AI Threat AlertPick Your Poison: Learning to Select Poison Sets for Stronger LLM Backdoor Attacks
Backdoor poisoning attacks add poisoned examples to otherwise-clean finetuning data, pairing a trigger with a target behavior that the model learns to produce when the trigger appears. Existing evaluations
Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks
Large language models (LLMs) are highly susceptible to backdoor attacks (BAs), wherein training samples are poisoned using trigger-based harmful content. Furthermore, existing defenses have proven ineffective when extensively tested
Empirical Evaluation of Data Poisoning Attacks in Supervised Learning
Data poisoning corrupts training data to degrade a model or to plant attacker-controlled behavior. This study evaluates two representative training-time attacks, label flipping and backdoor poisoning, on MNIST
Sleeper Cell: Injecting Latent Malice Temporal Backdoors into Tool-Using LLMs
model generates benign textual responses immediately after destructive actions. We empirically show that these poisoned models maintain state-of-the-art performance on benign tasks, incentivizing their adoption. Our findings
Rounding-Guided Backdoor Injection in Deep Learning Model Quantization
quantization to embed malicious behaviors. Unlike conventional backdoor attacks relying on training data poisoning or model training manipulation, QuRA solely works using the quantization operations. In particular, QuRA first employs
The Trigger in the Haystack: Extracting and Reconstructing LLM Backdoor Triggers
Detecting whether a model has been poisoned is a longstanding problem in AI security. In this work, we present a practical scanner for identifying sleeper agent-style backdoors in causal
ASTRIDE: A Security Threat Modeling Platform for Agentic-AI Applications
However, these systems introduce novel and evolving security challenges, including prompt injection attacks, context poisoning, model manipulation, and opaque agent-to-agent communication, that are not effectively captured by traditional
BRFID: Toward Byzantine-Robust Federated Intrusion Detection
Flipping 60\% of training labels from a single Byzantine client using label-flipping model poisoning self-degrades an attacker's own federated detection accuracy, $99.96\%$ (at no poisoning rate
Backdooring Bias in Large Language Models
model, with an adversary targeting the model builder's LLM. However, in the bias manipulation setting, the model builder themselves could be the adversary, warranting a white-box threat model
Zero-Knowledge Federated Learning with Lattice-Based Hybrid Encryption for Quantum-Resilient Medical AI
models across hospitals without centralizing patient data. However, the exchange of model updates exposes critical vulnerabilities: gradient inversion attacks can reconstruct patient information, Byzantine clients can poison the global model
Collaborative penetration testing suite for emerging generative AI algorithms
Problem Space: AI Vulnerabilities and Quantum Threats Generative AI vulnerabilities: model inversion, data poisoning, adversarial inputs. Quantum threats Shor Algorithm breaking RSA ECC encryption. Challenge Secure generative AI models against
Poisoning Learned Index Structures: Static and Dynamic Adversarial Attacks on ALEX
baseline to isolate adversarial effects. Our results show a clear separation between threat models. Static poisoning has minimal impact on lookup performance in ALEX under bulk-loaded settings, while dynamic
Inference-Time Backdoors via Hidden Instructions in LLM Chat Templates
model processing. We show that an adversary who distributes a model with a maliciously modified template can implant an inference-time backdoor without modifying model weights, poisoning training data
Data Poisoning Vulnerabilities Across Healthcare AI Architectures: A Security Threat Analysis
analyses needed for detection. Supply chain weaknesses allow a single compromised vendor to poison models across 50 to 200 institutions. The Medical Scribe Sybil scenario shows how coordinated fake patient
Physics-Attested Federated Learning: Securing Collaborative Anomaly Detection in Critical Water Infrastructure
telemetry. However, existing defenses operate strictly in update space, leaving aggregators blind to data poisoning; model updates derived from fabricated telemetry remain indistinguishable from honest contributions. We repurpose cyber-physical
Phantasia: Context-Adaptive Backdoors in Vision Language Models
backdoor attack that dynamically aligns its poisoned outputs with the semantics of each input. Instead of producing static poisoned patterns, Phantasia encourages models to generate contextually coherent yet malicious responses
Federated Art Chase: Privacy Leakage and Data Poisoning in Federated Fine-Tuning
report closes with a live session in which participants draw, fine-tune, and poison the model through Indigma's FedMaestro orchestrator. Code and executed notebooks, runnable on Google Colab
Cybersecurity in Edge Computing: A Trust-Aware Federated Hybrid Intrusion Detection Framework
temporal feature extraction. Model training is performed collaboratively via federated learning, ensuring raw network telemetry remains isolated at local gateways. Furthermore, to defend against adversarial model poisoning attacks, we introduce
Poster: FedWM-Guard: Thwarting Imagination Poisoning in Federated World Model-based Autonomous Driving
Federated learning (FL) can improve world model (WM)-based autonomous driving (AD) without centralizing raw private vehicle data, but it also turns model aggregation into a safety-critical integrity boundary
Who Speaks for the Trigger? Dynamic Expert Routing in Backdoored Mixture-of-Experts Transformers
these targeted experts. Unlike traditional backdoor attacks that rely on superficial data poisoning or model editing, BadSwitch primarily embeds malicious triggers into expert routing paths with strong task affinity, enabling