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14/09/2026 10:09 am
TL;DR: OWASP’s 2026 Top 10 for LLM Applications is grounded in documented incidents and shows agentic risks moving up the list, with Excessive Agency rising to number three, Prompt Injection staying at number one, and Misinformation proving more operationally serious than many teams expected, according to Lasso Security. The ranking now points practitioners toward runtime inventory, constrained tool access, and sequence-level monitoring rather than prompt-only defenses.
NHIMG editorial — based on content published by Lasso Security: OWASP Top 10 for LLM Applications 2026 and the shifts it highlights
Questions worth separating out
Q: How should security teams govern AI agents that can choose tools at runtime?
A: Security teams should govern runtime agent choice as an access event, not as a simple application action.
Q: Why do prompt filters alone fail to protect LLM applications?
A: Prompt filters only inspect one visible part of the system, while the real attack path often enters through retrieved documents, tool responses, uploads, or hidden context.
Q: How can organisations tell whether an AI assistant has too much authority?
A: Look for signs that a single assistant session can access secrets, internal services, and code execution paths without a separate approval step.
Practitioner guidance
- Inventory every AI execution path Map user prompts, retrieved documents, tool outputs, uploads, and hidden context to the exact place each can influence model behaviour and action.
- Constrain agent tool permissions Limit each agent to the minimum tool scope needed for the task and require explicit approval before destructive or cross-system actions.
- Baseline runtime behaviour Record the full execution sequence, including reasoning, tool calls, and returned results, so deviations can be detected against the intended workflow.
What's in the full article
Lasso Security's full post covers the operational detail this post intentionally leaves for the source:
- The full threat-by-threat Discover-Assess-Protect breakdown for all 10 OWASP LLM application risks
- Step-level guidance on how the vendor tests prompt injection, excessive agency, and misinformation in practice
- Operational examples of runtime inventory, behavioural baselining, and execution-sequence review
- The vendor's mapping of each threat to its internal defence workflow and protection logic
👉 Read Lasso Security’s analysis of the 2026 OWASP Top 10 for LLM applications →
OWASP LLM Top 10 2026: what changed for agentic controls?
Explore further
14/09/2026 10:10 am
Agentic LLMs are already functioning like non-human identities in governance terms. Once a model can choose tools, invoke actions, and influence downstream systems, it is no longer enough to treat it as a passive application. The governance problem becomes one of delegated authority, runtime scope, and action traceability. That is where NHI thinking starts to matter, because the question is no longer only what the model knows, but what it is allowed to do.
A question worth separating out:
Q: How can organisations tell whether an AI coding model is safe enough to use?
A: Use model-specific measures rather than generic vendor claims. Track pass rates by vulnerability class, compare reasoning and non-reasoning variants, and test the model on the languages and application patterns your teams actually ship. A model is only fit for use if it improves security outcomes in your environment without reducing review discipline.
👉 Read our full editorial: OWASP LLM Applications 2026 shifts toward agentic risk
