AI Security Assessment

What is an AI Security Assessment?

An AI Security Assessment – also called an AI pentest, LLM pentest or AI security test – examines AI applications for vulnerabilities such as prompt injection, data exfiltration or misuse of connected tools, and assesses the risk in the enterprise context.

Applications of artificial intelligence (AI) in enterprises range from chat applications based on large language models (LLMs) and retrieval-augmented generation (RAG) systems with access to internal knowledge sources to AI agents that invoke external tools and APIs. These systems increasingly process business-critical data and are integrated into existing business processes and IT landscapes.

From a security perspective, the relevant concern is not the language model in isolation but the full AI application: model, context construction, RAG pipeline, orchestration logic, tools and APIs, permissions, data flows, output processing, and operating environment. We therefore test the concrete implementation in its enterprise context, not just the model. This shows which attacks are actually possible, which actions the system can trigger, and what business impact could result.

Many LLM-based AI applications also behave non-deterministically. Their outputs are context-dependent and only partially predictable, as a significant part of the control logic resides within the language model itself. These model- and context-specific risks require specialized assessment methods. In real projects, particularly critical risks often arise from overprivileged agents and integrations, for example when an assistant is given access to entire SharePoint areas, HR databases, or other sensitive data sources. At the same time, classical vulnerabilities remain relevant: insecure APIs, broken access control, XSS, SSRF, remote code execution, misconfigurations, or insufficient logging can also be exploitable in AI systems and increase the impact of AI-specific attacks.

AI Security Assessment Process: Methodology & Approach

Our AI Security Assessment provides a systematic evaluation of your AI-based applications with regard to AI-specific vulnerabilities and misconfigurations. Building on established frameworks such as the OWASP Top 10 for LLM Applications 2025, the OWASP Top 10 for Agentic Applications 2026, and the MITRE ATLAS knowledge base, we combine automated attack techniques with manual testing by our assessors.

The assessment typically begins with a threatanalysis workshop in which we analyze the AI application’s architecture, data flows, and trust boundaries and derive specific threat scenarios. A clear distinction between the different system types and their architectural components is essential: LLM-based chat applications, RAG systems with connected data sources, and agentic systems with tool access and autonomous decision-making each present different attack surfaces and risk profiles.

A central element of the threat analysis is examining the information flow along the entire processing chain. In particular, we identify the trust boundaries between system components: where untrusted inputs such as user queries, uploaded documents, or externally retrieved content enter the processing pipeline, how and where this data is merged with the model context and, most importantly, which data flows and decisions take place within the LLM context and can therefore be influenced by an attacker through manipulated inputs. Based on this analysis, we derive concrete attack scenarios and prioritize the resulting test cases by business context and technical risk.

Depending on the type of application or system, the relevant threats and the agreed time budget, the assessment is either designed to be as comprehensive as possible or follows a risk-based approach comparable to a penetrationtest . In the risk-based approach, the focus is on particularly security-critical or exposed areas and the scope of the test is determined by the time budget agreed upon in advance.

Building on the results of the threat analysis, we perform both automated and manual analyses to identify vulnerabilities. As a first step, we deploy specialized tools that automatically execute a broad range of attack variants against the AI application, across different input modalities, including text and, where in scope, files and images. These automated tests cover the known attack categories. However, since they are inherently limited to predefined patterns, our assessors complement the results with targeted manual analysis. They evaluate findings in the context of the specific system architecture, examine orchestration logic for logical weaknesses, and conduct application-specific attack scenarios that go beyond standardized testing catalogs.

Because many LLM-based AI systems behave non-deterministically, we execute attacks multiple times and in variations to assess actual exploitability. We evaluate both the reproducibility and the consistency of identified vulnerabilities, which provides a sound basis for the risk rating. A gray- or white-box approach is recommended.

AI Security Assessment and Relevant Regulations & Standards

The security of AI applications is addressed by several regulations and standards. An AI Security Assessment supports you in demonstrably meeting the technical requirements for robustness and cybersecurity, particularly for high-risk AI systems:

• EUAIAct : The EU regulation on artificial intelligence defines risk-based requirements for AI systems, including transparency obligations, technical documentation, and requirements for robustness and cybersecurity. An AI Security Assessment provides technical evidence for the robustness and cybersecurity requirements. For high-risk AI systems, these requirements are mandatory pursuant to Art. 15 of the regulation.

• OWASPTop10forLLMApplications2025 : The internationally recognized catalog of the most critical security risks for AI language models is a key basis of our assessment methodology. It covers, among others, Prompt Injection, Sensitive Information Disclosure, Supply Chain Vulnerabilities, and Excessive Agency.

• OWASPTop10forAgenticApplications2026 : This complementary catalog specifically addresses the security risks of agentic AI systems, including Agent Goal Hijack, Tool Misuse, and Memory & Context Poisoning.

• OWASPMCPTop10(Beta) : This catalog, currently in development, addresses the specific security risks of the Model Context Protocol (MCP), an emerging protocol increasingly used for tool integration in AI systems. Identified risk categories include Tool Poisoning, Command Injection, Privilege Escalation via Scope Creep, and Context Injection.

• MITREATLAS : ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a knowledge base of adversary tactics, techniques, and case studies for AI systems and serves as a reference for systematic assessment.

Assessment Results: Risk Evaluation & Security Measures

As a result of the assessment, we will provide a detailed report. Depending on the type and scope of the project, the final report will include the following parts:

• Management summary with a description of the results and the security level
• Description of the project approach, scope, schedule and methodology
• Detailed description of identified vulnerabilities in order to understand underlying issues and to enable reconstruction of possible attacks (where necessary with proof-of-concept implementation)
• Detailed description of the iterative exploitation process when using chained vulnerabilities
• Risk assessment of identified vulnerabilities taking into account the IT environment or the application context (risk classification: low, medium, high, critical)
• Description of measures to remedy the vulnerabilities
• If necessary, a description of higher-level strategy, concept and process-related measures or optimization suggestions

If desired, the following points can additionally be integrated into the final report:

• A detailed overview of the AI application architecture and data flows
• A threat model specific to the AI components and their integration
• Recommendations for secure development and operational practices for AI applications
• Prioritized remediation recommendations based on a defense-in-depth approach