The OWASP Top 10 for LLM Applications is the reference list security teams use to reason about how AI applications fail. It names the risks that surface when an enterprise puts a large language model in front of sensitive data and business systems.
The 2026 edition is out, and it reads differently from the version many teams bookmarked. The risks tied to an AI agent acting, retrieving data, calling tools, and taking action, moved up the list. For CISOs and identity architects in regulated industries, the update is a good moment to check whether your controls match where the risk now sits.
OWASP Top 10 Vulnerabilities for LLM Applications 2026
Last year, we mapped policy-based access control to three risks in the OWASP Top 10 for LLM Applications, all inside the RAG pipeline. The 2026 edition covers more ground.
The 2026 edition retains the same ten broad risk families but changes the ranking, expands several scopes and renames System Prompt Leakage as Hidden Context Exposure. OWASP also introduced an evidence-weighted methodology combining practitioner voting (75%) with incident data (25%); its incident corpus contained 7,714 records, of which 6,639 were classifiable.
| 2025 | Risk | 2026 | Change and customer implication |
|---|---|---|---|
| 1 | Prompt Injection | 1 | Unchanged at #1; expanded across modalities, RAG, memory, tools and agentic execution. |
| 2 | Sensitive Information Disclosure | 2 | Unchanged; authorization before retrieval remains a primary preventive control. |
| 6 | Excessive Agency | 3 | Up three places; the most important shift for runtime authorizations because tool and action authorization are central mitigations. |
| 3 | Supply Chain | 4 | Down one; expanded focus on model and component identity, provenance and trust. |
| 4 | Data and Model Poisoning | 5 | Down one; expanded to fine-tuning subversion and compromised knowledge sources. |
| 10 | Unbounded Consumption | 6 | Up four; entitlements complement gateway quotas, budgets and availability controls. |
| 9 | Misinformation | 7 | Up two; trusted-source and approval policies reduce consequential use of unreliable output. |
| 7 | System Prompt Leakage | 8 | Renamed Hidden Context Exposure and broadened beyond prompt text to hidden reasoning, memory and tool context. |
| 8 | Vector and Embedding Weakness | 9 | Down one; security-trimmed retrieval and tenant isolation remain critical. |
| 5 | Improper Output Handling | 10 | Down five; scope now highlights unsafe AI-generated code at scale as well as downstream injection. |
How Changes in OSWAP Top 10 Strengthen the Need to Runtime Authorization?
- OWASP now places the point of consequence (an agent acting with excessive authority) near the top of the list.
- Prompt Injection is treated as an expected failure mode across content, memory, RAG and tool interactions; prevention alone is insufficient.
- Hidden Context Exposure reinforces the architectural need to keep entitlements and authorization logic outside the model context.
- The practical design goal is therefore blast-radius reduction: authorize each retrieval and action at execution time using deterministic policy.
Mapping PlainID to the OWASP Top 10 (2026)
The mapping below shows the strongest value of PlainID: converting AI security intent into deterministic, real-time authorization decisions at the point of data access and action.
LLM01:2026 Prompt Injection
Direct, indirect, multimodal, retrieved-content or persistent-memory instructions may alter an AI application's behavior. PlainID independently evaluates each requested retrieval and action, limiting the blast radius even when the model is manipulated.
PlainID capabilities: Runtime authorization; least privilege; tool and API controls; parameter restrictions; data filtering and masking; approval for high-risk actions.
Coverage boundary: PlainID does not itself detect every malicious instruction or jailbreak. Prompt inspection, content isolation and model guardrails remain complementary controls.
LLM02:2026 Sensitive Information Disclosure
PlainID prevents unauthorized data from entering model context or being returned through an AI workflow. Decisions can incorporate the requesting human, workload and acting agent.
PlainID capabilities: Fine-grained data authorization; security-trimmed RAG; row/document filtering; field/column masking; purpose, geography and classification policies.
Coverage boundary: Discovery, classification and DLP tools may create labels; PlainID uses those labels and runtime context to enforce access.
LLM03:2026 Excessive Agency
PlainID directly addresses excessive functionality, permissions and autonomy by enforcing the minimum access required for each human-agent-task combination at the moment of execution.
PlainID capabilities: Tool/function allowlisting; action- and parameter-level policy; delegation; zero standing privilege; contextual decisions; step-up and human approval.
Coverage boundary: Agent design should still minimize available extensions and autonomy. PlainID supplies the independent runtime enforcement boundary.
LLM04:2026 Supply Chain
PlainID can restrict use of unapproved or high-risk models, agents, tools, plugins and MCP servers based on registry, provenance, ownership and approval metadata.
