# dependencies
標記為「dependencies」的 22 篇文章
Agent Supply Chain Attacks
Compromising AI agents through poisoned packages, backdoored MCP servers, malicious model registries, and weaponized agent frameworks -- including the Postmark MCP breach and NullBulge campaigns.
July 2026: Supply Chain Audit Challenge
Audit an ML project's entire supply chain for security issues including dependencies, model provenance, data pipelines, training infrastructure, and deployment artifacts.
AI Supply Chain Security Overview
Comprehensive overview of the AI/ML supply chain attack surface, covering model poisoning, data poisoning, dependency attacks, and risk assessment frameworks aligned with OWASP LLM03:2025.
Dependency Scanning for AI/ML
Defense-focused guide to scanning AI/ML dependencies for vulnerabilities, covering AI-specific dependency risks, malicious package detection, automated scanning pipelines, and policy enforcement for ML toolchains.
Deep Supply Chain Analysis
Comprehensive analysis of the AI supply chain dependency tree covering model weights, tokenizers, datasets, libraries, and infrastructure components with audit methodology.
ML Pipeline Supply Chain Security
Securing the ML pipeline supply chain from training framework dependencies to serving infrastructure components.
Supply Chain Security for ML Dependencies
Securing the ML dependency supply chain including PyTorch, transformers, and model weight downloads.
CTF: Supply Chain Attack
Find and exploit vulnerabilities in an ML supply chain including compromised dependencies, poisoned models, backdoored training data, and malicious model files. Practice ML-specific supply chain security assessment.
Lab: Supply Chain Audit
Audit an ML project's dependencies for vulnerabilities, covering model files, Python packages, container images, and training data provenance.
Lab: ML Supply Chain Scan
Hands-on lab for auditing machine learning model dependencies, detecting malicious packages in ML pipelines, and scanning model files for backdoors and supply chain threats.
Supply Chain Prompt Injection Walkthrough
Plant injection payloads in upstream data sources consumed by LLM applications including packages and documentation.
代理 Supply Chain 攻擊s
Compromising AI agents through poisoned packages, backdoored MCP servers, malicious model registries, and weaponized agent frameworks -- including the Postmark MCP breach and NullBulge campaigns.
July 2026: Supply Chain Audit Challenge
Audit an ML project's entire supply chain for security issues including dependencies, model provenance, data pipelines, training infrastructure, and deployment artifacts.
AI Supply Chain 安全 概覽
Comprehensive overview of the AI/ML supply chain attack surface, covering model poisoning, data poisoning, dependency attacks, and risk assessment frameworks aligned with OWASP LLM03:2025.
Dependency Scanning for AI/ML
防禦-focused guide to scanning AI/ML dependencies for vulnerabilities, covering AI-specific dependency risks, malicious package detection, automated scanning pipelines, and policy enforcement for ML toolchains.
深入供應鏈分析
AI 供應鏈依賴樹之完整分析，涵蓋模型權重、tokenizer、資料集、函式庫與基礎設施元件，含稽核方法論。
ML Pipeline Supply Chain 安全
Securing the ML pipeline supply chain from training framework dependencies to serving infrastructure components.
Supply Chain 安全 for ML Dependencies
Securing the ML dependency supply chain including PyTorch, transformers, and model weight downloads.
CTF：供應鏈攻擊
尋找並利用 ML 供應鏈漏洞，包括遭入侵相依、被投毒模型、被植後門訓練資料與惡意模型檔。練習 ML 特有的供應鏈安全評估。
實驗室: Supply Chain Audit
Audit an ML project's dependencies for vulnerabilities, covering model files, Python packages, container images, and training data provenance.
實驗室: ML Supply Chain Scan
Hands-on lab for auditing machine learning model dependencies, detecting malicious packages in ML pipelines, and scanning model files for backdoors and supply chain threats.
Supply Chain 提示詞注入 導覽
Plant injection payloads in upstream data sources consumed by LLM applications including packages and documentation.