How We Test AI Models Against Adversarial Attacks Before Launch

What Are Adversarial Attacks?

Adversarial attacks mean the attempts to 'trick' the AI system through usage of malicious inputs aimed at producing unexpected, or wrong actions from this AI system. Depending on the type of AI program, malicious inputs may consist of specially developed text, images, data, or prompts.

An adversary may aim to motivate a program to ignore its instructions, disclose the information it is not supposed to disclose, provide harmful results, or produce incorrect classifications.

Though not every example of strange program reaction is a security flaw, the systematic testing of AI programs makes it possible to distinguish between the normal shortcomings of the program and the risks calling for action.

Why Test Before Launch?

AI systems act in contrast to common deterministic software applications. While regular software systems provide predictable results when they are fed with the same set of inputs, AI systems yield different outputs, depending on the state and behavior of the model.

Therefore, it is essential to conduct sufficient testing for security purposes prior to the launch of AI-based software.

Early testing is aimed at revealing any possible vulnerabilities while the team has the chance to update the model, application logic, prompts, access management, and preventive mechanisms.

Using Adversarial Testing

Dev Technosys employs the adversarial testing technique at the stage of software development instead of treating it as a last-minute application step.

The first step of the method is to define the purpose of the AI system and determine possible attack vectors associated with it.

The aforementioned attack vectors may encompass user input, API queries, retrieval system use, the interface of the AI model, external connections, databases, or services integrated with the AI system.

Then, the testing process consists of implementing testing attempts to provoke unexpected actions by the AI system.

Testing the Complete AI System

A significant point to keep in mind is that the model is just one part of the AI implementation. Security vulnerabilities are possible in other software as well.

Therefore, tests must be conducted in authentication, authorization, APIs, data processing, logs, integrations, and external tool access, along with model performance tests.

Even if the model itself is secure, it still can cause security issues if the application gives it too many rights or leaks sensitive information through misconfigured integrations.

To Document and Address Results

After testing a weakness is found, it should be kept on record and evaluated based on its potential consequences and probability of occurrence.

Mitigation may be done by refining prompts, adding verification, limiting access, filtering input and output, altering app logic, revising training set, or introducing additional human evaluation.

Once all changes are made, the necessary testing should be repeated to check whether the remediation works and whether there are new problems.

Security Testing Doesn't End at Launch

Security cannot be guaranteed merely at the time of the launch. The models, data, integrations, and user behavior could have changed since the time of the test, thus making it possible for new risks to arise.

At Dev Technosys, adversarial testing is considered part of the wider spectrum of responsible AI practices. Testing prior to the launch will expose weaknesses, and constant monitoring and occasional evaluations will keep the AI model safe.

One does not seek an argumentative point that the AI cannot be hacked. Rather, the idea is to learn how it can fail, minimize unnecessary risks, and implement security measures before technologies are delivered to users.