One Pic is All it Takes: Poisoning Visual Document Retrieval Augmented Generation with a Single Image

Multimodal retrieval augmented generation (M-RAG) has recently emerged as a method to inhibit hallucinations of large multimodal models (LMMs) through a factual knowledge base (KB). However, M-RAG also introduces new attack vectors for adversaries that aim to disrupt the system by injecting malicious entries into the KB. In this work, we present a poisoning attack against M-RAG targeting visual document retrieval applications, where the KB contains images of document pages. Our objective is to craft a single image that is retrieved for a variety of different user queries, and consistently influences the output produced by the generative model, thus creating a universal denial-of-service (DoS) attack against the M-RAG system. We demonstrate that while our attack is effective against a diverse range of widely-used, state-of-the-art retrievers (embedding models) and generators (LMMs), it can also be ineffective against robust embedding models. Our attack not only highlights the vulnerability of M-RAG pipelines to poisoning attacks, but also sheds light on a fundamental weakness that potentially hinders their performance even in benign settings.

SoK: On Closing the Applicability Gap in Automated Vulnerability Detection

The frequent discovery of security vulnerabilities in both open-source and proprietary software underscores the urgent need for earlier detection during the development lifecycle. Initiatives such as DARPA's Artificial Intelligence Cyber Challenge (AIxCC) aim to accelerate Automated Vulnerability Detection (AVD), seeking to address this challenge by autonomously analyzing source code to identify vulnerabilities. This paper addresses two primary research questions: (RQ1) How is current AVD research distributed across its core components? (RQ2) What key areas should future research target to bridge the gap in the practical applicability of AVD throughout software development? To answer these questions, we conduct a systematization over 79 AVD articles and 17 empirical studies, analyzing them across five core components: task formulation and granularity, input programming languages and representations, detection approaches and key solutions, evaluation metrics and datasets, and reported performance. Our systematization reveals that the narrow focus of AVD research-mainly on specific tasks and programming languages-limits its practical impact and overlooks broader areas crucial for effective, real-world vulnerability detection. We identify significant challenges, including the need for diversified problem formulations, varied detection granularities, broader language support, better dataset quality, enhanced reproducibility, and increased practical impact. Based on these findings we identify research directions that will enhance the effectiveness and applicability of AVD solutions in software security.