| Lakera AI Security Guide |
Basics of AI Security |
| Lakera LLM Security Playbook |
Framework, tools, datasets |
| AI security fundamentals |
This learning path helps you understand the basic concepts of AI security, the types of security controls that apply to AI systems, and the security testing procedures that you can implement in AI systems to increase the security posture of AI environments |
| Google's Secure AI Framework (SAIF) |
Google introduced the Secure AI Framework (SAIF), a conceptual framework to secure AI systems |
| Threat Modeling AI/ML Systems and Dependencies |
Supplements existing SDL threat modeling practices by providing new guidance on threat enumeration and mitigation specific to the AI and Machine Learning space |
| OWASP Top 10 for Large Language Model Applications |
- LLM01: Prompt Injection
- LLM02: Insecure Output Handling
- LLM03: Training Data Poisoning
- LLM04: Model Denial of Service
- LLM05: Supply Chain Vulnerabilities
- LLM06: Sensitive Information Disclosure
- LLM07: Insecure Plugin Design
- LLM08: Excessive Agency
- LLM09: Overreliance
- LLM10: Model Theft
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| The Five Most Common AI Model Risks and How to Prevent Them |
- Exposing code to the internet
- Vulnerabilities in AI packages
- Sensitive data in AI models
- Data leakage and tampering
- Exposed AI API keys
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