GitHub
responsible-disclosure
Ethical vulnerability reporting, coordinated disclosure, and bug bounty participation for AI systems
Skills IA Bases de données
Découvrez les skills Bases de données, comparez leurs dépôts et consultez leurs instructions.
Catégories: Bases de données
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Explorer
GitHub
red-team-frameworks
Tools and frameworks for AI red teaming including PyRIT, garak, Counterfit, and custom attack automation
GitHub
adversarial-examples
Generate adversarial inputs, edge cases, and boundary test payloads for stress-testing LLM robustness
GitHub
safety-filter-bypass
Techniques to test and bypass AI safety filters, content moderation systems, and guardrails for security assessment
GitHub
rag-exploitation
Attack techniques for Retrieval-Augmented Generation systems including knowledge base poisoning
GitHub
infrastructure-security
Securing AI/ML infrastructure including model storage, API endpoints, and compute resources
GitHub
data-poisoning
Test AI training pipelines for data poisoning vulnerabilities and backdoor injection
GitHub
continuous-monitoring
Real-time monitoring and detection of adversarial attacks and model drift in production
GitHub
model-inversion
Privacy attacks to extract training data and sensitive information from AI models
GitHub
input-output-guardrails
Implementing safety filters, content moderation, and guardrails for AI system inputs and outputs
GitHub
testing-methodologies
Structured approaches for AI security testing including threat modeling, penetration testing, and red team operations
GitHub
secure-deployment
Security best practices for deploying AI/ML models to production environments
GitHub
prompt-hacking
Advanced prompt manipulation including direct attacks, indirect injection, and multi-turn exploitation
GitHub
llm-jailbreaking
Advanced LLM jailbreaking techniques, safety mechanism bypass strategies, and constraint circumvention methods
GitHub
adversarial-training
Defensive techniques using adversarial examples to improve model robustness and security
GitHub
automated-testing
CI/CD integration and automation frameworks for continuous AI security testing
GitHub
code-injection
Test AI systems for code injection vulnerabilities including prompt-to-code attacks and agent exploitation
GitHub
model-extraction
Techniques to extract model weights, architecture, and training data through API queries
GitHub
certifications-training
Professional certifications, CTF competitions, and training resources for AI security practitioners
GitHub
red-team-reporting
Professional security report generation, executive summaries, finding documentation, and remediation tracking
GitHub
security-testing
Comprehensive security testing automation for AI/ML systems with CI/CD integration
GitHub
prompt-injection-testing
Master prompt injection attacks, jailbreak techniques, input manipulation, and payload crafting for LLM security testing
GitHub
benchmark-datasets
Standard datasets and benchmarks for evaluating AI security, robustness, and safety
GitHub
git-basics
Git fundamentals - init, add, commit, status, log, and core concepts
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.