arXiv:2606.26793v1 Announce Type: cross Abstract: Multimodal agentic retrieval-augmented generation (RAG) systems expand the attack surface beyond prompt injection to include text poisoning, image injection, direct-query attacks, and orchestrator-level tool manipulation. Existing red-teaming approaches are typically surface-specific and often recycle known attack templates; on text-poisoning benchmarks we measure 73-84% exact duplication. We present MIRROR, a unified cross-surface framework that performs memory-guided Monte Carlo tree search while conditioning candidate generation on retrieved
The increasing complexity and autonomy of agentic RAG systems necessitate more sophisticated red-teaming approaches to identify and mitigate novel attack vectors beyond traditional prompt injection.
As AI agents become more prevalent in critical applications, robust and comprehensive red-teaming frameworks are crucial to ensure their security and prevent exploitation and misuse, safeguarding digital infrastructure.
Current fragmented and template-reliant red-teaming methods are being replaced by unified, memory-guided approaches that can uncover cross-surface vulnerabilities and novel attack patterns in complex AI systems, particularly multimodal ones.
- · AI security researchers
- · AI red-teaming platforms
- · Organizations deploying agentic RAG
- · Malicious actors targeting AI systems
- · Vulnerable AI systems
- · Legacy AI security firms
MIRROR provides a more effective and generalized method for identifying novel attack vectors in advanced multimodal agentic RAG systems.
Improved red-teaming capabilities will lead to more secure AI deployments, reducing the risk of data breaches, system manipulation, and reputational damage for organizations using RAG.
The development of robust red-teaming frameworks could accelerate the responsible deployment of sophisticated AI agents into sensitive sectors by increasing public and institutional trust in their security.
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