As part of a gray box testing approach, we analyze your AI application holistically. This includes architecture documentation, prompt templates, and tool specifications. We test both via the graphical user interface and via REST API endpoints. On top of that, we evaluate administrative configuration interfaces such as various agent builder systems and model configurations for potential security risks.
Prompt injections are one of the biggest threats to AI applications. Attackers manipulate inputs to bypass security mechanisms, manipulate the intended behavior of AI agents, expose system prompts, or access sensitive data. We test your application specifically for these attack vectors. On top of that, we analyze user prompts and inputs (including modalities such as text, files, images), system and developer prompts, guardrails, and other safety layers. This also involves checking whether prompt templates, API keys, or sensitive customer data are unintentionally exposed in context or via tools.
Agentic AI systems are increasingly acting autonomously: they call APIs, use internal tools, delegate tasks to other agents, and make independent decisions. This autonomy enables new efficiency gains, but also poses significant security risks. The efficiency of agentic systems requires them to be equipped with extensive access rights. They often operate deep within the corporate infrastructure with access to sensitive data and critical services, and have extensive freedom of action. We therefore comprehensively review orchestration logic, planning loops, inter-agent messages (e.g., Agent2Agent or MCP), authorization concepts, as well as role and task delegations. This also involves identifying typical vulnerabilities such as agent goal hijacking, misuse of tools, identities, and privileges, as well as memory poisoning and rogue agents.
Recovery-augmented generation (RAG) expands AI systems with external data sources – and thus significantly increases the attack surface. We analyze the entire RAG pipeline: from document capturing and context embedding to the handling of personal data. At the same time, we examine your application’s output handling. Downstream consumers such as renderers, interpreters, shells, browsers, or other automation systems can be vulnerable to common attack vectors such as cross-site scripting (XSS), server-side request forgery (SSRF), remote code execution (RCE), and other attacks due to manipulated outputs.
We document all identified vulnerabilities in detail and prioritize them. You will receive a comprehensive report with specific recommendations for action – from quick wins to strategic improvements. Our results are based on established standards such as the OWASP LLM Top 10, the OWASP Top 10 for Agentic Applications, and the OWASP MCP Top 10. As the field of AI security is rapidly evolving, we continuously incorporate the latest research findings and new insights into best practices in our work. This ensures that our recommendations are always up to date and provide the basis for securing your AI applications in the long term.
AI applications often process highly sensitive company data and are increasingly making autonomous and privileged decisions. This makes AI applications extremely attractive targets for attackers.
At the same time, another novel and extensive attack surface is emerging: The OWASP LLM Top 10 show that risks such as prompt injections, insecure output processing, and excessive agency are among the most critical threats to modern AI systems. Agentic architectures add complex attack patterns that are unknown to classic application logic – such as manipulated decision-making processes, misused tool calls, or uncontrolled agent interactions.
Conventional security tests are insufficient in this regard. They are not designed to holistically evaluate the interaction of prompts, context, models, tools, and autonomous decisions. Our AI Agent Security Audit closes this gap. Through specialized tests, we reveal risks that remain undetected by classic penetration tests or code scans – well before they have a business-critical impact.
Don’t expose your AI applications and agentic systems to unnecessary risk. The AI Agent Security Audit by Oneconsult allows you to identify critical vulnerabilities in AI applications, agentic frameworks, and RAG architectures before attackers exploit them.
Contact us for a personalized consultation. Our experienced penetration testers will help you to test your AI systems in a targeted manner, assess risks realistically, and strengthen the security and resilience of your entire IT environment in the long term.
An AI Agent Security Audit is a targeted security review of AI applications such as chatbots, RAG systems, and agentic frameworks. Real attack techniques are used to specifically uncover exploitable vulnerabilities in prompts, agent logic, tool integrations, and system architecture.
With prompt injections, attackers deliberately manipulate inputs to an AI system in order to bypass security mechanisms. Doing so can expose system prompts, extract sensitive data, or trigger unauthorized actions. According to OWASP LLM Top 10, prompt injections are among the critical risks of modern AI applications.
No. Conventional vulnerability scanners detect known software vulnerabilities such as outdated libraries or misconfigurations. However, the risks of AI applications arise from the interaction of natural language, model behavior, context, and system architecture. Prompt injections, insecure agent logic, or manipulated RAG pipelines cannot be detected with signature-based scans. They require targeted, manual attack simulations by specialized security experts.
Yes. In several publicly documented cases, AI agents were compromised shortly after their release – for example, through exposed admin panels, stolen API keys, and active infostealer campaigns. Security researchers have also shown that coding agents could be completely compromised by targeted prompt injections – even to the point of executing malicious code. These incidents illustrate that agent-based AI systems require specialized security testing before they can be used productively.
Agentic AI systems can independently call up tools, access external data, and execute actions in connected systems. This autonomy significantly increases the attack surface. Typical risks include prompt injections via external data sources (e.g., manipulated documents or websites), uncontrolled tool usage with unintended side effects, excessive rights expansion due to a lack of least privilege principles, and data leaks via agent outputs. Since agent-based systems often link multiple components, individual vulnerabilities can add up to critical attack chains.
Without targeted security testing, this question cannot be answered reliably. Many AI applications are primarily optimized for their functionality, while security-related aspects such as prompt isolation, access controls, or output validation are often insufficiently tested. Our experience shows that even carefully developed systems often have exploitable vulnerabilities that only become apparent under real attack conditions. An AI Agent Security Audit creates transparency – with concrete findings, clear prioritization, and actionable recommendations.
Availability Monday to Friday 8:00 a.m. – 6:00 p.m (exception: customers with SLA – please call the 24/7 IRR emergency number).
Private individuals please contact your trusted IT service provider or the local police station.
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