-applications-2026-what-changed-and-why-it-matters.jsonld"}}