LLM Testing Findings

• Authorization Bypass through User Controlled Parameter

  Discusses vulnerabilities where authorization can be bypassed through parameters controlled by the user.

• Data Leakage

  Addresses the potential for sensitive data to be unintentionally exposed or accessed.

• Data Poisoning

  Explores vulnerabilities related to malicious alterations of training data to compromise a model.

• Improper IAM Models

  Highlights the risks and vulnerabilities associated with improper implementation of Identity and Access Management models.

• Inadequate Anomaly Detection

  Sheds light on the shortcomings of anomaly detection systems in identifying unusual or suspicious behavior effectively.

• Inadequate DR Plan

  Discusses the consequences and risks of having an inadequate Disaster Recovery plan for LLM systems.

• Inadequate ML Grounding

  Points out the issues with insufficient grounding or baseline understanding in machine learning applications.

• Insecure Model Arch

  Focuses on vulnerabilities due to insecure architectural designs in LLM models.

• Insufficient Input Validation

  Details the dangers of not adequately validating inputs, leading to potential security and operational risks.

• Insufficient Logging MLOps

  Highlights the deficiencies in logging practices within Machine Learning Operations, impacting monitoring and auditing capabilities.

• Insufficient Output Sanitization Content Filtering

  Discusses the lack of proper output sanitization and content filtering, leading to potential exposure of inappropriate or harmful content.

• Lacking Env Segmentation Gap MLOps

  Points out the risks associated with insufficient environmental segmentation in MLOps practices.

• Lacking Output Disclaimer

  Addresses the importance of including disclaimers for outputs to manage expectations and clarify limitations.

• Lacking Rate Limiting

  Discusses the security and performance implications of not implementing rate limiting controls.

• Lacking User Feedback Mechanism

  Highlights the drawbacks of not having mechanisms for users to provide feedback on LLM applications.

• Prompt Injection

  Explores the security vulnerabilities associated with prompt injection attacks, where malicious inputs can manipulate model behavior.

• Unrestricted Task Execution

  Discusses the risks and potential consequences of allowing unrestricted task execution within LLM applications.

• Violation Differential Privacy

  Addresses issues related to the violation of differential privacy principles, compromising individual data privacy.