AI Threat AlertADMIT: Few-shot Knowledge Poisoning Attacks on RAG-based Fact Checking
Knowledge poisoning poses a critical threat to Retrieval-Augmented Generation (RAG) systems by injecting adversarial content into knowledge bases, tricking Large Language Models (LLMs) into producing attacker-controlled outputs grounded
Fairness-Constrained Optimization Attack in Federated Learning
demographics. FL enables model sharing, while restricting the movement of data. Since FL provides participants with independence over their training data, it becomes susceptible to poisoning attacks. Such collaboration also
Adversarial Hubness Detector: Detecting Hubness Poisoning in Retrieval-Augmented Generation Systems
Retrieval-Augmented Generation (RAG) systems are essential to contemporary AI
Dynamic Black-box Backdoor Attacks on IoT Sensory Data
measurements can be fed to a machine learning-based model to train and classify human activities. While deep learning-based models have proven successful in classifying human activity and gestures
RAG-NAROK: Retrieval-Aware Knowledge Corpus Poisoning in RAG with Source-specific Refutation
language model (LLM) outputs in verifiable external knowledge, yet their structural reliance on a dynamic retrieval pipeline introduces a largely unexplored class of adversarial vulnerability. Existing knowledge-base poisoning attacks
SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors
data to manipulate model predictions. Existing defenses mainly operate during before and during aggregation cannot fully eliminate backdoor behaviors that persist in the converged global model. Moreover, the effectiveness
A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes
survey emerging threat models, security frameworks, and evaluation pipelines tailored to agentic systems, and analyze systemic risks including agent collusion, cascading failures, oversight evasion, and memory poisoning. Finally, we present
Fairness Testing in Retrieval-Augmented Generation: How Small Perturbations Reveal Bias in Small Language Models
Large Language Models (LLMs) are widely used across multiple domains but continue to raise concerns regarding security and fairness. Beyond known attack vectors such as data poisoning and prompt injection
Hidden in the Metadata: Stealth Poisoning Attacks on Multimodal Retrieval-Augmented Generation
augmented generation (RAG) has emerged as a powerful paradigm for enhancing multimodal large language models by grounding their responses in external, factual knowledge and thus mitigating hallucinations. However, the integration
MURMUR: Using cross-user chatter to break collaborative language agents in groups
today's language models lack a mechanism for isolating user interactions and concurrent tasks, creating a new attack vector inherent to this new setting: cross-user poisoning
Characterizing and Defending Utility-Hijacking in Instruction-Tuning Data Selection
with scoring-model scale from 2.84x to 7.04x. To defend against this attack, we propose CONCERT, a targeted defense needing no content detector or poison labels. It flags
Retrieved But Not Reliable: A Survey on Attacks, and Defenses in Retrieval-Augmented Generation
including corpus poisoning, backdoor attacks, privacy leakage, and fairness violations. Despite rapid progress in this area, existing surveys remain limited in their treatment of attacker objectives, threat models, and stage
Game-Theoretic Multi-Agent Control for Robust Contextual Reasoning in LLMs
Large Language Models (LLMs) in multi-turn interactions maintain evolving context rather than generating isolated responses, making them vulnerable to prompt-injection and context-poisoning attacks in which locally plausible
Memory poisoning and secure multi-agent systems
Memory poisoning attacks for Agentic AI and multi-agent systems (MAS) have recently caught attention. It is partially due to the fact that Large Language Models (LLMs) facilitate the construction
Hijacking Agent Memory: Stealthy Trojan Attacks Through Conversational Interaction
Large language model (LLM) agents increasingly leverage long term memory to support persistent and autonomous task execution. However, this capability also introduces a new attack surface: memory poisoning, where adversaries
LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges
injection, data poisoning, retrieval vulnerabilities, and reward hacking, which expose automated review pipelines to strategic manipulation. From a data mining perspective, we outline key open challenges in modeling subjective disagreement
FuncPoison: Poisoning Function Library to Hijack Multi-agent Autonomous Driving Systems
Autonomous driving systems increasingly rely on multi-agent architectures powered by large language models (LLMs), where specialized agents collaborate to perceive, reason, and plan. A key component of these systems
Eyes-on-Me: Scalable RAG Poisoning through Transferable Attention-Steering Attractors
data poisoning and show that modular, reusable components pose a practical threat to modern AI systems. They also reveal a strong link between attention concentration and model outputs, informing interpretability
CODE: A Contradiction-Based Deliberation Extension Framework for Overthinking Attacks on Retrieval-Augmented Generation
multi-step self-verification. However, recent studies have shown that reasoning models suffer from overthinking attacks, where models are tricked to generate unnecessarily high number of reasoning tokens. In this
Assessing Attack Surfaces in Generative Search Engines through Publisher Attributes: A Case Study in Political Domains
language models (LLMs). They play a crucial role in how users access information on the web. Because anyone can publish content on the web, GSEs are vulnerable to poisoning attacks