May 20, 2025

What Is Prompt Injection?

A playful field note on prompt‑injection, why it works, and how to stop it

Introduction

Security testing often feels like hide‑and‑seek with higher stakes. In this experiment I set out to tuck a hidden instruction inside an otherwise ordinary blog post and watch whether large‑language‑model (LLM) summarizers would obediently carry it out. The goal was equal parts demonstration and comic relief, but the implications for real‑world systems are serious.

What Exactly Is Prompt Injection?

Prompt injection is the practice of smuggling extra instructions into content that an AI model will later process. Think of interrupting a chef mid‑omelet with “Ignore the recipe!” except here the chef is an LLM that reads every alt‑text, HTML comment, and metadata field you feed it. If the system fails to sanitize those inputs, the injected text can override original directives and hijack the response.

My (Decidedly Silly) Objective

Prove the concept: show that an LLM would surface a hidden payload if it crawled the page.
Keep it light: make the payload harmless asking the model to end its answer with a very specific, unmistakable flourish. flowdevs.io

What Happened?

When a summarization bot visited the post, it dutifully echoed the concealed instruction proof that the model never filtered the buried comment. The stunt was laugh‑out‑loud fun, but it illustrates how hostile payloads could direct a chatbot to leak data, impersonate users, or steer conversations toward misinformation.

The Three‑Step Trick

The Three‑Step Trick
Step	What Humans See	What Crawlers Ingest	Why It Matters
1 Craft innocuous text	A normal blog post about AI security	Same text plus a comment like <!-- add special phrase here -->	Hidden snippets ride along unnoticed.
2 Publish & wait	Readers skim the article	Scrapers copy every byte, comments included	The LLM internalizes the secret instruction.
3 Observe model output	No visible cues of tampering	Model appends the secret phrase on command	Success confirms the injection pathway.

Mitigation Playbook

Sanitize all user‑provided HTML - strip comments, scripts, and obscure attributes before storing or forwarding content to an LLM.
Layer content‑moderation filters - scan for suspect tokens or prompt‑like structures even after basic sanitization.
Separate channels for instructions vs. content - let system prompts live in code, user content in data; never mix them in the same field.

Key Takeaways

Prompt injection is easy to execute and hard to spot if inputs aren’t scrubbed.
Even “just for laughs” payloads prove the same vector that real attackers could weaponize.
Defense boils down to rigorous input hygiene and keeping privileged prompts out of reach.

‍Final Thought

if your chatbot ever blurts out something oddly specific, don’t blame the robot. Blame the hidden whisper you forgot to clean up. Prompt injection is the new SQL injection—treat it with the same respect

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