New paper proves prompt injection impossible in current LLM architectures

A new paper published on arXiv, "On the Inseparability of Instructions and Data in Shared-Embedding Sequence Models," presents a formal proof that preventing prompt injection in current shared-embedding language models is mathematically impossible. The research introduces Prompted Action Models and defines Semantic-Faithful Control (SFC), demonstrating that SFC is unachievable due to the inseparable nature of untrusted input and control-authoritative actions within shared pipelines. This structural limitation, akin to code-data confusion in Von Neumann machines, suggests that architectural separation of instruction and data channels is necessary to mitigate prompt injection risks, rather than relying solely on in-pipeline classification or alignment techniques. AI

IMPACT Suggests a fundamental architectural shift is needed to secure LLM applications against prompt injection.

RANK_REASON Academic paper published on arXiv detailing theoretical limitations of current LLM architectures. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Hugging Face
• Prompted Action Models
• prompt injection
• Semantic-Faithful Control
• Von Neumann machines

• paper
• safety

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