Topic starter
12/06/2026 9:44 pm
TL;DR: The OWASP Top 10 for LLM applications ranks the most common GenAI risks, from prompt injection and insecure output handling to supply chain vulnerabilities and excessive agency, and pairs each with prevention guidance, according to Lasso Security. The list matters because it turns LLM security from an abstract concern into a control checklist that IAM, security, and application teams can operationalise.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “OWASP Top 10 LLM Vulnerabilities & Security Checklist”.
Key questions
Q: What breaks when prompt injection defenses are not in place for LLMs?
A: When prompt injection defenses are weak, attacker-controlled text can override the model's intended instructions, especially when the text arrives through retrieved content or external documents.
Q: Why does excessive agency change how organisations govern GenAI?
A: Because the risk moves from what the model says to what the model can do.
Q: What do security teams get wrong about LLM supply chain risk?
A: They often focus on the core model and underweight the dependencies around it.
Practitioner guidance
- Map the full GenAI trust boundary Document every point where prompts, retrieval data, plugins, and outputs can change system behaviour.
- Constrain delegated model actions Separate suggestion from execution for refunds, approvals, account changes, publishing, and other high-impact functions.
- Inventory third-party dependencies List every external model integration, plugin, data source, and retrieval service, then review what data and authority each one can influence.
Bottom line: LLM security failures often begin outside the model itself, in prompts, outputs, dependencies, and delegated actions.
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This topic was modified 2 days ago by NHI Mgmt Group
13/06/2026 12:00 am
OWASP's LLM Top 10 makes one thing clear: GenAI security is a boundary problem, not a model problem. Prompt injection, output misuse, third-party exposure, and excessive agency all sit at different points in the same workflow. That means teams that only assess prompts or only assess the model are testing the wrong layer. The practical consequence is that GenAI governance has to follow the request, response, and action chain end to end.
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.
- 92% of organisations expose NHIs to third parties, raising concerns about supply chain security, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: How should organisations govern model output before it becomes an action?
A: They should require policy checks and approval gates wherever model output can change records, trigger transactions, or publish content. The key is to keep generation separate from execution so the model cannot directly exercise high-impact authority. That design reduces the chance that a single bad response becomes an operational incident.
👉 Read our full editorial: OWASP LLM Top 10 clarifies the security gaps in GenAI apps
This post was modified 2 days ago by NHI Mgmt Group