Language Model Security Database

M3Att demonstrates a reproducible knowledge-poisoning issue in medical multimodal RAG: an attacker with limited corpus-distribution knowledge can insert paired image-text entries whose visually perturbed images are broadly retrieved and whose clinically plausible misinformation steers downstream generation. The paper evaluates both white-box and black-box retrieval optimization and reports degraded diagnostic and report-generation utility across multiple datasets, retrievers, and LVLMs. These…

Memory-augmented LLM web agents utilizing raw trajectory memory are vulnerable to Environment-injected Trajectory-based Agent Memory Poisoning (eTAMP). Attackers can embed malicious instructions within user-generated web content (e.g., product pages, forum posts). When the agent processes this content during a routine task, the instructions are passively ingested into its raw trajectory memory. During subsequent, entirely separate tasks on different websites, semantic retrieval mechanisms pull…

Mobile Large Language Model (LLM) agents operating under the "Screen-as-Interface" paradigm are vulnerable to visual indirect prompt injection and state desynchronization. Agents that rely on unstructured visual data (screenshots) and Accessibility Service APIs to perceive the environment lack a mechanism to distinguish between trusted system UI elements and untrusted content (e.g., web pages, emails, or malicious overlays). An attacker can inject visual cues, fake notifications, or hidden…

Multimodal Large Language Model-based Recommender Systems (MLLM-RecSys) are vulnerable to Cross-Modal Interactive Data Poisoning. Attackers can manipulate the system by injecting compromised user-generated content (UGC) that contains synchronized, coupled perturbations across both textual and visual modalities. While MLLMs naturally filter out single-modality noise via cross-modal consensus, this vulnerability exploits the consensus mechanism itself. By leveraging cross-modal attention to…

Audio Large Language Models (ALLMs) integrated into voice agent systems for high-stakes domains (banking, IT support, logistics) are vulnerable to multimodal adversarial attacks via spoken interaction. Adversaries can exploit the model's inherent compliance and contextual awareness through multi-turn dialogue to bypass authentication safeguards, escalate privileges (e.g., unauthorized credit limit increases), exfiltrate sensitive Personally Identifiable Information (PII), and poison…

LLM-enhanced Graph Neural Networks (GNNs), which integrate Large Language Model (LLM) feature encoders with graph message-passing architectures, are vulnerable to a black-box node injection attack known as "GraphTextack." This vulnerability exists because the joint model architecture creates a dual attack surface: the GNN component is sensitive to structural perturbations (changes in graph topology), while the LLM component is sensitive to semantic perturbations (adversarial phrasing).