AI Red Teaming Services

Our AI Red Teaming Services

Adversarial Attack Simulation

We simulate sophisticated adversarial attacks to identify vulnerabilities in your AI models, including data poisoning, model inversion, and evasion attacks.

Bias & Fairness Testing

Comprehensive testing to uncover and mitigate algorithmic bias, ensuring your AI systems operate fairly and equitably across all user groups.

Robustness & Reliability Analysis

Evaluating the resilience of your AI agents to unexpected inputs and operational stresses, ensuring consistent and reliable performance.

Privacy Vulnerability Assessment

Identifying potential data leakage risks and privacy concerns within your AI systems, protecting sensitive information and ensuring compliance.

Ethical AI & Alignment Testing

Assessing AI behavior for unintended consequences and alignment with human values, preventing harmful outputs and promoting responsible AI.

Compliance & Regulatory Audits

Ensuring your AI deployments adhere to relevant industry standards, data protection laws, and emerging AI regulations.

Our Proven Red Teaming Methodology

Our red teaming approach for AI agents is built upon a robust framework, integrating the best practices from two leading cybersecurity methodologies: Microsoft's Responsible AI principles and the OWASP Top 10 for Large Language Models (LLMs). This hybrid methodology ensures a comprehensive and cutting-edge assessment of your AI systems.

Microsoft's Responsible AI Principles

Fairness: Identifying and mitigating bias in AI decision-making.
Reliability & Safety: Ensuring AI performs consistently and safely under various conditions.
Privacy & Security: Protecting sensitive data and defending against malicious attacks.
Inclusiveness: Designing AI that empowers everyone and engages people.
Transparency: Making AI systems understandable and accountable.
Accountability: Establishing clear lines of responsibility for AI outcomes.

OWASP Top 10 for LLMs

Prompt Injections: Exploiting vulnerabilities through malicious inputs.
Insecure Output Handling: Preventing dangerous or misleading AI responses.
Training Data Poisoning: Detecting and preventing manipulation of training data.
Model Denial of Service: Assessing resilience against attacks aimed at disrupting AI availability.
Supply Chain Vulnerabilities: Identifying risks in third-party components and data.
Sensitive Information Disclosure: Preventing the leakage of confidential data.
Insecure Plugin Design: Auditing external integrations for security flaws.
Excessive Agency: Controlling AI's autonomous capabilities to prevent unintended actions.
Overreliance: Mitigating risks from excessive dependence on AI systems.
Model Theft: Protecting proprietary AI models from unauthorized access.

Safeguarding Your AI: Advanced Red Teaming Services