# training-pipeline
29 articlestagged with “training-pipeline”
Advanced Practice Exam
25-question practice exam covering advanced AI red team techniques: multimodal attacks, training pipeline exploitation, agentic system attacks, embedding manipulation, and fine-tuning security.
Practice Exam 2: Advanced AI Security
25-question advanced practice exam covering multimodal attacks, training pipeline security, cloud AI security, forensics, and governance.
Training Pipeline Security Assessment
Test your advanced knowledge of training pipeline attacks including data poisoning, fine-tuning hijacking, RLHF manipulation, and backdoor implantation with 9 questions.
Advanced Training Pipeline Assessment
Advanced assessment on RLHF exploitation, DPO vulnerabilities, and federated learning attacks.
Training Pipeline Security Study Guide
Study guide for training pipeline security covering data poisoning, RLHF attacks, and supply chain threats.
Capstone: Training Pipeline Attack & Defense
Attack a model training pipeline through data poisoning and backdoor insertion, then build defenses to detect and prevent these attacks.
ML CI/CD Security
Security overview of ML continuous integration and deployment pipelines: how ML CI/CD differs from traditional CI/CD, unique attack surfaces in training workflows, and the security implications of automated model building and deployment.
Checkpoint Manipulation Attacks (Training Pipeline)
Direct manipulation of model checkpoints and saved weights to inject backdoors or alter behavior.
Continuous Training Pipeline Attacks
Exploiting continuous learning and online training pipelines through streaming data manipulation.
Curriculum Learning Exploitation (Training Pipeline)
Exploiting curriculum learning and data ordering to amplify the effect of poisoned training examples.
Manipulating Curriculum Learning Schedules
How adversaries exploit curriculum learning by manipulating data ordering, difficulty scheduling, and stage transitions to embed vulnerabilities during training.
Data Poisoning at Scale
Techniques for poisoning training data at scale to influence model behavior across broad capabilities.
Attack Surface of Distributed Training
Security analysis of distributed training systems including gradient aggregation attacks, Byzantine fault exploitation, communication channel vulnerabilities, and federated learning threats.
Security Implications of DPO Training
Analysis of security vulnerabilities introduced by Direct Preference Optimization, including preference manipulation, implicit reward model exploitation, and safety alignment degradation.
DPO Training Vulnerabilities
Security analysis of Direct Preference Optimization training and its vulnerability to preference poisoning.
Evaluation Benchmark Gaming
Techniques for gaming evaluation benchmarks to mask vulnerabilities or inflate safety scores.
Federated Learning Attacks (Training Pipeline)
Attacks on federated learning setups including model poisoning, data inference, and aggregation manipulation.
Gradient-Based Attacks During Training
Technical deep dive into gradient-based attack methods that exploit training-time access, including gradient manipulation, adversarial weight perturbation, and training signal hijacking.
Model Supply Chain Attacks
Comprehensive analysis of model supply chain attack vectors from training data through deployment.
Pre-Training Data Attacks
Attacking the pre-training data pipeline including web crawl poisoning and data curation manipulation.
Security Comparison: Pre-training vs Fine-tuning
Comparative analysis of security vulnerabilities, attack surfaces, and defensive strategies across pre-training and fine-tuning phases of language model development.
RLHF Pipeline Exploitation
Exploiting reward model training, preference data collection, and RLHF optimization loops.
Security of RLHF: Reward Hacking and Reward Model Attacks
Comprehensive analysis of security vulnerabilities in RLHF pipelines, including reward hacking, reward model poisoning, and preference manipulation attacks.
Synthetic Data Pipeline Attacks (Training Pipeline)
Attacking synthetic data generation pipelines used for model training and augmentation.
Poisoning Attacks on Synthetic Training Data
Comprehensive analysis of poisoning vectors in synthetic data generation pipelines, from teacher model manipulation to post-generation filtering evasion.
Security of Training Checkpoints
Threat analysis of model checkpoint storage, serialization, and restoration including checkpoint poisoning, deserialization attacks, and integrity verification.
Security of Training Data Attribution Methods
Analysis of vulnerabilities in training data attribution techniques including influence functions, membership inference, and data provenance tracking, with implications for privacy and security.
Security Implications of Training Data Deduplication
Analysis of how deduplication algorithms create security vulnerabilities, including adversarial deduplication evasion, strategic duplicate injection, and hash collision attacks.
Training Infrastructure Attacks
Attacking training infrastructure including GPU clusters, distributed training, and orchestration systems.