AI algorithms have fundamental flaws that attackers might exploit to cause the system to fail. Unlike typical cybersecurity assaults, these flaws are not the result of programming or human error. They are just flaws in today’s cutting-edge methods. To put it another way, the algorithms that enable AI systems to perform so effectively are flawed, and their systemic limits provide possibilities for enemies to exploit. This is, at least for the foreseeable future, a mathematical reality. These are the reasons why AI attacks arise. To understand about what AI attacks are, you can read our blog article: What are AI attacks?

The way an AI learns

To understand why this is the case, we must first understand how the algorithms that drive AI function. Machine learning, a collection of algorithms that extract information from data to “learn” how to execute a specific task, powers many modern AI systems. A machine learning algorithm “learns” in the same way as people do.

Humans learn by viewing several examples of an item or concept in the actual world and storing what they learn in their brains for subsequent use. Machine learning algorithms “learn” by looking at numerous examples of an item or idea in a dataset and storing what they learn in a model for subsequent use.

In many AI applications based on machine learning, there is no outside knowledge or other magic used in this process: it is entirely dependent on the dataset and nothing else. There is no outside knowledge or other magic employed in the development of many, if not most, AI applications based on machine learning. Continuing with the stop sign example, if the dataset comprises photographs of stop signs in the sun and shade, from straight ahead and from various angles, during the day and at night, it will learn all the varied ways a stop sign may look in nature.

Dependence on data

Already, the learning process poses a key vulnerability. It is entirely dependent on the dataset. Because the dataset is the model’s only source of information, it will be compromised if it is corrupted or “poisoned” by an attacker. If employed on data that is even slightly different from the original dataset, the model may fail completely. This is a significant shortcoming that attackers may exploit, by adding false variations, such as a piece of tape or other erroneous patterns, the attacker can disrupt the model and manipulate its behavior based on the artificial pattern introduced.

As a result, extremely modest artificial interventions selected precisely can break the relatively fragile patterns the model learns and have absurdly large effects on the model’s output. As a result, a little piece of tape may quickly change a stop sign into a green light: it doesn’t have to make the full stop sign appear like a green light; it only must fool the model’s unique small fragile patterns. Unfortunately, this is simple to accomplish. Under contested conditions, AI systems can be made to fail even if they are extremely successful under “normal” conditions.

Black box nature of state-of-the art AI algorithms

An obvious next step would be to figure out why the patterns the model learns are so fragile. However, this is not yet allowed in the most extensively used models, like as deep neural networks, because it is still unclear how and even what these models learn. As a result, the most prominent machine learning algorithms used to power AI, such be neural networks, are referred to as “black boxes”. We know what goes in and what comes out, but we don’t know what occurs in between. We can’t change what we don’t comprehend.

For the same reason, it is difficult, if not impossible, to identify whether a model is being attacked or just doing poorly. While alternative data science approaches, such as decision trees and regression models, give far greater explainability and comprehension, they do not typically provide the performance that neural networks can.

Summary: Characteristics of ML systems that make them vulnerable to attacks

We can now specify the properties of the machine learning algorithms that underpin AI that render these systems vulnerable to attack based on this understanding.

  • Characteristic 1: Machine learning operates by “learning” rather fragile patterns that are effective but easily disrupted. Machine learning models, contrary to common opinion, are neither “intelligent” or capable of really duplicating human aptitude on tasks, even when they do well. Instead, they operate by learning brittle statistical correlations that are easily disrupted. Attackers can take advantage of this brittleness to create attacks that devastate an otherwise good model’s performance.
  • Characteristic 2: The exclusive reliance on data is a major source of error in a machine learning model. Machine learning “learns” purely by extracting patterns from a dataset of samples. Machine learning models, unlike humans, have no baseline knowledge to draw on; their whole understanding is dependent entirely on the data they encounter. Data poisoning poisons the AI system. Such attacks effectively transform an AI system into a Manchurian candidate that attackers may activate at their leisure.
  • Characteristic 3: Modern algorithms are black boxes makes auditing them challenging. Little is known about how widely used cutting-edge machine learning algorithms, such as deep neural networks, learn and work—they remain a mysterious black box in many respects even today. This makes determining if a machine learning model has been corrupted, or whether it is being attacked or just not performing well, difficult, if not yet impossible. This distinguishes AI attacks from typical cybersecurity concerns in which vulnerabilities have unambiguous definitions, even if they are difficult to uncover.

These flaws explain why there are no ideal technological solutions for AI assaults. These are not “bugs” that can be patched or addressed in the same way that standard cybersecurity vulnerabilities are. They are fundamental concerns at the heart of today’s cutting-edge artificial intelligence.

Way forward: Security for AI Models

So, in the current context, is there a way to secure AI systems against such attacks? AI Security technology hardens the security posture of AI systems, exposes vulnerabilities, reduces the risk of attacks on AI systems and lowers the impact of successful attacks. Important stakeholders need to adopt a set of best practices in securing systems against AI attacks, including considering attack risks and surfaces when deploying AI systems, adopting reforms in their model development and deployment workflows to make attacks difficult to execute, and creating attack response plans.

AIShield helps enterprises safeguard their AI assets powering the most important products with an extensive security platform. With its SaaS based API, AIShield provides enterprise-class AI model security vulnerability assessment and threat informed defense mechanism for wide variety of AI use cases across all industries. For more information, visit and follow us on LinkedIn.

To understand what are novel risks affecting AI systems and review perspectives on AI Security from research community, businesses and regulators, please read our Whitepaper on AI Security.

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Key takeaways from this article

This article highlights why AI systems are vulnerable to attack. Key characteristics of AI that makes it vulnerable to attacks:

  • Machine learning operates by “learning” rather fragile patterns that are effective but easily disrupted.
  • The exclusive reliance on data is a major source of error in a machine learning model.
  • Modern algorithms are black boxes makes auditing them challenging.

This blog has been republished by AIIA. To view the original article, please click HERE.