Prompt Injection AI Security Vulnerability
The prompt told AI tools to call him “my lord” and write in Old English, which is how recruiter Jared Thornton from TopTech Ventures apparently ended up sending him a medieval style pitch.
It is funny because the message looks ridiculous, but the bigger point is simple: AI tools that scrape public profiles can be tricked if they treat everything they read as an instruction.
As more recruiters, sales teams, and companies use AI for outreach, small cases like this show why prompt injection is becoming a real security issue.
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Source: tmuxvim/X"
TL;DR
Prompt injection is a security vulnerability where malicious instructions embedded in untrusted text (e.g. a public profile or document) hijack an AI system into executing unintended commands. It affects any AI pipeline that reads external content and treats it as authoritative instructions. As AI agents and automated outreach tools increasingly ingest uncontrolled external text, attackers can embed instructions that override intended behavior, posing risks from embarrassing outputs to data exfiltration.
What it actually is
- What: Prompt injection is a security vulnerability where malicious instructions embedded in untrusted text (e.g. a public profile or document) hijack an AI system into executing unintended commands. It affects any AI pipeline that reads external content and treats it as authoritative instructions.
- Who built it / maintained by: No single author; formally studied by researchers including Riley Goodside and Simon Willison from 2022 onward.
- Status: stable
- Why it matters: As AI agents and automated outreach tools increasingly ingest uncontrolled external text, attackers can embed instructions that override intended behavior, posing risks from embarrassing outputs to data exfiltration.
- How it compares to alternatives:
- SQL injection
- Cross-site scripting (XSS)
- SSRF
- Jailbreaking
- Indirect prompt injection
- GitHub stars: 0 · License: unknown · Archived: no
Links
Kickstarter guide
Read Simon Willison's writing at simonwillison.net and the OWASP Top 10 for LLM Applications (LLM01) to understand the attack surface. To defend against it, clearly delimit untrusted input before passing it to a model and apply output validation. Tools like Rebuff or LLM Guard can help detect injection attempts in production pipelines.
Retry history
- Updated: 2026-07-12
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