COE Security’s AI and LLM Penetration Testing evaluates the security of your AI models and LLM-based applications, focusing on vulnerabilities, adversarial risks, and privacy concerns. Our service includes:

• Scoping and Planning: Collaborating with your team to define the testing objectives, identifying key AI models or LLMs in use, and specifying the particular risks and threats that need to be addressed.

• Adversarial Attacks Testing: Simulating adversarial machine learning attacks such as input manipulation (e.g., prompt injection for LLMs) to assess how vulnerable your models are to slight perturbations that could cause incorrect predictions or outputs.

• Data Poisoning Testing: Analyzing the possibility of data poisoning attacks, where malicious actors manipulate the training dataset to compromise the model’s performance or introduce biases that could lead to incorrect or harmful outputs.

• Model Inversion Testing: Evaluating the risk of model inversion attacks, which involve attempting to extract sensitive or private information from an AI model by exploiting its output, essentially reversing the model’s predictions to gather insights into the training data.

• API Security Assessment: Testing the security of APIs used by AI systems or LLMs, ensuring proper access control, authentication, and authorization mechanisms are in place to prevent unauthorized users from querying or manipulating the AI models.

• Bias and Fairness Testing: Assessing your AI model for biases or fairness issues, ensuring that the model produces equitable results for all users and does not unintentionally favor certain groups over others.

• Model Explainability Testing: Evaluating the interpretability and transparency of your AI model. We test how well users can understand the decision-making process behind the AI’s predictions, which is critical for detecting and mitigating risks associated with opaque decision-making.

• Privacy and Data Protection Testing: Assessing whether the AI model is inadvertently leaking sensitive or personally identifiable information through its predictions or outputs. We test the model for compliance with privacy regulations like GDPR and CCPA.

• Performance Under Adversarial Conditions: Testing the resilience of the model under stress or adversarial conditions, including challenging scenarios designed to confuse or trick the AI, and evaluating its robustness against unexpected inputs.

• Model Deployment Security: Reviewing the deployment environment for security risks, ensuring that model access is properly controlled, communications are encrypted, and that the environment is hardened against external threats such as unauthorized access or tampering.

• Integrity and Trustworthiness Assessment: Testing the integrity of the AI models and their training processes to ensure that they have not been tampered with or manipulated during development or deployment, and that they maintain a trustworthy performance.

• Security of Third-Party Integrations: Assessing the security of third-party libraries, frameworks, and services used in the AI development process, including examining the integration points to ensure they are not vulnerable to exploitation.

• Reporting and Remediation Support: Delivering a comprehensive report with findings, a risk assessment, and prioritized recommendations for securing your AI models and LLM applications. This includes guidance on how to mitigate the identified vulnerabilities and improve the overall security posture of your AI system.