Topic starter
23/06/2026 9:15 pm
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.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “A Standardization Guide to Prompt Injection: Text-Based Techniques vs Intent”.
Key questions
Q: What breaks when prompt injection is treated as one generic risk?
A: Teams lose the ability to distinguish attacker objectives from delivery methods, so testing, triage, and remediation all become fuzzy.
Q: Why does prompt injection create risk in connected AI workflows?
A: Because connected workflows bring external data into the model’s decision surface.
Q: What are the signs that prompt filtering is failing?
A: Common signals include hidden Unicode characters, suspicious formatting, unexpected role shifts, cross-language instruction fragments, and payloads that only become harmful when split across turns.
Practitioner guidance
- Define prompt-injection classes explicitly Create an internal taxonomy that separates objectives such as prompt leakage and jailbreak from delivery techniques such as formatting, encoding, and instruction smuggling.
- Inspect prompt assembly and normalisation Review how your application strips, preserves, or transforms whitespace, Unicode variants, markup, and hidden text before the model sees it.
- Separate user data from executable instructions Treat retrieved documents, HTML, and conversation history as untrusted content unless the workflow explicitly validates them.
Bottom line: Prompt injection should be classified by both attacker intent and delivery technique, because the same objective can use many different text-based methods.
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25/06/2026 2:26 am
Prompt injection is now a governance taxonomy problem, not just a red-team tactic. The article shows that the same malicious objective can be delivered through many text-based techniques, which makes one-dimensional policy language too blunt for operational use. Security teams need a classification model that captures both attacker intent and execution method so incidents can be compared consistently and controls can be assigned to the right failure mode.
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 should teams govern prompt content that arrives through HTML or retrieved documents?
A: Treat retrieved HTML and documents as data first, not instructions. The control goal is to stop embedded directives, comments, or hidden elements from being interpreted as task logic. If the workflow cannot preserve that boundary, prompt injection can ride inside otherwise legitimate content and bypass simple content filters.
👉 Read our full editorial: Prompt injection taxonomy shows the gap between intent and technique