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