Artificial intelligence (AI) has made significant strides across various industries in recent years, from autonomous vehicles to medical diagnostics. However, adversarial AI is emerging as a new dangerous threat as these technologies evolve. From false identification to road accidents, the impact of adversarial attacks can be devastating if left unchecked.

Understanding Adversarial AI

Adversarial AI, also known as adversarial attacks, involves intentionally tampering with AI models to alter their outputs or steal sensitive information. Specifically, Adversarial AI  causes these models to misinterpret inputs or trick them into making incorrect predictions that benefit the attacker. According to research, 41% of organizations have experienced AI security incidents, including adversarial attacks on their AI systems. 

Types and Methods of Adversarial AI Attacks

Adversarial AI can be divided into 2 methods of attack:

• White-box attacks: The attacker has full access to the machine learning model, including its architecture, weights, and training data. With this inside knowledge, they can precisely target and directly exploit weaknesses in the model’s structure.
• Black-box attacks: The attacker only has access to the model’s inputs and outputs, with no knowledge of its inner workings. In this case, trial-and-error methods can be used to reverse-engineer the model and find vulnerabilities.

Adversarial attacks typically fall into four main types:

• Evasion attacks: The attacker modifies the input to confuse the AI model, making it misclassify or produce inaccurate results. An example would be subtly altering an image to trick a facial recognition system into mistaking someone for another person.
• Data poisoning: Corrupted data is introduced during the AI training process. When the system encounters a specific "trigger" later, it produces erroneous outputs.
• Inference attacks: Attackers can exploit the outputs of AI models to reveal sensitive details about the training data. This can lead to leakage of sensitive information, such as confidential health or financial records.
• Model extraction: Attackers can gradually figure out and replicate an AI model’s architecture through repeated queries. By inputting a large number of carefully crafted prompts, the attacker can infer the model’s structure and parameters based on the model’s output from a blackbox state. This is a significant threat to proprietary systems where intellectual property and trade secrets are at risk. The attacker can then create similar models to compete in the market or use the model as a proxy to construct more sophisticated attacks [2].

The Looming Dangers of Adversarial AI

Unlike traditional cybersecurity threats, adversarial AI does not attack the system’s infrastructure but rather the way the system learns and adapts. By introducing subtle influence into the system, adversaries can completely alter an AI system’s decision-making process. When put into real-world context, adversarial AI can potentially have devastating consequences.

Researchers have found that small changes to road signs could trick self-driving cars into misidentifying them. The road sign, which is used as the input for the system, could be attacked in a physical or simulated way so that the car would perceive it differently. For example, a simulated attack to subtly modify a stop sign can cause the system’s classifier to interpret it as a speed limit 45 sign, which can lead to disastrous traffic accidents [1]. 

In AI-powered surveillance systems, cameras can be tricked into "not seeing" a person. By slightly altering their appearance using specialized “patches” or patterns, attackers can make themselves invisible to surveillance cameras [4]. This attack can effectively render the whole security system ineffective.

For voice systems, adversarial attacks have shown the ability to confuse voice recognition systems to create false transcripts. Potential consequences include errors in legal documentation or automated customer service responses [5].

Furthermore, with the increasing popularity and usage of generative AI, attackers can manipulate the models for spreading fake news or malicious campaigns. These intentions result in misinformation being produced by the AI when inquired. 

Combating Adversarial AI

Even though preventing adversarial AI can be challenging, some countermeasures can be implemented. Firstly, adversarial training is a method that can be carried out in the early stage of development. By exposing the AI model to adversarial examples during the training process, it can recognize and develop resilience against manipulation. While this method improves model robustness, it often comes at the cost of longer training time and reduced accuracy [6]. 

Securing API access is an approach to protecting AI systems at deployment. By limiting query rates, organizations can control the number of requests a user can make within a specified time frame, effectively mitigating the risk of automated attacks that attempt to extract sensitive information from the model. Moreover, unusual activities can also be detected and handled in a timely manner through API monitoring.

Another method of defending AI systems is periodic testing and auditing. Regular testing of AI systems with adversarial inputs can help detect vulnerabilities and prevent exploitation. Furthermore, audits ensure that any data drift in the model is corrected, maintaining its resilience against evolving threats.

Implementing data governance policies in the system can also help reduce the risk of adversarial attacks. This can include policies such as limiting access to training data based on roles or implementing user control settings. Last but not least, while technology solutions play a big role, the human element is equally important. Educating employees and stakeholders on AI security and adversarial pattern recognition and promoting strong data management practices can go a long way.

The Future of AI Security

Cybersecurity is one of the most crucial priorities for businesses. As 30% of all AI cyberattacks will involve methods like training data poisoning, AI model theft, or adversarial sample manipulation by 2025 [3], stakeholders must adopt a multi-faceted defense strategy to improve their security posture.