Preventing Prompt Injection in Agentic AI
Published:Dec 29, 2025 15:54
•1 min read
•ArXiv
Analysis
This paper addresses a critical security vulnerability in agentic AI systems: multimodal prompt injection attacks. It proposes a novel framework that leverages sanitization, validation, and provenance tracking to mitigate these risks. The focus on multi-agent orchestration and the experimental validation of improved detection accuracy and reduced trust leakage are significant contributions to building trustworthy AI systems.
Key Takeaways
- •Addresses the vulnerability of multimodal prompt injection attacks in agentic AI.
- •Proposes a Cross-Agent Multimodal Provenance-Aware Defense Framework.
- •Employs text and visual sanitization, output validation, and provenance tracking.
- •Demonstrates improved detection accuracy and reduced trust leakage through experiments.
- •Contributes to the development of secure, understandable, and reliable agentic AI systems.
Reference
“The paper suggests a Cross-Agent Multimodal Provenance-Aware Defense Framework whereby all the prompts, either user-generated or produced by upstream agents, are sanitized and all the outputs generated by an LLM are verified independently before being sent to downstream nodes.”