Semantic Prompt Injection in Multimodal LLM Pipelines Resists Pattern-Based Defenses

As LLM systems consume images, audio, documents, and text together, attackers can embed malicious instructions across modalities that evade detection because the real threat is semantic — attacks using novel framing, narrative manipulation, or multi-turn context poisoning that no pattern-matching classifier can reliably catch. Security teams and developers deploying multimodal pipelines have no robust, generalizable defense layer for intent-based injection, only brittle heuristics that generate high false-positive rates on benign inputs. The problem grows as agentic systems with tool access make successful injection increasingly consequential.