AI Security and LLM Pentesting
AI and LLM penetration testing — prompt injection, model poisoning, data extraction, and how to assess AI-powered features for vulnerabilities.
2026-09-03
As organizations embed AI and large language models (LLMs) into their products, new attack vectors emerge. Traditional web application testing does not cover AI-specific vulnerabilities — which means organizations deploying AI features need specialized security testing.
AI-specific vulnerabilities
| Vulnerability | What it means | Risk |
|---|---|---|
| Prompt injection | Attacker overrides system prompts to manipulate LLM output | High — can bypass content filters and extract data |
| Training data extraction | Attacker recovers sensitive data used in model training | Critical — PII, proprietary data exposure |
| Model poisoning | Attacker corrupts training data to influence model behavior | Critical — backdoored responses |
| Insecure plugin design | Third-party plugins with excessive permissions access | High — data exfiltration via plugins |
| Sensitive data leakage | LLM inadvertently outputs sensitive information in responses | High — compliance violation risk |
| Denial of service | Complex prompts designed to exhaust compute resources | Medium — cost and availability impact |
The OWASP Top 10 for LLM Applications
The OWASP Top 10 for LLM Applications has emerged as the standard reference for AI application security. It covers prompt injection, data leakage, supply chain vulnerabilities, and plugin security.
What an AI pentest covers
An AI-focused penetration test should include:
- System prompt analysis. Can the system prompt be extracted or overridden through crafted inputs?
- Output validation. Does the application properly validate and sanitize LLM outputs before displaying them?
- Plugin and tool assessment. What permissions do AI plugins have? Can they be abused?
- Data classification. Does the AI have access to data it should not? Are responses filtered for sensitive content?
- Model access controls. Can unauthorized users query the model or access training data?
Compliance considerations
Regulatory frameworks are catching up. The EU AI Act will require risk assessments and security testing for high-risk AI systems. Organizations deploying AI features should document their testing methodology and results as part of their AI governance program.
Secure your AI-powered features
Affordable Pentesting offers AI security assessments covering LLM applications and AI-powered features. Start a pentest or contact us to discuss an AI-specific engagement.
Related reading: API Security Penetration Testing