More
Choose
Useful links
Germany

Siemensstraße 23,
76275 Ettlingen
+49(0) 7243-718 77 55

Back to top

LLM Security

Our services

Secure your LLM models and systems:

As Large Language Models (LLMs) rapidly transform everything from customer support to data analytics, their security risks are often underestimated. These powerful but surprisingly manipulable AI systems can be tricked through prompt injection or malicious inputs, potentially compromising entire infrastructures and leaking sensitive data. At Auxilium Cyber Security GmbH, we offer specialized LLM pentesting services to identify hidden vulnerabilities, and then provide tailored defense strategies based on our findings—ensuring your AI-driven operations remain secure, compliant, and resilient.

The Risks: How LLMs Get Hacked

LLMs and AI-driven systems are highly susceptible to emerging attack vectors, often overlooked in traditional security frameworks. Without proper security measures, these vulnerabilities can be exploited to compromise entire infrastructures.

Common AI Security Threats

Prompt Injection Attacks

Malicious actors craft inputs that trick AI systems into producing unintended or harmful outputs, effectively manipulating the AI's behavior.

Data Extraction & Leakage

Attackers exploit AI systems to retrieve sensitive information, such as personal data or confidential business details, that should remain private.

Jailbreaking & Filter Bypassing

Techniques used to override an AI's built-in safety measures, enabling the system to perform actions or provide information it was designed to restrict.

Model Poisoning

Introducing malicious data during the training phase of an AI model, causing it to learn incorrect behaviors or make faulty decisions.

System Exploitation

Leveraging vulnerabilities in AI systems to gain unauthorized access to broader computer networks, potentially leading to data breaches or system failures.

What Needs Protection?

Safeguarding your AI assets is paramount, whether you're developing standalone models or integrating AI into complex systems, understanding what requires protection is the first step toward robust security.

Standalone LLMs or AI Models

These are self-contained AI models, such as language models for text generation and analysis. Securing them is challenging since their vulnerabilities depend on their deployment context and how they interact with other systems. While they are rarely used in complete isolation, they should still be tested for resilience against prompt injection, ensuring ethical and safe outputs, robust data leakage prevention, and protection against model poisoning.

Get in touch

AI-Based Systems

AI-powered applications integrate models with assistants and external data, making them vulnerable to indirect prompt injection, data exfiltration, and cross-component attacks. Security testing should verify that the AI operates within its intended scope, all system components are properly monitored, and interactions between them are controlled to prevent unauthorized access and unintended behavior.

Get in touch

AI Agents

AI agents are particularly critical as they can execute real-world actions and often have broad access to sensitive data and systems. Given their power and autonomy, they must be rigorously tested to ensure they only operate within their intended scope. A secure design should enforce strict tool usage policies and strong protections against prompt injection to prevent agents from being manipulated by attackers into performing unintended actions.

Get in touch

Retrieval-Augmented Generation (RAG) Systems

Since these systems pull external information to enhance responses, they require strict access control to vector stores. Ensuring the integrity of retrieved data is crucial to prevent adversarial modifications that could distort outputs or introduce false information.

Get in touch

Our Security Assistance Process:
From Pentesting to Protection

Ensuring the security of AI systems requires a structured, multi-step process that identifies vulnerabilities, applies security constraints, and implements defense mechanisms to safeguard against real-world threats. Our LLM Security Assistance Process follows a systematic approach:

Pentest

1.
Identifying System Components & Interactions

Before securing an AI system, we analyze its architecture, components, and external interactions. This step helps us understand potential attack surfaces, such as LLM APIs, user inputs, vector databases (RAG systems), and AI-driven automation processes. By mapping out these components, we pinpoint where security threats may arise.

2.
Identifying Security Constraints & Gaps

Once we understand the attack surface, we assess the necessary security constraints that should be in place to protect the system. This includes:

  • Prompt Injection Protections
    Mechanisms to filter or neutralize malicious inputs.
  • Data Access & Leakage Controls
    Ensuring sensitive information is not exposed.
  • Authentication & Access Restrictions
    Preventing unauthorized interactions or system abuse.
  • Model Poisoning Defenses
    Ensuring external data cannot be used to corrupt AI behavior.
3.
Testing the Strength of Existing Defenses

If security constraints are already in place, we evaluate their effectiveness by testing how well they hold up against real-world attacks. This includes:

  • Adversarial Testing for Prompt Injection
    Attempting to bypass restrictions using malicious inputs.
  • Jailbreak Attempts & Filter Bypassing
    Testing if safeguards can be overridden to make the AI produce unintended outputs.
  • Access Control Testing
    Checking for authentication bypasses or privilege escalation flaws.
  • Sandbox Evasion Techniques
    Ensuring the AI system cannot be tricked into revealing restricted information.

This step validates whether existing defenses are effective or if they need further reinforcement.

Strengthening AI Security

Secure AI Design: Building AI with Security-First Principles

Beyond immediate defenses, we help businesses design AI systems with security at their core. Secure AI development is about preventing vulnerabilities before they arise, rather than just patching issues after they’re found.


Our secure AI design strategies include:

  • Minimizing AI Privileges
    Ensuring AI systems operate with only the access they truly need.
  • Using Deterministic Functions for Critical Tasks
    Offloading security-sensitive functions to traditional, verifiable systems instead of relying on AI.
  • Implementing Data Access Controls
    Protecting AI-integrated systems from unauthorized access to sensitive databases and vector stores.
  • Hardening AI Decision-Making Processes
    Making AI-driven decisions more predictable and resistant to manipulation.

Setting up effective AI guardrails requires expertise and a deep understanding of the specific application context. Security cannot be applied in a generic way: each system has unique permitted and unpermitted actions, and security measures must be tailored accordingly. Identifying the right constraints requires test insights and evaluations to adapt to specific threats and AI behaviors.

Why choose us?

At Auxilium Cyber Security, we bring together deep expertise in both AI and cybersecurity, ensuring that your AI models, systems, and agents are secured against real-world threats. Our team has extensive experience in testing AI applications, identifying vulnerabilities, and implementing effective security defenses.

We go beyond traditional security testing by investing in AI security research, developing advanced automated security tools, and creating hands-on AI security labs to continuously refine our approach. Our proven methodology, combining combining threat modeling, pentesting, and defense implementation, ensures that AI-driven systems remain resilient, compliant, and secure.



GET IN TOUCH


AI Security Labs - Learn & Practice LLM Security

Our AI Security Labs is an educational platform designed for hands-on LLM security training. It features an AI agent (chatbot) that interacts with students, loads course materials into a vector store, and assists with learning. The platform also includes an AI-driven testing system that generates exams, evaluates responses, and assigns grades automatically.

What makes this lab unique is that it contains realistic vulnerabilities, simulating the actual risks that arise when security is not properly implemented in AI systems. It provides a practical environment for security researchers, ethical hackers, and AI enthusiasts to test prompt injection, data extraction, jailbreak exploits, AI manipulation, and more. By experimenting in a controlled setting, learners can develop practical pentesting skills and gain a deeper understanding of LLM security risks and defenses.

Get in touch!