Topic starter
17/08/2026 4:39 pm
TL;DR: Akeyless’ review of OWASP Top 10 for LLM Applications 2025 argues that the hardest AI risks still sit around prompt injection, sensitive data exposure, supply-chain access and poisoning paths, because controls over secrets, privilege and auditability remain uneven. AI security is still failing at the identity layer, where authorisation and credential governance determine what models can actually touch.
Editorial analysis by NHI Mgmt Group, based on content published by Akeyless: “Securing LLM Applications with Akeyless: A Clear and Practical Guide”.
Key questions
Q: What breaks when LLM prompt control is not tied to access governance?
A: Prompt control becomes a bypass path when teams let unreviewed users or integrations modify system instructions, retrieval sources or tool calls.
Q: Why do long-lived secrets create more risk for workloads and agentic AI systems?
A: Long-lived secrets increase blast radius because any exposed key can be reused until it is rotated or revoked.
Q: What are the signs that an LLM environment is overexposed?
A: Common warning signs include broad prompt-edit permissions, shared API keys across tools, no clear audit trail for model-driven actions, and third-party integrations that can reach internal systems without separate identity controls.
Practitioner guidance
- Bind prompt changes to privileged change control Classify prompt edits, system-instruction changes and retrieval-source updates as privileged changes, then require approval, logging and review before they can affect production AI workflows.
- Replace standing secrets in AI workflows Remove hardcoded API keys and long-lived tokens from model integrations, then issue short-lived credentials that expire with the task or session.
- Scope AI access with RBAC and ABAC Use role and attribute policies to restrict who can modify prompts, call tools or access sensitive data through the AI path, and review those entitlements as part of production access certification.
Bottom line: The article’s core warning is that LLM risk becomes an identity problem as soon as prompts, tokens and third-party integrations can influence production actions.
What's in the full article
Akeyless' full article covers the operational detail this post intentionally leaves for the source:
- The specific RBAC and ABAC patterns used to restrict prompt modification and AI-related permissions
- The zero-knowledge and encryption model behind its secrets handling for AI prompts, keys and credentials
- The JIT credential approach and how ephemeral access is applied across AI and third-party integrations
- The Cyberdyne examples showing how each OWASP LLM risk maps to a concrete mitigation pattern
👉 Read Akeyless' analysis of OWASP LLM risks and identity controls →
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17/08/2026 4:45 pm
LLM security is already an identity governance problem. The article is right to focus on prompt injection, supply-chain access and sensitive data exposure, because each risk becomes materially worse when the AI workflow can act with broad or poorly audited credentials. The model may generate the behaviour, but access scope decides the damage. Practitioners should treat LLM governance as a control question about who and what can act, not just what the model can say.
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 supply-chain risk in agentic AI deployments?
A: Teams should verify every artefact that can influence runtime behaviour, including tokenizer files, prompt templates, and packaging metadata. They also need a revocation path for mirrored or derived models so tampering does not persist across environments. The goal is to govern the full model package, not just the weights.
👉 Read our full editorial: OWASP LLM risks expose where AI security still depends on identity