Introduction
Artificial intelligence technologies are advancing rapidly, and large language models (LLMs), autonomous agents and other AI systems are becoming increasingly prevalent in businesses. However, these innovations bring new security challenges. Attacks such as prompt injection, manipulation of training data or exfiltration of sensitive information can compromise the privacy and integrity of systems. Small and medium‑sized enterprises (SMEs) adopting AI solutions in their products or processes need to understand these issues in order to protect their own data and that of their clients.
Security Problems and Risks for LLMs and AI Models
The risks facing language models and intelligent agents differ from those of traditional applications. Models can be coaxed into producing unwanted or dangerous output through carefully crafted input. This phenomenon – prompt injection – may cause sensitive data to be revealed or unauthorised actions to be executed in an automated workflow. There is also the danger of adversarial attacks: by slightly altering input data, an attacker can cause the model to give incorrect or harmful responses. If training data contains proprietary or personal information, there is a risk that the LLM might “leak” it during use. Finally, the model supply chain – libraries, API providers and datasets – can present an attack surface if the provenance and integrity of each component are not verified.
Who Can Be Affected?
The consequences of a security flaw in an AI‑based system can affect a wide range of stakeholders. SMEs that use language models to automate customer service or analyse large volumes of data risk breaching customer confidentiality. Developers and IT teams integrating third‑party models are exposed to supply‑chain vulnerabilities. End users can suffer if AI is manipulated to provide misleading advice or to disseminate offensive or fraudulent content. Partners and vendors interacting with the company’s APIs might also be caught up in an incident if protective measures are inadequate.
What Should Be Done
To mitigate these risks, organisations need a holistic approach to securing AI‑based systems. Start with a risk assessment to understand where and how models are used, what data they handle and what the impact of misuse could be. Implement strict access controls on APIs and models, use strong authentication and apply the principle of least privilege. Continuously monitor input sent to models to detect anomalous patterns or potential injection attempts. Logging and auditing of AI interactions are essential for reconstructing events if an incident occurs. Choose model and dataset providers carefully; verify licences and data origins to avoid copyright violations or the inclusion of confidential information. Training remains a pillar: developers should be familiar with model peculiarities and potential attack vectors, while staff using AI solutions should understand the system’s limits and follow best practices.
Zerberos and AI Security Reviews
Conducting penetration tests and security reviews on AI systems may seem unusual, but it is a crucial step to ensure the resilience of emerging technologies. Zerberos offers security reviews specifically tailored for LLMs, AI models and autonomous agents. These audits analyse how the model handles user input, examine the quality of training data, scrutinise the underlying infrastructure and evaluate integrations with other systems. Through systematic analysis, hidden vulnerabilities are identified and practical recommendations for mitigating risks are provided. For an SME, investing in such an assessment means preventing reputational and financial damage and ensuring that the adoption of AI is secure and compliant.
Conclusion
Deploying large language models and intelligent agents opens up tremendous opportunities, but requires special attention to security. Issues such as prompt injection, adversarial attacks and data leakage can undermine customer trust and cause substantial harm. All parties – SMEs, developers, end users and partners – may be affected, so protection must be planned comprehensively. Mapping risks, applying access controls, monitoring inputs, training staff and choosing reputable vendors are fundamental steps. Engaging specialists like Zerberos for penetration tests and dedicated reviews provides an independent, in‑depth view of the security posture of your AI systems. With a proactive approach, businesses can harness the potential of AI while minimising its dangers.