Topic starter
12/06/2026 12:09 am
TL;DR: OWASP’s updated 2025 Top 10 for LLM applications elevates prompt injection, sensitive information disclosure, supply chain exposure, and excessive agency as the most relevant risks for copilots, chatbots, and agents, according to Pomerium. Identity-aware, policy-backed access is now the control layer that determines whether prompt-driven systems can reach data or tools safely.
Editorial analysis by NHI Mgmt Group, based on content published by Pomerium: “The OWASP Top 10 for LLMs and How to Defend Against Them”.
Key questions
Q: How should security teams think about protecting LLM applications beyond simply restricting access?
A: Security teams should treat LLM protection as a control problem, not just an access problem.
Q: Why do prompt injection attacks create identity risk?
A: Prompt injection becomes an identity problem when the model can use its authorised access to query data, call tools, or trigger actions on behalf of the request.
Q: What breaks when LLMs have access to sensitive systems with static secrets?
A: Static secrets let one compromise persist across many requests, so a single prompt or workflow error can keep exposing the same systems until the secret is rotated.
Practitioner guidance
- Define route-level policy for every LLM workflow Map each model route to the exact users, groups, and services that may reach it, then deny all other calls before they hit the target system.
- Replace static credentials with short-lived access Use identity-bound, short-lived credentials for model-connected data sources so a single compromised prompt cannot reuse the same access path repeatedly.
- Separate read access from action rights Allow a model to retrieve only the data needed for the task, and require a distinct approval path for any downstream write or execution step.
Bottom line: LLM security becomes an access-control problem as soon as models can reach tools or data on behalf of a request.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 1 day ago by NHI Mgmt Group
12/06/2026 9:12 am
Identity-aware access, not prompt hygiene, is the control layer that matters for LLM security. The article is right to elevate prompt injection and sensitive information disclosure, but the real governance issue is whether the system can reach tools or data in the first place. If access is policy-backed at the edge, a malicious prompt has far less room to turn into an incident. Practitioners should treat route-level authorisation as the boundary that makes LLM deployment governable.
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.
A question worth separating out:
Q: How do teams reduce excessive agency in AI-powered workflows?
A: Teams reduce excessive agency by limiting functionality, shrinking privileges, and removing approval-free execution for high-impact actions. The safest pattern is least privilege for tools, short-lived credentials tied to the initiating identity, and downstream authorization that does not depend on the model behaving correctly.
👉 Read our full editorial: OWASP top 10 for LLMs exposes the access gap in AI security
This post was modified 1 day ago by NHI Mgmt Group