AI Threat AlertFLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning
learning (FL) enables collaborative model training while preserving data privacy. However, it remains vulnerable to malicious clients who compromise model integrity through Byzantine attacks, data poisoning, or adaptive adversarial behaviors
Repurposing and Evaluating the (In)Feasibility of Dataset Poisoning enabled Watermarking for Contrastive Learning
third-party or internet data is common. Recent studies show CL models are vulnerable to data-poisoning backdoor attacks, but their generalization and robustness are underexplored. We systematically evaluate existing
From Theory to Practice: Evaluating Data Poisoning Attacks and Defenses in In-Context Learning on Social Media Health Discourse
This study explored how in-context learning (ICL) in large language models can be disrupted by data poisoning attacks in the setting of public health sentiment analysis. Using tweets
Cisco Integrated AI Security and Safety Framework Report
threats now span content safety failures (e.g., harmful or deceptive outputs), model and data integrity compromise (e.g., poisoning, supply-chain tampering), runtime manipulations (e.g., prompt injection, tool and agent misuse
Trustworthy Distributed Machine Learning in Cloud-Edge-IoT Systems: A Heterogeneity-Aware Survey of Security, Privacy, and Robustness
detectability, and defense reliability. Next, we synthesize attack-defense interactions across poisoning, backdoors, Byzantine manipulation, privacy leakage, model extraction, network/orchestration attacks, Sybil behavior, and strategic participation. We further compare layered
Temporal Poisoning: Clean-Label Backdoors via Event Redistribution in SNNs
inspection are blind by construction, while feature-space methods detect the poison only in selected settings. Our model-free detector, based on per-step event mass, detects the evaluated temporal
SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models
Backdoor attacks create significant security threats to language models by
Stealthy Poisoning Attacks Bypass Defenses in Regression Settings
natural and physical sciences, yet their robustness to poisoning has received less attention. When it has, studies often assume unrealistic threat models and are thus less useful in practice
State Backdoor: Towards Stealthy Real-world Poisoning Attack on Vision-Language-Action Model in State Space
Vision-Language-Action (VLA) models are widely deployed in safety
Osmosis Distillation: Model Hijacking with the Fewest Samples
generated by dataset distillation methods, where an adversary can perform a model hijacking attack with only a few poisoned samples in the synthetic dataset. To reveal this threat, we propose
Needle-in-RAG: Prompt-Conditioned Character-Level Traceback of Poisoned Spans in Retrieved Evidence
evidence, but it also opens a data-layer attack surface: poisoned corpus entries can steer outputs without changing model parameters. Existing defenses and traceback methods are largely passage-level, which
LoRA-Patch: Defending Against Backdoored LoRA Adapters via Directional Steering
typically sanitize backdoored models by updating, pruning, distilling, or mixing model parameters. However, these approaches are misaligned with adapter-based deployment, where the trusted base model is expected to remain
Universal Defenses for Tool-Integrated LLM Agents Against Adversarial Attacks
adversarial attacks, including direct prompt injection, indirect prompt injection, memory poisoning, and backdoor attacks, which exploit the model's openness to prompt injection and tool manipulation. In this work
MCP Pitfall Lab: Exposing Developer Pitfalls in MCP Tool Server Security under Multi-Vector Attacks
email, document, crypto) with six server variants (baseline and hardened) and model three attack families: tool-metadata poisoning, puppet servers, and multimodal image-to-tool chains, in a unified, trace
Test-Time Attention Purification for Backdoored Large Vision Language Models
defenses across diverse datasets and backdoor attack types, while preserving the model's utility on both clean and poisoned samples
Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
semantic deviation caused by label flipping, both of which make poisoned graphs easily detectable by anomaly detection models. To address this, we propose DPSBA, a clean-label backdoor framework that
Semantic Chameleon: Corpus-Dependent Poisoning Attacks and Defenses in RAG Systems
documents are preferentially retrieved at inference time, enabling targeted manipulation of model outputs. We study gradient-guided corpus poisoning attacks against modern RAG pipelines and evaluate retrieval-layer defenses that
Transferable Backdoor Attacks for Code Models via Sharpness-Aware Adversarial Perturbation
software development but remain vulnerable to backdoor attacks via poisoned training data. Existing backdoor attacks on code models face a fundamental trade-off between transferability and stealthiness. Static trigger-based
FedDetox: Robust Federated SLM Alignment via On-Device Data Sanitization
critical issue we define as unintended data poisoning, which can severely damage the safety alignment of global models during federated alignment. To address this, we propose FedDetox, a robust framework
Towards Safer RAG: Only Agents Capable of System 2 Thinking may Access Untrusted Documents
Generation (RAG) has significantly enhanced the performance of large language models (LLMs), yet these systems remain vulnerable to knowledge-poisoning attacks, in which misinformation in retrieved documents can influence