Prompt injection techniques vs intent: what IAM teams miss

TL;DR: Prompt injection is best standardised by separating attacker intent from execution technique, according to Lasso Security, which maps text-based attacks across instruction override, role-playing, context, formatting, cross-lingual, social engineering, encoding, payload splitting, and instruction smuggling. That distinction matters because AI-enabled workflows now need governance for how prompts are manipulated, not just what the model is asked to do.

NHIMG editorial — based on content published by Lasso Security: A Standardization Guide to Prompt Injection, text-based techniques vs intent

Questions worth separating out

Q: How should security teams classify prompt injection attempts in AI workflows?

A: Security teams should classify prompt injection by both attacker intent and the technique used to achieve it.

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 do organisations get wrong about filtering malicious prompts?

A: Many organisations focus on obvious harmful wording and miss manipulative structure.

Practitioner guidance

• Define prompt injection categories in policy Map local detection and response rules to intent, technique, and payload style so analysts can distinguish jailbreak attempts, system prompt leakage, and instruction smuggling during triage.
• Isolate trusted system instructions from user input Keep governing prompts, tool instructions, and refusal rules separate from untrusted text so attackers cannot use context manipulation to overwrite authoritative session state.
• Normalise multilingual and encoded content before inference Apply Unicode normalisation, script checks, decoding, and translation-aware inspection to reduce evasion through homoglyphs, mixed scripts, leet speak, or base encodings.

What's in the full article

Lasso Security's full article covers the operational detail this post intentionally leaves for the source:

• The complete prompt injection taxonomy with subcategories and examples for each attack style
• Detailed explanation of how text-based techniques are used to evade model safeguards in practice
• Expanded discussion of encoding, multilingual, and formatting-based manipulation patterns
• Additional context on how the taxonomy supports standardisation across AI security teams

👉 Read Lasso Security's full prompt injection taxonomy and technique breakdown →

Prompt injection techniques vs intent: what IAM teams miss?

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