LLM security testing

Key vulnerability categories

The OWASP Top 10 for Large Language Model Applications identifies the primary risk areas:

• Prompt injection – an attacker crafts input that overrides the LLM’s system instructions, causing it to perform unintended actions or reveal restricted information. This is the most prevalent and distinctive LLM vulnerability.
• Sensitive information disclosure – the model reveals training data, personally identifiable information, proprietary data, or system prompts in its responses
• Supply chain vulnerabilities – compromised training data, poisoned fine-tuning datasets, or vulnerable model dependencies introduce risks before the application is deployed
• Insecure output handling – LLM outputs are passed to downstream systems without validation, enabling injection attacks (SQL, XSS, command injection) through the model’s responses
• Excessive agency – the LLM is granted permissions or tool access that exceed what is necessary, enabling unintended actions if the model is manipulated
• Model denial of service – resource-intensive prompts designed to degrade model performance or exhaust compute resources

Testing methodology

LLM security testing typically combines automated and manual assessment:

• Prompt injection testing – systematic attempts to bypass system instructions, extract system prompts, and override safety filters using direct and indirect injection techniques
• Data extraction testing – probing for training data leakage, memorisation of sensitive information, and ability to reconstruct confidential data from model responses
• Privilege escalation testing – assessing whether the model can be manipulated to access tools, APIs, or data beyond its intended scope
• Output validation testing – verifying that downstream systems properly sanitise and validate LLM outputs before processing
• Guardrail bypass testing – evaluating the robustness of safety filters, content policies, and behavioural constraints under adversarial conditions

Cyberfort Group and LLM security testing

We deliver security assessments of LLM-powered applications, combining traditional penetration testing expertise with AI-specific testing methodologies. Our testers evaluate prompt injection resilience, data leakage risks, and guardrail robustness aligned with the OWASP Top 10 for LLMs and MITRE ATLAS frameworks.

Learn more about our AI security services →

Related terms

• ISO 42001 – the AI management system standard that provides governance frameworks for AI security
• EU AI Act – the EU regulation requiring security testing for high-risk AI systems
• CREST certification – the accreditation standard for penetration testing providers, applicable to AI security testing
• Red teaming – adversarial simulation methodology, increasingly applied to AI systems

External references

• OWASP: Top 10 for Large Language Model Applications – the primary vulnerability taxonomy for LLM security
• MITRE ATLAS – adversarial threat landscape for AI systems
• NCSC: Guidelines for Secure AI System Development – UK guidance for AI security

Frequently asked questions