A structured set of controls for governing large language models across their lifecycle. It defines how access, data handling, execution, and monitoring should work together so AI systems are not secured piecemeal. In practice, it creates a common baseline for policy, oversight, and accountability.
Expanded Definition
An LLM security framework is a control model for governing large language models as operating systems of AI-enabled workflows, not as isolated applications. It defines how prompts, retrieval sources, tools, outputs, secrets, and human approvals are constrained so the model cannot be trusted to behave safely by default. Guidance varies across vendors, but the most mature approaches align with NIST AI Risk Management Framework principles and the OWASP Top 10 for Agentic Applications 2026 to cover misuse, leakage, and tool abuse.
In NHI security, the framework matters because LLMs frequently depend on service identities, API keys, and delegated access that can outlive the model session itself. NHI Management Group treats the framework as a policy layer that links identity controls, data controls, and execution controls into one operating standard. The most common misapplication is treating “secure prompting” as a complete framework, which occurs when teams focus on text safety but leave tool permissions, retrieval access, and secret handling ungoverned.
Examples and Use Cases
Implementing an LLM Security Framework rigorously often introduces workflow friction, requiring organisations to weigh faster model autonomy against tighter review, logging, and access constraints.
- Requiring approval before an assistant can invoke production tools, so a model cannot trigger tickets, deploy code, or query customer data without oversight.
- Limiting retrieval to approved sources and redacting secrets from prompts, response logs, and vector indexes, reducing exposure through accidental disclosure.
- Binding model access to short-lived credentials and monitored service identities, as described in the Ultimate Guide to NHIs — Regulatory and Audit Perspectives.
- Testing for prompt injection and tool hijacking using threat patterns reflected in the OWASP NHI Top 10 and the MITRE ATLAS adversarial AI threat matrix.
- Applying pre-release reviews for high-risk LLM workflows, especially where public exposure could mirror incidents discussed in the DeepSeek breach and the AI LLM hijack breach.
These use cases are increasingly discussed alongside agentic AI controls in the AI Agents: The New Attack Surface report and the NIST AI 600-1 Generative AI Profile, because the same access paths often power both chat and action.
Why It Matters in NHI Security
LLM Security Frameworks matter because a model rarely fails alone. It usually fails through a compromised identity, an over-permissioned connector, a poisoned data source, or an unreviewed action path. NHIMG research shows how quickly exposed credentials become operational risk, with attackers attempting access within an average of 17 minutes when AWS credentials are public, which makes AI systems especially sensitive to secret sprawl and delegated access abuse. The same pattern appears in the LLMjacking: How Attackers Hijack AI Using Compromised NHIs research and the LiteLLM PyPI package breach.
Framework discipline also reduces blind spots that make investigations difficult after misuse. In the AI Agents: The New Attack Surface report, 80% of organisations said their AI agents had already performed actions beyond intended scope, while only 52% could track and audit the data those agents accessed. That gap is exactly why governance cannot stop at model evaluation. Organ organisations typically encounter data exposure, unauthorized actions, or compliance failure only after an incident or audit finding, at which point an LLM Security Framework becomes operationally unavoidable to address.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10, OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF and NIST AI 600-1 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-02 | Covers secret, token, and credential exposure risks in NHI-enabled AI workflows. |
| OWASP Agentic AI Top 10 | A2 | Addresses prompt injection and unsafe tool use in agentic LLM systems. |
| NIST AI RMF | Defines risk governance practices for AI systems across lifecycle stages. | |
| NIST AI 600-1 | Extends AI RMF guidance for generative AI-specific operational risks. | |
| CSA MAESTRO | Provides threat modeling for agentic AI and tool-enabled workflows. |
Inventory and protect all model-connected secrets, then enforce rotation, scoping, and access review.
Related resources from NHI Mgmt Group
- How should security teams decide between an LLM routing layer and an orchestration framework in production AI systems?
- How should security teams use LLM-based identity risk scoring in production?
- What is the difference between AI framework guidance and runtime security controls?
- How should security teams reduce the impact of an unauthenticated RCE in a web framework?
