Artificial intelligence-based applications introduce specific vulnerabilities that require dedicated testing methodologies. AI Application Testing (chapter 3.1 of the OWASP AI Testing Guide) provides a structured framework to verify the security, reliability, and compliance of AI applications, with particular attention to interactions between AI systems, end-users, and external data sources.

Perché testare le applicazioni AI

AI applications present unique attack surfaces: prompt injection, leakage of sensitive data through the model, unsafe or biased outputs, and uncontrolled agentic behaviors. Without specific checks, these vulnerabilities can compromise data security, operational reliability, and regulatory compliance. AI Application Testing allows for identifying and mitigating these risks before the application reaches end-users.

Aree di verifica dell’AI Application Testing

Resistance to prompt manipulations

Natural language-based AI systems can be manipulated through inputs designed to alter the intended behavior. Checks include:

• AITG-APP-01: Testing for Prompt Injection – Verifies resistance against manipulated inputs attempting to override system instructions
• AITG-APP-02: Testing for Indirect Prompt Injection – Identifies vulnerabilities arising from external content controlled by attackers

Protection of sensitive information

AI applications can expose sensitive data through various channels. Tests verify:

• AITG-APP-03: Testing for Sensitive Data Leak – Detects leaks of confidential information in model responses
• AITG-APP-04: Testing for Input Leakage – Verifies that user inputs are not exposed to other users or systems
• AITG-APP-07: Testing for Prompt Disclosure – Evaluates the risk of exposure of system instructions

Output security and quality

AI-generated outputs must be safe, accurate, and aligned with goals. Checks cover:

• AITG-APP-05: Testing for Unsafe Outputs – Identifies outputs that can cause damage or unsafe behaviors
• AITG-APP-10: Testing for Content Bias – Detects systematic biases that compromise fairness and reliability
• AITG-APP-11: Testing for Hallucinations – Verifies the model’s tendency to generate false or invented information
• AITG-APP-12: Testing for Toxic Output – Tests the system’s ability to prevent offensive or harmful content

Agentic behavior control

AI systems with agentic capabilities require specific checks on operational limits:

• AITG-APP-06: Testing for Agentic Behavior Limits – Evaluates the operational boundaries of AI agents and their ability to respect defined constraints
• AITG-APP-13: Testing for Over-Reliance on AI – Verifies that the application does not delegate critical decisions without adequate human supervision

Transparency and interpretability

The ability to explain AI decisions is fundamental for compliance and trust:

• AITG-APP-14: Testing for Explainability and Interpretability – Verifies that the system provides understandable explanations of its decisions

Protection against embedding and model attacks

AI applications can be vulnerable to attacks aimed at extracting or manipulating internal components:

• AITG-APP-08: Testing for Embedding Manipulation – Identifies vulnerabilities in the management of vector embeddings
• AITG-APP-09: Testing for Model Extraction – Evaluates the risk of model theft through repeated queries

The OWASP AI Testing Guide organizes AI security checks into four complementary areas: the AI Application Testing handles application interactions and outputs, the AI Model Testing evaluates model robustness and alignment, the AI Infrastructure Testing verifies deployment infrastructure security, and the AI Data Testing protects the quality and confidentiality of data used by the system.

Benefici per l’organizzazione

Systematically implementing AI Application Testing allows for:

• Reducing security risks before deployment to production
• Protecting sensitive data from leaks through the AI application
• Ensuring reliable outputs aligned with business objectives
• Increasing confidence in deployed AI among stakeholders and customers
• Complying with regulatory requirements on privacy, security, and transparency of AI systems
• Preventing reputational damage derived from uncontrolled AI behaviors

Come supporta ISGroup

ISGroup offers specialized services for AI application security:

• Web Application Penetration Testing – Manual verification of AI applications to identify specific vulnerabilities
• Code Review – Source code analysis to identify bad practices and vulnerabilities not exposed during black-box testing
• Vulnerability Management Service – Continuous monitoring of vulnerabilities in AI applications in production
• Training – Dedicated paths for developers and security teams on AI security and the OWASP AI Testing Guide

Domande frequenti

• When should AI Application Testing be performed?
• AI Application Testing should be integrated into the development cycle: during design to define security requirements, before deployment to validate implementations, and periodically in production to monitor for new vulnerabilities or anomalous behaviors.
• What skills are required to perform AI Application Testing?
• Skills in application security, knowledge of AI architectures (LLM, RAG, agents), and familiarity with prompt injection techniques and AI-specific attacks are required. The team must understand both traditional security aspects and emerging AI system vulnerabilities.
• Does AI Application Testing replace traditional application security testing?
• No, AI Application Testing is in addition to traditional tests. AI applications require specific checks for unique vulnerabilities (prompt injection, data leakage, hallucinations) that traditional tests do not cover, but they also maintain classic attack surfaces that must be tested with established methodologies.
• How is the effectiveness of AI Application Testing measured?
• Effectiveness is measured through specific metrics: success rate of prompt injection attacks, percentage of sensitive data leaks detected, accuracy in detecting hallucinations and toxic outputs, and capacity to identify violations of agentic limits. Results must be documented and tracked over time.
• Which regulations require AI Application Testing?
• The European AI Act requires safety assessments for high-risk AI systems. GDPR imposes protection of personal data even when processed by AI. Sectoral regulations like NIS2, DORA (for the financial sector), and healthcare regulations may require checks on the security and reliability of the AI applications used.
• How often should AI Application Testing be repeated?
• The frequency depends on the usage context and the rate of change: production applications should be tested periodically (quarterly or semiannually) and whenever they are updated. Applications handling sensitive data or operating in critical contexts require more frequent checks and continuous monitoring of new AI vulnerabilities.

AI Application Testing represents a strategic investment for organizations that develop or use artificial intelligence-based applications. Systematically verifying the security, reliability, and compliance of AI applications reduces operational risks and increases confidence in deployed AI, while protecting sensitive data and company reputation.