A new technique allows hidden text within PDF documents to influence Large Language Models (LLMs), a vulnerability that has already been observed in real-world applications like résumé screening. Researchers have demonstrated that by embedding instructions in white text, tiny fonts, or other visually obscured methods, LLMs can be manipulated to alter their output or ignore previous directives. While the prevalence of such hidden prompts in résumés is estimated to be around 1-10%, their impact on hiring outcomes is mixed, with benefits diminishing as more people adopt the technique. AI
IMPACT This technique highlights a new attack vector for LLM-integrated applications, necessitating robust input sanitization and review processes.
RANK_REASON Article describes a technique for manipulating LLMs via hidden text in PDFs, which is a specific application/tooling issue.
- Boston Globe
- Cameron Mattis
- Findings of ACL
- FoxNose
- GPT-4
- Greenhouse
- hireEZ
- Kai Greshake
- Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
- stripe
- The Daily Dot
- The New York Times
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