arXiv:2606.17467v1 Announce Type: cross Abstract: Prompt injection defenses evaluated on synthetic benchmarks do not generalize to real enterprise documents, which are longer, denser, and interleave legitimate authority language with factual content. We demonstrate this gap with a real-document benchmark of 122 tasks across five professional domains (financial, legal, medical, scientific, DevOps) using actual SEC filings, Federal Register rules, PubMed abstracts, arXiv papers, and GitHub postmortems. Paraphrasing, the strongest defense on synthetic benchmarks, shows no statistically significan
The proliferation of LLM agents in professional domains necessitates robust defenses against prompt injection, highlighting a critical security and reliability challenge that is becoming increasingly urgent to address.
This research reveals a significant gap between synthetic prompt injection defenses and real-world enterprise documents, posing a substantial risk to the trustworthiness and deployment of sophisticated AI agents.
The understanding of effective prompt injection defenses for enterprises shifts, demanding more realistic testing benchmarks and advanced, provenance-aware solutions over simpler paraphrasing techniques.
- · AI security researchers
- · Enterprises implementing robust LLM safety
- · Developers of provenance-aware AI systems
- · Developers relying on synthetic benchmarks
- · Companies with vulnerable LLM deployments
- · Simple prompt injection defense mechanisms
Enterprise adoption of LLM agents will be bottlenecked by security and reliability concerns until these real-world vulnerabilities are mitigated.
Demand for specialized AI security and 'red-teaming' services will increase significantly as companies grapple with complex real-document threat landscapes.
The development of a new generation of 'trustworthy AI' standards and regulations specifically addressing real-world prompt injection and data provenance in professional domains may accelerate.
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Read at arXiv cs.CL