Semantic Intent Fragmentation: A Single-Shot Compositional Attack on Multi-Agent AI Pipelines

Semantic Intent Fragmentation (SIF) targets a structural weakness in current AI safety architectures: the 'atomicity bias' of guardrails. Most current safety layers (like Llama Guard or Azure AI Content Safety) evaluate requests in isolation. SIF formalizes how to exploit multi-agent planners by tricking them into building a dangerous 'puzzle' where every individual piece is harmless. From a competitive standpoint, the project currently sits at a defensibility score of 2 because it is a fresh research artifact (0 stars, 9 days old) rather than a tool with an ecosystem. Its value is theoretical and educational. Frontier labs (OpenAI, Anthropic) face a 'medium' risk here because while they are building multi-agent supervisors, SIF represents a cat-and-mouse game where the labs must now implement 'Global Context' or 'Plan-Level' classifiers, which significantly increases latency and cost. Platform domination risk is high because the remediation for SIF—compositional safety monitoring—will likely be integrated directly into orchestration platforms like LangChain, AutoGen, or cloud-native services (AWS Bedrock Agents). Once these platforms implement a 'plan-analyzer' guardrail, the specific attack vectors described here will be mitigated. The '6-month' displacement horizon reflects the speed at which safety research is currently being productized into standard LLM firewalls.