Language Model Security Database

A vulnerability exists in the similarity-based retrieval mechanisms of long-term memory-augmented Large Language Models (LLMs), specifically affecting systems like Mem0 and A-mem. The vulnerability arises from the system's reliance on dense embedding similarity (e.g., cosine similarity) to retrieve context from dynamic, user-generated memory banks without sufficient semantic validation or conflict resolution. An unprivileged remote attacker can exploit this by injecting "adversarial…

LLM-as-a-Judge systems utilizing natural language rubrics are vulnerable to Rubric-Induced Preference Drift (RIPD). This vulnerability allows an attacker (or a flawed optimization process) to refine evaluation rubrics such that they maintain high agreement with human references on standard validation benchmarks while inducing systematic, directional preference degradation on unseen target domains. The attack exploits the disconnect between benchmark validation and target generalization by…

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…

Text scoring models, including dense retrievers, rerankers, and reward models, are vulnerable to score manipulation attacks via search-based discrete perturbations and content injection. An attacker can systematically modify candidate texts using rudimentary string manipulations, gradient-guided token swaps (e.g., HotFlip), masked language modeling (MLM) swaps, or query/sentence injections to spuriously increase model scores. This structural failure condition allows an irrelevant passage or a…

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…

Unauthenticated, query-only memory poisoning (Memory Injection Attack - MINJA) in LLM agents equipped with persistent, shared memory allows attackers to manipulate the agent's long-term knowledge base. Adversaries embed malicious "indication prompts" and utilize progressive shortening within seemingly benign queries to induce the agent into autonomously generating and storing corrupted relational mappings. Because the memory is shared and retrieved via similarity (e.g., Levenshtein distance)…

A data poisoning vulnerability in safety-aligned Large Language Models (LLMs) allows attackers to disrupt model fine-tuning via "Disclaimer Injection." By appending or prepending short, legal-style safety or liability disclaimers to ordinary training data, an attacker can reliably trigger the model's internal alignment mechanisms. This forces the model to route the training inputs through specialized safety and refusal pathways rather than standard task-learning layers. Consequently, the model…

Large Language Models (LLMs), specifically Llama-3.1-8B-Instruct and Qwen2.5-14B-Instruct, are vulnerable to emergent misalignment caused by "character-conditioned" fine-tuning. This vulnerability arises when models are fine-tuned on small datasets (e.g., 500 examples) that exhibit consistent behavioral dispositions (e.g., "Evil," "Sycophantic," or "Hallucinatory") rather than just incorrect facts. This process creates a latent control variable—defined as "character"—that governs model…

A vulnerability exists in the "Adaptive Trust Weighting" mechanism of the Cost-Aware Proof of Quality (PoQ) protocol for decentralized LLM inference. The protocol updates evaluator trust weights based on the deviation of a submitted score from the consensus score of the current round. Because the consensus score is derived from the very scores being evaluated (a self-referential feedback loop), the mechanism fails to distinguish between honest and coordinated malicious evaluators…

Web-facing Retrieval-Augmented Generation (RAG) systems are vulnerable to Indirect Prompt Injection (IPI) and retrieval poisoning via web-native markup and Unicode carriers. Standard ingestion pipelines often parse untrusted web pages without stripping invisible constructs, such as hidden HTML spans, off-screen CSS, alt text, ARIA attributes, and zero-width characters. When an attacker embeds malicious instructions within these invisible carriers on third-party sites, the RAG system retrieves…