Topic starter
06/06/2026 2:20 am
TL;DR: The OWASP Top 10 for LLM Applications 2025 maps how prompt injection, data leakage, supply-chain tampering, and excessive agency turn language-model features into enterprise security risks, according to WorkOS. The core issue is that classical app security assumes deterministic inputs and outputs, while LLM-enabled systems break that assumption at the model, retrieval, and authorization layers.
Editorial analysis by NHI Mgmt Group, based on content published by WorkOS: “The OWASP Top 10 for LLM applications: What developers shipping AI features need to know”.
Key questions
Q: What breaks when LLM security is enforced only in the application layer?
A: Alternate routes bypass the controls.
Q: Why do LLM apps need permission-aware retrieval before generation?
A: Because once the model has seen data, the exposure has already happened.
Q: What are the signs that an LLM has been granted more agency than it should have?
A: Common warning signs include unnecessary tools exposed to the model, broad database or API permissions, support workflows that can edit or delete records, and debug features still active in production.
Practitioner guidance
- Harden retrieval at the identity layer Enforce tenant and document permissions before the model receives any context, using the requester’s identity rather than a shared service account.
- Remove security rules from prompts Move roles, access rules, and approval logic into deterministic application code so prompt leakage cannot expose the authorisation model.
- Scope tool access to the task Give each LLM tool the narrowest credentials and actions required for the job, and avoid persistent privileges that outlive the session.
Bottom line: LLM applications break classical app security assumptions because model behaviour is probabilistic and can be manipulated through both direct and indirect prompt injection.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 4 days ago by NHI Mgmt Group
06/06/2026 3:58 am
LLM security failures are trust-boundary failures, not model-quality failures: The OWASP Top 10 for LLM applications is really a map of where application teams have been outsourcing security decisions to a probabilistic system. Once a model reads content, retrieves data, or triggers tools, the boundary must be enforced by deterministic controls outside the model. The implication is that security architecture, not prompt craftsmanship, becomes the primary defence line.
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.
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap, according to the State of Secrets in AppSec.
A question worth separating out:
Q: How should teams separate LLM output handling from application trust decisions?
A: Treat the model output as untrusted input and validate it before any downstream system acts on it. That means schema checks, context-aware encoding, and blocking outputs that do not match the expected format or permission scope. The application must decide what is allowed, while the model only proposes text or structure for review.
👉 Read our full editorial: OWASP LLM top 10 shows where app security breaks down
This post was modified 4 days ago by NHI Mgmt Group