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13/10/2026 10:40 am
TL;DR: PlainID’s analysis of the 2026 OWASP Top 10 for LLM Applications shows Excessive Agency rising to #3, while prompt injection remains #1 and Hidden Context Exposure broadens beyond prompt text into memory and tool context. Runtime authorization, not model-layer detection alone, is now the decisive control boundary for AI access and action.
Editorial analysis by NHI Mgmt Group, based on content published by PlainID: “How PlainID Tackles the OWASP Top 10 Vulnerabilities for LLM Applications”.
By the numbers:
- OWASP’s 2026 incident corpus contained 7,714 records, of which 6,639 were classifiable.
Key questions
Q: How should teams implement runtime authorization for LLM applications?
A: Treat runtime authorization as the enforcement point for data access, tool use, and downstream actions.
Q: Why do LLM applications need authorization before retrieval as well as before action?
A: Because retrieval can place sensitive information into model context before any output is generated, and that context can be reused in later tool calls or responses.
Q: What breaks when excessive agency is not bounded by policy?
A: The system can turn a model suggestion into a real-world action with too much authority, too much scope, or too much autonomy.
Practitioner guidance
- Enforce runtime authorization for AI actions Place deterministic policy checks in front of every retrieval, tool call, API operation, and downstream destination so a model request is not the same thing as an approved action.
- Separate policy from model context Keep entitlements, approval rules, and sensitive access logic outside prompts, memory, and hidden reasoning artifacts so exposure of context does not become exposure of authority.
- Scope agent authority by task and identity Evaluate the human user, the workload, and the acting agent together so the permitted action set is only the intersection of current task need and standing authority.
Bottom line: The article reframes LLM risk around runtime authority, not just prompt quality or model safety.
What's in the full article
PlainID's full article covers the operational detail this post intentionally leaves for the source:
- The full risk-by-risk mapping from OWASP LLM01 to LLM10, including PlainID's control boundary for each one
- The article's detailed explanation of how runtime authorization fits across RAG, APIs, microservices, and MCP servers
- PlainID's specific capability mapping for tool allowlisting, parameter controls, and step-up approval
- The evidence-weighted methodology used in the 2026 OWASP Top 10 update
👉 Read PlainID's analysis of runtime authorization for the OWASP Top 10 for LLM Applications →
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13/10/2026 10:40 am
Runtime authorization is now the primary control boundary for LLM applications. The 2026 OWASP Top 10 makes it harder to pretend that model safeguards alone are enough, because Excessive Agency sits near the top of the risk list and prompt injection is treated as a given. That shifts the governance problem from model behaviour to authorised consequence. For practitioners, the decisive question is which requests a fooled or manipulated model can still turn into real actions.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
- 43% of security professionals are concerned about AI systems learning and reproducing sensitive information patterns from codebases, according to the State of Secrets in AppSec.
A question worth separating out:
Q: What should teams do when AI agents act on behalf of real users?
A: Treat the agent as a scoped identity with explicit permissions, audit trails, and revocation rules. The human owner should be verified strongly, but the agent still needs its own access boundaries. That prevents delegated activity from becoming invisible shadow access.
👉 Read our full editorial: Runtime authorization now anchors LLM application security