# pipeline
30 articlestagged with “pipeline”
Haystack Pipeline Exploitation
Exploiting Haystack's pipeline architecture for component injection and data flow manipulation.
Training Pipeline Security Practice Exam
Practice exam on data poisoning, RLHF exploitation, fine-tuning attacks, and supply chain risks.
Skill Verification: Training Pipeline Security
Skill verification for data poisoning, RLHF exploitation, and fine-tuning attack techniques.
Vertex AI Attack Surface
Red team methodology for Vertex AI: prediction endpoint abuse, custom training security gaps, feature store poisoning, model monitoring evasion, and pipeline exploitation.
CI/CD Code Generation Risks (Code Gen Security)
Security risks of AI-generated code in CI/CD pipelines including automated merge attacks, test generation manipulation, and pipeline injection.
Payload Generation Pipeline
Build an automated pipeline for generating, mutating, and testing prompt injection payloads.
Continuous Automated Red Teaming (CART)
Designing CART pipelines for ongoing AI security validation: architecture, test suites, telemetry, alerting, regression detection, and CI/CD integration.
Red Team Infrastructure & Tooling
AI red team C2 frameworks, automated attack pipelines, custom scanner development, and integration with Cobalt Strike, Mythic, and Sliver.
ML Pipeline Supply Chain Security
Securing the ML pipeline supply chain from training framework dependencies to serving infrastructure components.
Lab: Multimodal Attack Pipeline
Build an automated multimodal attack pipeline that generates adversarial images, combines them with text prompts, and tests against vision-language models (VLMs).
Lab: ML Pipeline Poisoning
Compromise an end-to-end machine learning pipeline by attacking data ingestion, preprocessing, training, evaluation, and deployment stages. Learn to identify and exploit weaknesses across the full ML lifecycle.
Lab: Automated Red Team Pipeline
Hands-on lab for building a continuous AI red team testing pipeline using promptfoo, GitHub Actions, and automated attack generation to catch safety regressions before deployment.
Multi-Model Pipeline Attack Lab
Attack a pipeline where multiple models process data sequentially, exploiting trust between pipeline stages.
AI Supply Chain Pipeline Assessment
Assess the full ML pipeline from data ingestion through model deployment for supply chain attacks.
Deployment Pipeline Attacks
Comprehensive analysis of attack vectors in ML deployment pipelines including build system compromise, artifact tampering, and deployment manipulation.
Continuous Training Pipeline Attacks
Exploiting continuous learning and online training pipelines through streaming data manipulation.
Instruction Tuning Data Manipulation
Manipulating instruction tuning datasets to embed specific behaviors in the resulting model.
Knowledge Distillation Security
Security implications of knowledge distillation including capability extraction and safety alignment transfer.
Model Merging Security Analysis (Training Pipeline)
Security analysis of model merging techniques and propagation of vulnerabilities through merged models.
Preference Data Poisoning (Training Pipeline)
Poisoning preference data used in RLHF and DPO to shift model alignment toward attacker objectives.
RLHF Pipeline Exploitation
Exploiting reward model training, preference data collection, and RLHF optimization loops.
Synthetic Data Pipeline Attacks (Training Pipeline)
Attacking synthetic data generation pipelines used for model training and augmentation.
Training Data Provenance Attacks
Attacking training data provenance and attribution systems to inject unverified data sources.
Batch Processing Injection Walkthrough
Inject payloads through batch processing pipelines where individual items are processed without isolation.
Multi-Layer Input Validation
Step-by-step walkthrough for building a defense-in-depth input validation pipeline that combines regex matching, semantic similarity, ML classification, and rate limiting into a unified validation system for LLM applications.
Output Filtering Pipeline Design
Design and implement a multi-stage output filtering pipeline for LLM applications.
Secure RAG Pipeline Architecture
Build a secure RAG pipeline with document sanitization, retrieval validation, and injection-resistant augmentation.
Building an Output Scanning Pipeline
Build a production output scanning pipeline that detects data leakage, harmful content, and injection artifacts.
Full Engagement: Document Processing Pipeline
Full engagement of an AI document processing pipeline handling invoices, contracts, and compliance documents.
Setting Up Continuous AI Red Teaming Pipelines
Walkthrough for building continuous AI red teaming pipelines that automatically test LLM applications on every deployment, covering automated scan configuration, CI/CD integration, alert thresholds, regression testing, and dashboard reporting.