HPE: Hallucinated Positive Entanglement for Backdoor Attacks in Federated Self-Supervised Learning

backdoor samples in the representation space. Finally, selective parameter poisoning and proximity-aware updates constrain the poisoned model within the vicinity of the global model, enhancing its stability and persistence

FedPoisonTTP: A Threat Model and Poisoning Attack for Federated Test-Time Personalization

data poisoning in the federated adaptation setting. FedPoisonTTP distills a surrogate model from adversarial queries, synthesizes in-distribution poisons using feature-consistency, and optimizes attack objectives to generate high-entropy

Beyond Denial-of-Service: The Puppeteer's Attack for Fine-Grained Control in Ranking-Based Federated Learning

Descending Edges to align the global model with the target model, and (ii) widening the selection boundary gap to stabilize the global model at the target accuracy. Extensive experiments across

Theory of Continual Learning Against Data Poisoning Attacks

latter regime, we derive a robust defense that minimizes the model's sensitivity to poisoned features, provably accelerating the convergence rate. Extensive experiments on realistic tasks further validate our theoretical

P2P: A Poison-to-Poison Remedy for Reliable Backdoor Defense in LLMs

Poison-to-Poison (P2P), a general and effective backdoor defense algorithm. P2P injects benign triggers with safe alternative labels into a subset of training samples and fine-tunes the model

SafeTune: Mitigating Data Poisoning in LLM Fine-Tuning for RTL Code Generation

datasets frequently lack security verification and are highly susceptible to data poisoning attacks. Such poisoning can cause models to generate syntactically valid but insecure hardware modules that bypass standard functionality

Byzantine-Robust Federated Learning Framework with Post-Quantum Secure Aggregation for Real-Time Threat Intelligence Sharing in Critical IoT Infrastructure

security suffer from two critical vulnerabilities: susceptibility to Byzantine attacks where malicious participants poison model updates, and inadequacy against future quantum computing threats that can compromise cryptographic aggregation protocols. This

On The Dangers of Poisoned LLMs In Security Automation

tuned Llama3.1 8B and Qwen3 4B models, we demonstrate how a targeted poisoning attack can bias the model to consistently dismiss true positive alerts originating from a specific user. Additionally

Python code when loaded, bypassing Picklescan's safety checks and enabling supply-chain poisoning of shared model files

Structure-Aware Distributed Backdoor Attacks in Federated Learning

across different model architectures. This assumption overlooks the impact of model structure on perturbation effectiveness. From a structure-aware perspective, this paper analyzes the coupling relationship between model architectures

CLASP: Defending Hybrid Large Language Models Against Hidden State Poisoning Attacks

State space models (SSMs) like Mamba have gained significant traction as efficient alternatives to Transformers, achieving linear complexity while maintaining competitive performance. However, Hidden State Poisoning Attacks (HiSPAs), a recently

Layer of Truth: Probing Belief Shifts under Continual Pre-Training Poisoning

track how internal preferences between competing facts evolve across checkpoints, layers, and model scales. Even moderate poisoning (50-100%) flips over 55% of responses from correct to counterfactual while leaving

Detecting Data Poisoning in Code Generation LLMs via Black-Box, Vulnerability-Oriented Scanning

generation large language models (LLMs) are increasingly integrated into modern software development workflows. Recent work has shown that these models are vulnerable to backdoor and poisoning attacks that induce

RAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning

ground-truth answers, and no access to model internals; it compares the model's own answers under counterfactual contexts. On poisoned Natural Questions at 5--30\% poison ratios, adversarial retriever

Securing the Model Context Protocol: Defending LLMs Against Tool Poisoning and Adversarial Attacks

Model Context Protocol (MCP) enables Large Language Models to integrate external tools through structured descriptors, increasing autonomy in decision-making, task execution, and multi-agent workflows. However, this autonomy creates

HAMLOCK: HArdware-Model LOgically Combined attacK

networks (DNNs) introduces new security vulnerabilities. Conventional model-level backdoor attacks, which only poison a model's weights to misclassify inputs with a specific trigger, are often detectable because

TooBad: 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