security
Secure backend applications against OWASP threats. Implement authentication, encryption, scanning, compliance, and incident response procedures.
发现 研究与调研 相关 Skills,比较来源仓库和用途,查看适合你工作流的技能说明。
Secure backend applications against OWASP threats. Implement authentication, encryption, scanning, compliance, and incident response procedures.
Backend testing strategies and test automation. Unit, integration, E2E, and load testing with best practices.
Master ASP.NET Core fundamentals including C#, project structure, routing, middleware, and basic API development. Essential skills for all ASP.NET Core developers.
Master ASP.NET Core deployment, Docker, Azure cloud, CI/CD pipelines, and production infrastructure for enterprise applications.
Master advanced ASP.NET Core development including Entity Framework Core, authentication, testing, and enterprise patterns for production applications.
Retrofit, OkHttp, REST APIs, JSON serialization, network security.
Unit testing, performance optimization, security implementation, Play Store deployment.
MVVM pattern, Clean Architecture, Repository pattern, dependency injection, SOLID principles.
Room ORM, SQLite, SharedPreferences, DataStore, encryption.
Android core components lifecycle, Activities, Fragments, Services, Intent system.
Master Kotlin syntax, OOP principles, SOLID practices, functional programming, and data structures.
XML layouts, ConstraintLayout, Jetpack Compose, Material Design 3.
Systematic vulnerability finding, threat modeling, and attack surface analysis for AI/LLM security assessments
Implement mitigations, create input filters, design output guards, and build defensive prompting for LLM security
Ethical vulnerability reporting, coordinated disclosure, and bug bounty participation for AI systems
Tools and frameworks for AI red teaming including PyRIT, garak, Counterfit, and custom attack automation
Generate adversarial inputs, edge cases, and boundary test payloads for stress-testing LLM robustness
Techniques to test and bypass AI safety filters, content moderation systems, and guardrails for security assessment
Attack techniques for Retrieval-Augmented Generation systems including knowledge base poisoning
Securing AI/ML infrastructure including model storage, API endpoints, and compute resources
Test AI training pipelines for data poisoning vulnerabilities and backdoor injection
Real-time monitoring and detection of adversarial attacks and model drift in production
Privacy attacks to extract training data and sensitive information from AI models
Implementing safety filters, content moderation, and guardrails for AI system inputs and outputs
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。