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13.10.25

Prompt Injection: a simple explanation for busy people

Prompt Injection: Plain-English guide 👇

A prompt injection is when someone sneaks instructions into text that an AI model reads - causing the model to ignore its original rules and do something it shouldn’t. Think of it like a cleverly worded detour sign that makes the AI takes a wrong turn.
 (NJP 2025)

What exactly is “prompt injection”?

Prompt injection is a tactic where attackers craft input (a message, a web page, a PDF or other documents, even hidden text) that overrides the AI’s intended behavior. The model then leaks data, executes unintended actions, or produces misleading output because it treats the injected text as higher-priority instructions. This can happen with direct prompts the user types or indirect prompts buried in external content the AI ingests.

Why should an organization care?

  • Data exposure: AI may reveal confidential info (PII, system prompts, credentials, source content). 

  • Unauthorized actions: If the AI can call tools/APIs, injected prompts may trigger emails, file operations, or risky workflow steps. 

  • Brand & compliance risk: Hallucinated or manipulated outputs can misinform customers, violate policies, or create audit findings. 

  • Supply-chain knock-on effects: Compromised plugins, connectors, or data sources can propagate malicious instructions into multiple apps.

What’s the risk to an individual user?

  • Privacy loss: Attackers can trick the model into recalling prior chat content or personal details the user provided. 

  • Fraud & social engineering: Poisoned outputs can steer users to phishing links or bad decisions that appear “AI-approved.” 

  • Reputation & errors: A junior analyst copying AI output into email or code can spread falsehoods or vulnerable snippets. 


What typically causes prompt injections?

  1. Trusting user text as instructions (no separation between “data” and “directives”).

  2. Indirect prompt sources like websites, PDFs, knowledge bases, and tickets that the AI reads automatically.

  3. Insufficient output handling (treating model text as safe to render, click, or execute). 

  4. Over-privileged tool access (the AI can perform powerful actions with little control). 

Fastest ways to reduce the risk (do these first)

For product owners / platform teams

  • Partition “instructions” from “data.” Use strict system prompts and message roles; never let external content change the AI’s core rules. 

  • Guard RAG & browsing.

    • Allow-list trusted domains and repositories.

    • Strip or neutralize markup, hidden text, and “system-like” phrases before retrieval.

    • Summarize sources rather than pasting raw content into the prompt. 

  • Validate model output before acting. Treat AI text as untrusted: sanitize, escape, and require human or policy checks before any action (click, execute, send, write to DB). 

  • Least privilege for tools/APIs. Scope tokens, rate-limit, add transaction guards (“are you sure?”), and require approvals for sensitive actions. 

  • Detection & monitoring. Log prompts/outputs, flag patterns (e.g., “ignore previous instructions”), and red-team with known injection strings during CI/CD. 

For security & governance

  • Adopt OWASP LLM Top 10 controls. Map your AI apps to LLM01 (Prompt Injection) and related risks (e.g., Sensitive Information Disclosure), then document mitigations. 

  • Policy & training. Publish short usage rules: do not paste secrets, verify links, and never execute code solely because the AI suggested it. 

For end users (fast hygiene wins)

  • Don’t paste sensitive data unless it’s explicitly approved.

  • Be skeptical of outputs that urge urgency, secrecy, or “ignore previous instructions.”

  • Confirm critical steps (money, credentials, production changes) with a second channel or a human. 


A simple mental model for juniors

  • Data is not instructions. Anything the AI reads might try to boss it around.

  • AI output is not truth. Treat it like a smart intern’s draft review before you act.

  • Power needs brakes. The more tools the AI can use, the more guardrails you must add. 


The Bottom line

Prompt injection is LLM risk No. 1 because it exploits the very thing that makes AI useful its responsiveness to natural language. Start by separating instructions from data, treating AI output as untrusted, locking down tool access, and adopting OWASP LLM Top 10 controls. These steps deliver the fastest, most meaningful drop in risk for both organizations and individual users. 



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FAQ

  • Is this the same as “jailbreaking”?
    Related but different: jailbreaking tries to bypass safety rules via user prompts; prompt injection also includes hidden or indirect instructions from external content. 

  • Can prompt injections be invisible?
    Yes. They can be embedded in code comments, HTML, PDFs, or metadata that humans might not notice - but the model parses. 


Sources used:

  1. OWASP GenAI Security Project LLM01: Prompt Injection and LLM Top 10 (2023–2025). 
  2. Palo Alto Networks Cyberpedia: What Is a Prompt Injection Attack? and What Is AI Prompt Security?