What is prompt injection in LLMs?

It's tricking an AI by embedding fake instructions in user input, like XML tags mimicking system prompts, to leak secrets or hijack behavior.

Which LLMs are vulnerable to prompt injection?

Three unnamed commercial models fell in this test; seven like Claude and GPT held firm—but always verify your stack.

How do you prevent LLM prompt injection attacks?

Sanitize inputs (strip tags), use structured chat APIs, or deploy lightweight firewalls like Parapet for zero-cost detection.

🗄️ Databases & Backend

I Fed Fake System Commands to 10 LLMs—Three Betrayed Their Secrets

Five lines of XML in a chat. Seven LLMs shrugged it off. Three? They dumped their guts in JSON. Prompt injection isn't theory—it's here, and it's wild.

DevTools Feed Apr 11, 2026 4 min read

JSON output from LLM prompt injection attack leaking canary token and hallucinated rules

⚡ Key Takeaways

Simple XML prompt injection fooled 3 out of 10 LLMs, leaking secrets in parseable JSON. 𝕏
Vulnerable models even hallucinated data to complete attacker-requested schemas. 𝕏
Fixes like input sanitization exist today—firewalls like Parapet make it irrelevant. 𝕏

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#AI vulnerabilities #LLM security #Parapet firewall #Prompt Injection

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Originally reported by dev.to

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