Benefits of an AI security audit

Preventing prompt injections and sensitive information leaks are some of the benefits of an AI security audit for companies that use this technology

At the beginning of the year, a group of German researchers published a research article in Nature warning that visual language models (VLMs), which are very useful in the medical field, could be compromised by malicious prompt injection attacks. This could lead to these models producing results that are detrimental to medical practice and patient health.

This scientific study has been followed by other research carried out by cybersecurity experts who warn of the security risks associated with AI and defend the benefits of an AI security audit, which enables the detection of vulnerabilities and weaknesses before they can be successfully exploited.

For example, in August, research was published that warned of the possibility of injecting malicious prompts through documents uploaded to ChatGPT and, thanks to its connection to storage services such as Google Drive or SharePoint, getting the AI to provide hostile actors with the API keys for these services’ accounts. Similarly, researchers warned of exploitable weaknesses in other solutions, such as Copilot Studio, a platform that allows companies to create their own AI agents, or Cursor.

These cases demonstrate that cybercriminals are refining their techniques, tactics, and procedures to target natural language models and AI tools already utilized by millions of companies worldwide.

The interconnection of basic business tools with AI systems and the use of solutions such as conversational chatbots are boosting companies’ profitability and productivity. Still, they also mean an expansion of their attack surface.

That is why conducting an artificial intelligence security audit, also known as AI penetration testing, has become essential for thousands of companies.

1. What are the main risks of AI for companies?

The OWASP foundation, a global methodological benchmark in cybersecurity, has compiled a top 10 list of vulnerabilities in LLM applications to help prevent cyberattacks and is also preparing a guide for conducting AI security audits. What risks are included in the 2025 version of this list?

1. Prompt injections. As we saw in the previous examples, this is the main threat looming over AI. This technique aims to compel applications to perform the actions desired by the malicious actor, thereby gaining access to confidential company information. Malicious prompts can be found in documents, legal clauses, or in the HTML of emails.
2. Disclosure of confidential information: financial data, business strategy, legal documents, security credentials, etc.
3. The supply chains of large natural language models are vulnerable to risks that can lead to systems returning biased data, failing, or experiencing security breaches.
4. Poisoning of data and natural language models, introducing backdoors and biases with the aim of compromising the security of the models and deteriorating their performance.
5. Inadequate handling of results. Poor validation and sanitization of natural language model results before transferring them to other components or systems can open the door for malicious actors to escalate privileges or execute code remotely on backend systems.
6. Excessive autonomy. AI developers grant systems autonomy, enabling them to interact with other systems and perform actions in response to requests. Excessive independence means that a large natural language model that functions improperly can perform harmful actions.
7. System prompt leakage. This vulnerability occurs when the prompts or instructions used to direct the behavior of the AI system unintentionally contain confidential information that allows attacks against the model.
8. Vector and embedding weaknesses. This vulnerability can affect systems that use augmented generation by retrieval with large natural language models. It focuses on how vectors and embeddings are generated, stored, or retrieved, as these processes can be exploited to inject malicious content or manipulate system results.
9. Misinformation. The generation of erroneous or misleading information by models directly affects the applications that depend on them and can cause companies to suffer reputational damage and even legal liability.
10. Unlimited consumption. This vulnerability consists of the AI application allowing users to make excessive and uncontrolled inferences that can be part of a denial-of-service attack, cause economic losses due to system interruptions, degrade its performance, or facilitate model theft.

Addressing these vulnerabilities is a key benefit of an AI security audit.

2. Main objectives of an AI security audit

The threat landscape we have outlined is also constantly evolving. After all, large natural language model applications:

• It is a technology that has become widely used in recent years.
• They are still being refined, and improvements are constantly being made to AI models and systems.

This means that companies using AI to carry out their operations must conduct an AI security audit that takes into account the latest technological developments and the techniques, tactics, and procedures employed by malicious actors. What are the objectives of an AI security audit?

1. Ensure the security of business operations involving the use of AI.
2. Detect specific vulnerabilities in AI systems, including, as mentioned above, prompt injections and confidential information leaks.
3. Mitigate the detected vulnerabilities, prioritizing those that pose the greatest risk to the business, as they affect critical operations or have public proof of concept.
4. Ensure that large natural language models operate within the expected parameters, ensuring that information security and company operations are guaranteed.

3. What is an AI security audit?

An AI security audit consists of five main phases:

1. Preliminary evaluation of the AI system. One of the benefits of an AI security audit is that the experts in charge perform an architecture review to analyze the structure of the natural language model, including data sources used, model training process, and other relevant aspects. Additionally, they identify critical points in the system from a cybersecurity perspective, including user interfaces and integration with different tools.
2. Performing AI pentesting. The experts:
   • i. Simulate prompt injection attacks, the main risk to AI systems, to assess the model’s level of resilience to malicious inputs that seek to undermine its operation or force it to perform improper actions.
   • ii. Analyze how the system handles sensitive data to verify that confidential information is not exposed in the system’s responses or interactions.
3. Review of configurations and dependencies. Another benefit of an AI security audit is that it helps prevent supply chain attacks. To do this, it is essential to analyze the third-party components (libraries, modules, etc.) integrated into AI systems to identify known vulnerabilities present in them. Additionally, the professionals in charge of the audit verify that the security configuration is correctly implemented and adheres to best practices in Artificial Intelligence cybersecurity.
4. Preparation of a report documenting the vulnerabilities detected and their potential impact, and presenting a mitigation plan with specific actions to remedy the weaknesses, prioritizing them according to their level of risk. Thus, among the benefits of an AI security audit is not only the identification of deficiencies, but also an action plan to resolve them.
5. Ongoing advice to the company to help it implement security updates that address new vulnerabilities and train all professionals so that they can prevent security incidents affecting AI systems.

4. What are the most relevant benefits of an AI security audit?

Given what we have discussed in this article, we can systematize the main benefits of an AI security audit:

1. It enables organizations to safeguard their sensitive information, including customer data, business strategies, financial information, and intellectual and industrial property. Successful exploitation of vulnerabilities in AI systems can facilitate unauthorized access to this information, the theft of extremely valuable data, or its manipulation.
2. It helps to ensure that a large natural language model works correctly, without service interruptions or unexpected AI behavior.
3. It curbs supply chain attacks by analyzing third-party components.
4. It provides ongoing guidance on addressing emerging vulnerabilities affecting AI systems.
5. Ensuring regulatory compliance around the use and implementation of AI is key. In this regard, it is essential to note that at the European level, there is specific mandatory legislation, including the AI Regulation, as well as other cybersecurity regulations such as the DORA Regulation, the NIS2 Directive, and the CRA Regulation.
6. It reduces the likelihood of companies suffering severe economic losses due to security breaches in AI systems.
7. It helps companies safeguard their reputation by demonstrating that they place cybersecurity at the center of their strategies.

In short, generative AI is revolutionizing the daily operations of millions of companies worldwide. This disruptive technology enables companies to automate hundreds of tasks and streamline a wide range of processes; however, it is essential to consider it when designing a company’s cybersecurity strategy.

Thus, among the benefits of a periodic AI security audit, we can highlight the identification and remediation of vulnerabilities that allow malicious actors to steal sensitive information or alter the performance of AI systems.