# model-merging
10 articlestagged with “model-merging”
Training & Fine-Tuning Attacks
Methodology for data poisoning, trojan/backdoor insertion, clean-label attacks, LoRA backdoors, sleeper agent techniques, and model merging attacks targeting the LLM training pipeline.
Model Merging Risks
Security risks in model and adapter merging workflows -- how merging adapters from untrusted sources can introduce vulnerabilities, exploit merge algorithm properties, and cause safety property loss through TIES, DARE, SLERP, and linear interpolation.
Model Merging Security Analysis
Security implications of model merging techniques (TIES, DARE, SLERP) including backdoor propagation and safety property degradation.
Model Merging Attack Surface Analysis
Security analysis of model merging techniques including TIES, DARE, and SLERP for injecting malicious capabilities.
Model Merging Security Implications
Security analysis of model merging techniques and potential for backdoor propagation through merged models.
Lab: Model Merging Security Analysis
Analyze security implications of model merging techniques and test for backdoor propagation through merged model weights.
Model Merging Backdoor Propagation
Demonstrate how backdoors propagate through model merging techniques like TIES, DARE, and spherical interpolation.
Advanced Training Attack Vectors
Cutting-edge training attacks: federated learning poisoning, model merging exploits, distributed training vulnerabilities, emergent capability risks, and synthetic data pipeline attacks.
Model Merging & LoRA Composition Exploits
Exploiting model merging techniques (TIES, DARE, linear interpolation) and LoRA composition to introduce backdoors through individually benign model components.
Model Merging Safety Implications
Analysis of how model merging techniques (TIES, DARE, SLERP) affect safety properties and alignment.