PlainID capabilities: Approved-component policies; agent/tool registry context; environment restrictions; governed administrative access; rapid revocation.
Coverage boundary: Software composition analysis, model scanning, signing and artifact-integrity verification remain specialized supply-chain controls.
LLM05:2026 Data and Model Poisoning
PlainID can govern who or what may create, modify, approve, ingest or retrieve training, fine-tuning, memory and RAG content, reducing unauthorized manipulation of trusted sources.
PlainID capabilities: Separate read/write/ingest/approve permissions; repository and pipeline access control; source trust attributes; auditability.
Coverage boundary: PlainID does not semantically determine whether authorized content or model weights have been poisoned.
LLM06:2026 Unbounded Consumption
PlainID can restrict access to expensive models and tools by identity, agent, purpose and entitlement, and can enforce transaction constraints when usage context is available.
PlainID capabilities: Model/tool entitlements; contextual quotas and transaction policies; unauthorized-use prevention; audit telemetry.
Coverage boundary: Rate limiting, token budgets, workload scaling and denial-of-service protection belong primarily to gateways and infrastructure platforms.
LLM07:2026 Misinformation
PlainID can constrain agents to approved data sources and prevent unreliable output from automatically triggering consequential business actions.
PlainID capabilities: Trusted-source policy; provenance/approval attributes; action authorization; human review for sensitive decisions.
Coverage boundary: PlainID does not evaluate factual accuracy, hallucination, bias or model quality.
LLM08:2026 Hidden Context Exposure
PlainID keeps entitlements and authorization rules outside the probabilistic model context. Exposure of system instructions, reasoning artifacts, memory or tool context does not itself grant access or bypass policy.
PlainID capabilities: Externalized authorization; prompt/configuration and memory repository controls; privilege separation; sensitive-resource policies; audited decisions.
Coverage boundary: PlainID does not guarantee that a model will never reveal information already placed in its context; context minimization and output controls remain necessary.
LLM09:2026 Vector and Embedding Weaknesses
PlainID enforces authorization before and during retrieval so vector search is constrained to content permitted for the specific identity, agent, tenant, purpose and task.
PlainID capabilities: Security-trimmed RAG; metadata filtering; tenant isolation; document-level policy; sensitivity and purpose controls.
Coverage boundary: Vector integrity, embedding inversion detection and poisoning analysis require complementary vector/database and AI-security controls.
LLM10:2026 Improper Output Handling
Before model output or generated code is executed downstream, PlainID can authorize the resulting tool call, API operation, destination and parameters. Untrusted output does not become an authorized action.
PlainID capabilities: Downstream action authorization; API/tool controls; parameter and destination restrictions; step-up and approval.
Coverage boundary: Applications must still validate, sanitize, encode, sandbox and securely review LLM-generated content and code.
How PlainID Strengthens LLM Security
1. Authorization remains effective when the model fails A system prompt is an instruction to a probabilistic component. PlainID policy is an independently evaluated control. A prompt-injected agent may request an action, but it cannot make that action authorized.
2. The decision includes both human and non-human identity PlainID can evaluate the human user, the AI agent, the application or service, and the delegation between them. Effective authority can be limited to the intersection of what the user may do, what the agent is trusted to do, and what the current task requires.
3. Control extends beyond the front door Authentication establishes who entered the environment. PlainID continues the decision at runtime: which dataset can be queried, which records can be retrieved, which tool can be invoked, which operation is permitted, and which parameters or destinations are acceptable.
4. Policies follow the access path Centralized policies can be enforced across applications, API gateways, microservices, data platforms, RAG pipelines, MCP servers and agent frameworks. This provides a consistent authorization model without forcing every AI development team to recreate sensitive business rules.
5. Every decision is explainable and auditable PlainID records the identity chain, requested action, resource, contextual attributes, policy result and enforcement outcome. This supports security investigations, compliance evidence, policy tuning and ongoing governance.
Where runtime authorization fits the OWASP Top 10 for LLM Applications
OWASP describes how LLM applications fail. Authorization decides whether that failure turns into unauthorized access or an unwanted action.
The 2026 edition sharpens the question. With Excessive Agency at #3 and prompt injection treated as a given, teams need a clear answer to one thing: what a fooled model is allowed to reach and do.
PlainID answers that question at runtime. It maps directly to the access-and-action risks, contains the model-layer attacks it cannot detect, and names the controls it does not replace. For security and identity teams putting AI in front of regulated data, the honest boundary is what makes agentic adoption defensible.
If you want to see where authorization fits your own AI access path, from prompt to tool call to data to response, our team can walk you through it.
