Catching prompt injections hidden in PDFs
❧ "When the going gets weird, the weird turn pro" ❦ Some months ago, I was tasked with defining what “ QA for AI applications” would look like at work. It’s been one of the most rewarding and most…
Quick read
A first-person writeup about testing a PDF-parsing agent for prompt-injection attacks and techniques to detect hidden malicious content. Someone would visit to learn practical, engineer-focused checks and defenses for catching hidden or altered text in PDFs used by AI systems.
- Detect faint hidden text by comparing pixel renders to dark-ink masks
- Map text-layer character boxes to pixels to find non-rendering characters
- Use structure tree tags (Alt, ActualText, E) to spot screen-reader only instructions
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- Yes
- AI crawlers
- Blocks OpenAI, Anthropic, Google, Common Crawl, ByteDance, Apple, Meta, Amazon, Cohere and Diffbot
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- Domain registered
- 2025
- Domain renews
- May 2027
Checked November 8, 2026 from the site’s own code and public records. See how the rest of the web compares in the NexURLs Internet Report.
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