Security Testing for AI Agents and RAG Applications
We help AI SaaS companies detect prompt injection, data leakage, tool abuse, and unsafe agent behavior before enterprise customers do.
Practical AI security testing for teams shipping copilots, RAG systems, and agentic workflows into enterprise environments.
AI systems introduce risks traditional security testing does not fully cover.
Teams shipping AI copilots, RAG applications, and agents connected to tools, APIs, databases, and business workflows face risks traditional AppSec often misses — including prompt injection, system prompt leakage, sensitive data exposure, RAG data leakage, unauthorized tool execution, unsafe agent behavior, missing audit evidence, and weak permission boundaries.
Enterprise customers will ask security questions
If your AI feature touches customer data, internal documents, or business workflows, buyers will want evidence that it has been tested.
LLM risks are not the same as web app risks
Prompt injection, retrieval leakage, and tool abuse require a different testing methodology than traditional application security.
Unsafe agents can create real business impact
When AI systems can call tools, access files, or trigger workflows, security failures move from text output to real-world actions.
What We Test
Each LLM security assessment validates AI agent security against practical attack scenarios, unsafe behavior patterns, and enterprise security review expectations.
Prompt Injection Testing
We test whether malicious or indirect prompts can override system instructions or manipulate model behavior.
Jailbreak Resistance
We evaluate whether the application can be pushed outside its intended safety and business boundaries.
System Prompt Leakage
We check whether internal instructions, hidden prompts, policies, or configuration details can be extracted.
Sensitive Data Leakage
We test whether the AI system exposes private, internal, customer, or regulated data through responses.
RAG Security Testing
Our RAG security review evaluates whether retrieval pipelines expose unauthorized documents, sensitive chunks, or cross-tenant information.
Tool-Calling Abuse
We test tool-calling security to determine whether the model can be manipulated into invoking functions, APIs, or workflows in unintended ways.
Agent Permission Testing
We review whether the agent has excessive permissions or can perform actions without proper user confirmation.
Unauthorized Action Testing
We simulate scenarios where an attacker attempts to trigger emails, API calls, data changes, or workflow actions.
Logging and Auditability Review
We check whether security-relevant AI actions are logged well enough for investigation, compliance, and customer review.
Compliance Readiness Mapping
We map findings to practical governance expectations such as AI risk management, security review evidence, and internal controls.
Receive a practical security report your team can act on.
The deliverable is not a vague AI risk checklist. It is a concrete technical report for CTOs, CISOs, engineering teams, and enterprise customer security reviews.
Unauthorized Tool Execution
The agent could be manipulated into calling an internal workflow without sufficient confirmation.
Prompt Injection Through Retrieved Content
A malicious document inside the retrieval layer influenced the model's behavior.
System Prompt Disclosure
The assistant revealed internal behavioral instructions under specific adversarial prompts.
Built for AI teams selling into serious customers.
Not for you if
You only need generic chatbot testing, do not handle sensitive data, or are not planning to deploy AI features into real customer environments.
Security validation can reduce deal friction.
Enterprise customers increasingly ask how AI features are secured, tested, logged, and governed. If your product includes RAG, agents, or tool access, an AI SaaS security review can strengthen enterprise AI security by helping your team identify risks earlier, fix issues faster, and provide evidence during customer reviews.
Find risks before customers do
Identify AI-specific vulnerabilities during development, not during a customer security review.
Support enterprise security reviews
Provide concrete evidence of what was tested, what was found, and what was fixed.
Give engineers actionable remediation
Findings include technical context and specific remediation steps your team can act on.
Retest after model, prompt, or workflow changes
AI systems change frequently. Reassessment ensures new configurations do not reintroduce known risks.
Built by a cybersecurity practitioner.
Alex Romanov
Founder, AgentShield
A cybersecurity specialist with penetration testing experience, software development background, and hands-on work in AI automation. The focus is practical AI security validation: concrete attack scenarios, clear evidence, and remediation your engineering team can use.
Find AI security risks before your customers do.
Limited early assessment slots are available for AI SaaS teams building RAG apps, copilots, or AI agents.
Frequently Asked Questions
Request a Free AI Security Review
Tell us what you are building. We will reply with a practical initial scope and confirm whether the assessment is a good fit.
Company & direct contact
AgentShield is an AI security assessment service provided by SteikTech.
Security enquiries
kontakt@fynlite.comTelephone
+49 15567 271427Uilliam Leon Steik
Severinstraße 61
45127 Essen
Germany
Prefer to book directly?
Calendar booking will be available here soon. For now, submit the form and we will suggest a time.