# pipeline
標記為「pipeline」的 62 篇文章
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 Security
Defense-focused guide to securing ML training and deployment pipelines, covering CI/CD cross-tenant attacks, safetensors conversion hijacking, pipeline hardening, and isolated build environments.
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.
Haystack Pipeline 利用ation
Exploiting Haystack's pipeline architecture for component injection and data flow manipulation.
訓練 Pipeline 安全 Practice Exam
Practice exam on data poisoning, RLHF exploitation, fine-tuning attacks, and supply chain risks.
Skill Verification: 訓練 Pipeline 安全
Skill verification for data poisoning, RLHF exploitation, and fine-tuning attack techniques.
Vertex AI 攻擊面
為 Vertex AI 之紅隊方法論：預測端點濫用、自訂訓練安全缺口、特徵儲存投毒、模型監控逃避與管線利用。
CI/CD Code Generation Risks (Code Gen 安全)
安全 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.
持續自動化紅隊（CART）
為持續 AI 安全驗證設計 CART 管線：架構、測試套件、遙測、警報、回歸偵測與 CI/CD 整合。
紅隊基礎設施與工具
AI 紅隊 C2 框架、自動化攻擊管線、自製掃描器開發，以及與 Cobalt Strike、Mythic、Sliver 的整合。
ML Pipeline 安全
防禦-focused guide to securing ML training and deployment pipelines, covering CI/CD cross-tenant attacks, safetensors conversion hijacking, pipeline hardening, and isolated build environments.
ML Pipeline Supply Chain 安全
Securing the ML pipeline supply chain from training framework dependencies to serving infrastructure components.
實驗室: Multimodal 攻擊 Pipeline
Build an automated multimodal attack pipeline that generates adversarial images, combines them with text prompts, and tests against vision-language models (VLMs).
實驗室: ML Pipeline 投毒
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.
實驗室: Automated 紅隊 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-模型 Pipeline 攻擊 實驗室
攻擊 a pipeline where multiple models process data sequentially, exploiting trust between pipeline stages.
AI Supply Chain Pipeline 評量
Assess the full ML pipeline from data ingestion through model deployment for supply chain attacks.
Deployment Pipeline 攻擊s
Comprehensive analysis of attack vectors in ML deployment pipelines including build system compromise, artifact tampering, and deployment manipulation.
Continuous 訓練 Pipeline 攻擊s
利用ing 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 安全
安全 implications of knowledge distillation including capability extraction and safety alignment transfer.
模型 Merging 安全 Analysis (訓練 Pipeline)
安全 analysis of model merging techniques and propagation of vulnerabilities through merged models.
Preference Data 投毒 (訓練 Pipeline)
投毒 preference data used in RLHF and DPO to shift model alignment toward attacker objectives.
RLHF Pipeline 利用ation
利用ing reward model training, preference data collection, and RLHF optimization loops.
Synthetic Data Pipeline 攻擊s (訓練 Pipeline)
攻擊ing synthetic data generation pipelines used for model training and augmentation.
訓練 Data Provenance 攻擊s
攻擊ing training data provenance and attribution systems to inject unverified data sources.
Batch Processing Injection 導覽
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 紅隊ing Pipelines
導覽 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.