arXiv:2607.03739v1 Announce Type: cross Abstract: We release a benchmark and failure-mode-aware evaluation framework for grounded QA under coordinated retrieval poisoning. The framework partitions reader outputs into four mutually exclusive categories (\emph{gold}, \emph{hijack}, \emph{abstention}, \emph{drift}), with instance-level paired clean-to-poison transition matrices and a Forced Exposure protocol isolating reader-side conflict resolution from retrieval variance. We introduce \emph{polymorphic sybil poisoning}, a coordinated attack class in which $S$ lexically diverse passages jointly
The rapid deployment of RAG systems in production environments necessitates robust evaluation frameworks to identify and mitigate critical vulnerabilities as AI systems become more ubiquitous.
This benchmark helps developers and organizations understand and defend against sophisticated 'polymorphic sybil poisoning' attacks that compromise the reliability and trustworthiness of AI-driven grounded QA systems.
The focus shifts from general AI security to specific, coordinated data poisoning attacks designed to manipulate RAG outputs, requiring new defensive strategies and evaluation methodologies.
- · AI security researchers
- · Organizations deploying RAG systems
- · Developers of defensive AI tools
- · Malicious actors employing poisoning techniques
- · Organizations with unhardened RAG systems
Improved resilience of RAG systems against specific data poisoning attacks for enterprise and public-facing applications.
Increased investment in proactive security measures and adversarial AI training for large language models and retrieval systems.
The development of a new competitive landscape for AI security solutions, focusing on 'trustworthy AI' as a key product differentiator.
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Read at arXiv cs.AI