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06/09/2026 3:44 pm
TL;DR: Direct prompt injection lets attackers manipulate LLM and AI agent behaviour by embedding malicious instructions in normal-looking input, bypassing infrastructure and authentication controls while triggering unauthorized data retrieval or tool execution, according to LEVO's analysis. The real risk is runtime execution integrity, because conventional security tools cannot reliably judge whether an agent's action was legitimate or adversarially steered.
NHIMG editorial — based on content published by LEVO: Direct prompt injection and AI runtime governance
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: How should security teams reduce prompt injection risk in AI agents?
A: Security teams should reduce prompt injection risk by constraining what enters the context window, limiting tool permissions, and separating untrusted retrieval content from privileged instructions.
Q: Why do prompt injection attacks create governance risk for AI agents?
A: Prompt injection creates governance risk because the model often sits in the control path between text input and tool execution.
Q: What are the signs that an AI agent may be vulnerable to prompt injection?
A: Look for mismatches between the prompt a system received and the actions it attempted, especially unexpected data retrieval, unusual API calls, or tool use that does not match the user's request.
Practitioner guidance
- Separate trusted instructions from untrusted input Use explicit instruction segregation so user content, retrieved content, and system policy cannot be blended into one prompt path.
- Limit delegated agent permissions Scope each AI agent to the smallest set of APIs, datasets, and workflows needed for its task.
- Add runtime policy enforcement Evaluate agent actions at execution time, not only at authentication time.
What's in the full article
LEVO's full article covers the operational detail this post intentionally leaves for the source:
- A step-by-step explanation of how direct prompt injection moves from malicious input to unauthorized tool execution.
- A fuller breakdown of why network, IAM, and API gateway controls cannot inspect model intent at runtime.
- Practical examples of runtime visibility and governance controls for AI agent execution paths.
- A vendor-specific view of how MCP Server interaction fits into prompt-injection detection and prevention.
👉 Read LEVO's analysis of direct prompt injection and AI runtime governance →
Direct prompt injection: are your AI controls keeping up?
Explore further
06/09/2026 3:45 pm
Direct prompt injection is an execution integrity problem, not a prompt hygiene problem. The attack succeeds when organisations assume model output is the only thing to control, while the real issue is whether the agent's action path can be steered by hostile input. That makes runtime governance the decisive control plane, especially where AI agents can touch enterprise systems, APIs, and sensitive data. For practitioners, the boundary to protect is not just access, but authorised execution.
A question worth separating out:
Q: What is the difference between prompt injection and indirect prompt injection?
A: Prompt injection targets the model directly through the user prompt. Indirect prompt injection hides malicious instructions inside data the model later reads from a trusted source, such as a form submission or knowledge base. Indirect attacks are more dangerous in agentic systems because the malicious content can travel through normal workflows before it is executed.
👉 Read our full editorial: Direct prompt injection exposes the AI runtime governance gap
