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12/06/2026 9:43 pm
TL;DR: Prompt injection lets adversarial input override a generative AI model’s intended instructions, exposing data, triggering unauthorized actions, and undermining trust in GenAI systems, according to Lasso Security. The control problem is not just malicious text, but the assumption that untrusted input can be safely mixed with instructions.
NHIMG editorial — based on content published by Lasso Security: Prompt Injection: What It Is and How to Prevent It
Questions worth separating out
Q: How should security teams reduce prompt injection risk in GenAI systems?
A: They should separate instructions from data, restrict tool permissions, and log model-triggered actions.
Q: Why does prompt injection become more dangerous when a model can use tools?
A: Because the output stops being just text.
Q: What do teams get wrong about indirect prompt injection?
A: They assume external content is passive.
Practitioner guidance
- Separate instructions from data Use prompt partitioning so system instructions, policy text, and user content remain structurally distinct before the model processes them.
- Sandbox every model-triggered action Run tool calls, code execution, and database operations in isolated environments with explicit allowlists.
- Log and review model decisions Capture prompts, retrieval inputs, tool invocations, and outputs so suspicious sequences can be reconstructed during incident analysis.
What's in the full article
Lasso Security's full guide covers the operational detail this post intentionally leaves for the source:
- Step-by-step examples of direct and indirect prompt injection techniques, including role-play, obfuscation, and adversarial suffix attacks.
- Detailed defence-in-depth guidance on prompt partitioning, sanitisation, sandboxing, and continuous monitoring.
- Practical examples of how prompt injection can affect chatbots, email assistants, and GenAI-integrated business systems.
- The article's own framing on how organisations should think about safe GenAI adoption without overextending model authority.
👉 Read Lasso Security's guide to prompt injection and GenAI prevention →
Prompt injection and GenAI controls: are your safeguards enough?
Explore further
12/06/2026 11:59 pm
Prompt injection is an instruction-boundary failure, not just a content-filter problem. The model is being asked to distinguish authority inside the same input stream, which is a governance problem as much as a technical one. Once untrusted text can override system intent, the application has lost control of who or what is directing the action. Practitioners should treat prompt separation as a first-class identity control, not a prompt-tuning exercise.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- Another finding from our research shows that only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: What should organisations do first if GenAI is connected to sensitive systems?
A: Start by limiting what the model can reach, then add monitoring and response controls around every action it can trigger. A model with broad permissions becomes a control-plane risk, so security teams should reduce authority before expanding deployment scope.
👉 Read our full editorial: Prompt injection exposes the trust gap in GenAI instructions
