Artificial intelligence has become mainstream in recent years because of its increased capabilities. People can now use AI to edit videos, write essays, research topics, create audio clips, build websites, design graphics, analyze data, and perform many tasks that require skill.
Companies that develop these AI systems use machine learning to train the software to interpret user input and produce relevant and near-accurate results. Many businesses have begun to rely on AI tools to reduce operational costs and become more efficient, but cybercriminals now seek to disrupt this process.
These cybercriminals deploy adversarial AI and ML techniques to reduce the effectiveness of AI systems by feeding them incorrect or biased information designed to make them less reliable and accurate. AI companies that suffer this attack will lose credibility in the eyes of consumers, making them lose market share and revenue.
How Adversarial AI and ML Works
Adversarial AI and ML is a cyber attack malicious actors employ to mislead or manipulate artificial intelligence and machine learning systems to reduce their performance. They can take advantage of cybersecurity mistakes and launch these attacks at any stage of the machine learning process. The common adversarial AI and ML techniques are poisoning training data with incorrect information and using misleading inputs to make an AI system produce undesired results. Cybercriminals can combine these attacks for a more disastrous effect.
To launch their attacks, cybercriminals first extract information about the characteristics and behavior of the machine learning system they want to attack. They will use this information to learn how to craft deceptive inputs they can feed the system to produce their intended results.
Protecting AI and ML Systems from Attacks
To prevent cyberattackers from successfully launching adversarial AI and ML attacks against your systems, follow these steps:
Understand the nature of the threat
You need to understand the threat model to adequately protect yourself against it. This means defining the goals, motives, strategies, and capabilities of a potential attacker. Find out the data an attacker can potentially access, how they can manipulate the data, the outputs they want to produce, and their measure of success. These will help you pinpoint the vulnerabilities and attack surfaces in your AI and ML system so you can patch them before they are exploited.
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Train the system with diverse and extensive data
To prevent or downplay the effects of data manipulation, train your AI system with extensive data from diverse sources. This will make the system learn from different scenarios and explore different viewpoints so it can produce fair and balanced results.
Strengthen the defenses of the machine learning model
Train your machine learning model to detect and defend itself against adversarial inputs. There are many defensive techniques you can implement for this, some of which are adversarial training, detection, preprocessing, regularization, and verification. Feed the model examples of adversarial inputs so it knows what to avoid.
Audit the system outputs
To verify that your AI system works as intended, input a command and compare the result with what you expect the system to produce. This comparison will help you detect errors and inaccuracies caused by adversarial attacks. The common audit techniques for artificial intelligence systems and machine learning models are attribution, clustering, and thresholding.
Endnote
Adversarial AI and ML are major cybersecurity threats that can affect companies that have integrated artificial intelligence with their operations. They should implement the measures above to protect themselves and ensure their AI systems remain reliable and trustworthy.
