AI Threat AlertTooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger
factors: attack performance, stealthiness, time complexity, and required poison rates. For example, achieving high attack performance typically demands a high poison rate and prolonged training, which undermines stealthiness, making
Hidden State Poisoning Attacks against Mamba-based Language Models
their hidden states, referred to as a Hidden State Poisoning Attack (HiSPA). Our benchmark RoBench25 allows evaluating a model's information retrieval capabilities when subject to HiSPAs, and confirms
SteganoBackdoor: Stealthy and Data-Efficient Backdoor Attacks on Language Models
Modern language models remain vulnerable to backdoor attacks via poisoned data, where training inputs containing a trigger are paired with a target output, causing the model to reproduce that behavior
Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery
Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients upload manipulated updates to degrade the performance of the global model. Although detection methods can identify and remove malicious
Steering Vectors are an Adversarial Attack Surface
verify. We test the attack on two open-weight model families and eight model-attribute combinations, observing that poisoned vectors reach an absolute attack success rate
Virus Infection Attack on LLMs: Your Poisoning Can Spread "VIA" Synthetic Data
data poisoning and backdoor attacks show that VIA significantly increases the presence of poisoning content in synthetic data and correspondingly raises the attack success rate (ASR) on downstream models
Adaptive and Robust Data Poisoning Detection and Sanitization in Wearable IoT Systems using Large Language Models
environments. This work proposes a novel framework that uses large language models (LLMs) to perform poisoning detection and sanitization in HAR systems, utilizing zero-shot, one-shot, and few-shot
Token by Token, Compromised: Backdoor Vulnerabilities in Unified Autoregressive Models
model in which a subtle word (e.g., ``cool'') induces modality-aligned brand promotion or ideological influence in 55% of generations. Without model access, ToBAC can be induced through data poisoning
Hidden State Poisoning Attacks against Mamba-based Language Models
their hidden states, referred to as a Hidden State Poisoning Attack (HiSPA). Our benchmark RoBench-25 allows evaluating a model's information retrieval capabilities when subject to HiSPAs, and confirms
DPOison-VL: Clean Preference Poisoning in Vision-Language Direct Preference Optimization
demonstrate that DPOison-VL achieves up to 98\% ASR with only 2\% poisoned data and exhibits cross-model transferability, while largely preserving clean accuracy and general utility. Our findings reveal
MCP Tool Poisoning in Agentic AI Frameworks: A Cross-Framework Measurement Study
models to external tool providers, but it carries attacker-controlled content (tool descriptions, schema fields, and response data) into model context without validation. Prior benchmarks measure model compliance with poisoned
Robust Industrial Cyber Physical Classification Using Neuromorphic Temporal Embeddings and Hybrid SNN XGBoost Under Machine Unlearning Attacks
loses only 0.9\% F1-macro at 10\% poisoning and delays target-class collapse from 60\% to 70\% poisoning compared with raw models. These results demonstrate that neuromorphic temporal encoding
SoK: Unlearnability and Unlearning for Model Dememorization
Advanced model dememorization methods, including availability poisoning (unlearnability) and machine unlearning, are emerging as key safeguards against data misuse in machine learning (ML). At the training stage, unlearnability embeds imperceptible
Factual Yet One-Sided: Stance Poisoning and Evidence Balancing in Retrieval-Augmented Generation
post-defense intervals included 0 in all eight settings, while the cross-model pro-poisoning results retained small positive residual point estimates of +0.045 to +0.063. For 20 candidate documents
The 'Sure' Trap: Multi-Scale Poisoning Analysis of Stealthy Compliance-Only Backdoors in Fine-Tuned Large Language Models
conduct a multi-scale analysis of this benign-label poisoning behavior across poison budget, total fine-tuning dataset size, and model size. A sharp threshold appears at small absolute budgets
Confundo: Learning to Generate Robust Poison for Practical RAG Systems
present Confundo, a learning-to-poison framework that fine-tunes a large language model as a poison generator to achieve high effectiveness, robustness, and stealthiness. Confundo provides a unified framework
CSC: Turning the Adversary's Poison against Itself
compromise model utility through unlearning methods that lead to accuracy degradation. This paper conducts a comprehensive analysis of backdoor attack dynamics during model training, revealing that poisoned samples form isolated
ViTeGate: Visual-Textual Triggered Knowledge Poisoning for Vision-Language Retrieval-Augmented Generation
inject poisoned image-text pairs into the knowledge corpus to influence model outputs. Existing knowledge poisoning attacks are typically always-on, allowing poisoned evidence to affect generation whenever
Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection
demonstrate that latent backdoors can be successfully implanted in flow-based IDS models by poisoning as little as 0.001% of the training data, maintaining near-nominal performance on clean inputs
High-quality Data Do not Mean Safe! Poisoning LLMs after Data Selection
QSTO), which optimizes poisoned samples under an explicit quality constraint to survive selection while preserving their safety-degrading influence. Across poisoning settings, target models, and filtering rates, Bi-QSTO maintains