Evaluating the Security Posture of AI Systems:
Understanding exposure, attack surfaces and defense strategies
AI Security Assessment
Our AI Security Assessment provides an independent evaluation of potential risks associated with integrating AI systems, particularly large language models (LLMs) within applications or business environments.
This assessment focuses on uncovering vulnerabilities such as prompt injection, data leakage, unauthorised access, model manipulation, and abuse of AI-driven functionalities that could compromise the confidentiality, integrity, or availability of the underlying systems or data.
By simulating adversarial use cases, reviewing system configurations assessment aims ensure components securely implemented responsibly governed resilient emerging threat vectors unique AI technologies.
Our Methodology
Our AI Assessment typically is a 5-day deployment, combining cutting-edge tools with expert analysis from skilled testers who specialise in identifying vulnerabilities in AI systems.
Our AI Assessment methodology involves several key elements:
Threat Modeling for AI Components:
- Analyse potential misuse cases (e.g. prompt injection, data exfiltration). penetration testing
- Map trust boundaries and AI-specific attack surfaces.
- Evaluate access control and authorisation models around the AI system.
Prompt Injection and Input Manipulation Testing:
- Test for direct and indirect prompt injection vulnerabilities.
- Attempt jailbreaks and content policy bypasses.
- Assess handling of adversarial input or ambiguous queries.
Model Behaviour and Abuse Analysis:
- Evaluate potential for model misuse (e.g. generation of sensitive info or malicious content).
- Test for unintended memorisation or leakage of training data.
- Validate content filtering, safety mechanisms, and response consistency.
API and Endpoint Security Review:
- Assess authentication and rate-limiting of AI-related endpoints.
- Check for exposure of system prompts, internal functions, or debug information.
- Review logging, telemetry, and monitoring configurations.
Data Handling and Privacy Review:
- Evaluate data sent third-party LLM APIs (e.g., OpenAI, Azure OpenAI). Identify risks of sensitive data retention or cross-tenant leakage.
- Check anonymisation, redaction, or pre-processing safeguards.
Adversarial and Red Team Scenarios:
- Simulate real-world misuse (e.g., phishing automation, policy evasion).
- Assess system behaviour under fuzzed, multi-step or chained inputs.
To ensure accuracy, the assessment requires collaboration with clients to provide necessary architectural design details, threat models, access credentials, environment information, linked APIs. Working together enables a comprehensive evaluation of potential risks associated these components.
Conclusion
In today’s rapidly evolving technological landscape where AI systems are increasingly integral to business operations, ensuring security of these systems is critical. Protecting against emerging threats such as prompt injection attacks and model manipulation requires a proactive, comprehensive approach.
By leveraging the combined strengths of IGXGlobal and Fortis Cyber, we help organisations identify vulnerabilities, strengthen security controls, and safeguard critical assets. With actionable insights recommendations from expert analysis you can trust us to enhance resilience posture stay ahead threats within AI systems.