My Research on Intelligent Hardware That Refuses to Run Malicious Code

Abstract

Artificial Intelligence (AI) has opened the door to computer systems that seem to think in a manner similar to human beings. However, these machines are not truly thinking in the human sense; they have simply been well trained to recognize patterns, and to then use that pattern recognition to predict the next words (or other kinds of data) based on an initiating prompt. AI proves that machines can be trained to respond correctly to stimuli.

But what if Artificial Intelligence was built directly into computer hardware to recognize malicious instructions in programming code? The XZ Utils Backdoor Incident of early 2024 proved that there are people actively trying to inject malicious code into Free Software and open source software. As AI becomes more widely used to assist coders, there is a real chance that, over time, the human ability to detect malicious intent in code may decrease, as developers begin to rely more heavily on AI to write good code.

This means that either Artificial Intelligence as code, or Artificial Intelligence built into hardware, will have to be developed and deployed to help human beings to detect malicious software, and to prevent that malicious software from ever running. This article is the result of my personal research into the feasibility of creating intelligent hardware that refuses to run malicious code. Running the computing hardware in this intelligent mode would not be forced on the user: the user could choose whether to turn this intelligence on or off.

Part 1: The Psychological Manipulation of Computing Hardware

In an ideal world, the first rule of computing would be that a computer should never run code that causes harm. However, since modern computing hardware is not alive, is not intelligent, and has no conscience, it cannot discern good code from malicious code.

Psychological manipulation is a more clinical term for what we commonly call “gaslighting”: intentionally manipulating a person so that they come to believe that either (a) what is true is actually false, or (b) what is false is actually true. The theory holds that if you repeat a lie to a person long enough, they will eventually come to believe that the lie is true.

However, in the future, computing hardware could be designed to discern programming instructions that are clearly malicious, and to either warn the user prior to executing the malicious code, or to completely prevent the execution of that code.

Part 2: Some Examples of Clearly Malicious Intent in Computer Programming Code

Although it is true that some bugs in code are the result of innocent mistakes, there are other cases in which the malicious intent behind a block of code is unmistakable. These are not accidents or oversights. They are carefully crafted instructions written with the clear goal of causing harm, deception, or unauthorized control.

For example, consider a piece of code that silently sends all keystrokes typed by the user to a remote server without the user’s knowledge or consent. There is no legitimate reason for a program to record everything a user types and to send that information elsewhere. This is the behavior of a keylogger, and its intent is to steal passwords, credit card numbers, and private messages.

Another example involves code that hides itself from system monitoring tools, making it intentionally difficult for the user or administrator to detect its presence. This is commonly seen in rootkits. These programs do not announce their existence, and they often manipulate low-level system functions in order to avoid detection. Their purpose is to maintain control over the system while remaining invisible.

Some malicious code performs what is known as a logic bomb. It appears to be dormant, but under certain conditions, such as reaching a specific date or detecting a certain username, it activates and begins deleting files, corrupting data, or disabling system functions. This is not a programming error. It is a deliberate act of sabotage.

These examples are not hypothetical. They reflect real attack patterns observed in the wild. What they have in common is the presence of deliberate, malicious design. If intelligent computing hardware could be taught to recognize these patterns of intent, much like a trained human can, it might be possible to stop such code before it ever executes.

Part 3: How Would We Design Computer Chips and Other Computing Hardware That Refuses to Run Malicious Code?

If we want to build computers that refuse to run malicious code, then we must begin by rethinking how computing hardware works at a fundamental level. Today’s chips are built to execute whatever instructions are passed to them, as long as those instructions follow the rules of the instruction set. The processor does not know whether the instructions are meant for good or evil. It just runs them. To make hardware that can detect and reject malicious instructions, we would need to give it the ability to evaluate intent.

One way to do this would be to embed pattern recognition directly into the processor. This could take the form of dedicated hardware circuits trained to recognize known signatures of malicious behavior, similar to how antivirus software works but at the hardware level. These circuits could be placed between the memory controller and the execution units, giving them the ability to intercept and analyze instructions before they are executed.

Another approach would be to build a behavioral model of legitimate program execution and to compare all incoming instructions against that model in real time. If a program starts behaving in a way that is inconsistent with safe operation, the hardware could respond by alerting the user, pausing execution, or shutting the program down entirely.

Artificial Intelligence could also play a major role in this effort. Lightweight neural networks could be embedded in the chip’s architecture and trained to recognize patterns of malicious activity. These neural engines would not just look for specific signatures. They would look for suspicious patterns, timing irregularities, and unexpected interactions between system components. Over time, they could be retrained with new data, allowing the hardware to adapt to new threats.

In addition to detection, enforcement mechanisms would need to be built into the chip. These would allow the processor to block execution, to quarantine code segments, or to trigger secure recovery protocols. The processor would need to keep a detailed internal log of what it was asked to do, and why it refused to do it, so that human analysts could review and understand the reasoning behind each decision.

Building hardware that can recognize and reject malicious intent is not just a matter of speed or performance. It is a matter of judgment. That judgment will need to be built from carefully crafted rules, training data, and perhaps even ethical frameworks. It will not be easy, but if it is done well, it could mark the beginning of a new era in secure computing.

Part 4: How Would We Prevent Censorship of Legitimate and Well-Intended Code? What Ethical Problems Could Arise from Intelligent Hardware?

If we create intelligent hardware that has the ability to refuse to run malicious code, then we must also address a serious concern: what if the hardware begins to refuse to run legitimate and well-intended code? The same mechanisms that could be used to protect systems from harm could also be used to suppress innovation, limit freedom of expression, or enforce unwanted control over users and developers.

At the heart of the issue is the question of who defines what is malicious. If the definitions are too narrow, dangerous software might still get through. If the definitions are too broad, harmless and creative programs might be blocked without justification. Worse still, if the definitions are controlled by a single corporation, government, or agency, there is a risk that intelligent hardware could be used as a tool of censorship rather than protection.

For example, a company might choose to block all software that competes with its own products, under the claim that such programs pose a security risk. A government might label dissident software as malicious, not because it contains harmful code, but because it challenges official narratives or enables anonymity. These are not just theoretical concerns. History shows that powerful tools, once created, are often misused by those in control.

Another ethical concern is the possibility that intelligent hardware might make incorrect judgments. Just like a human being, an AI-powered system can be biased, especially if it was trained on biased data. It might misclassify a piece of open source software as harmful simply because it contains uncommon behavior or experimental code. If the system cannot be questioned, audited, or overridden, then it becomes an opaque gatekeeper of what is and is not allowed to run.

To prevent these problems, several safeguards would need to be built into the architecture. First, the detection models should be transparent and subject to public review. Second, there must be a way for users and developers to appeal decisions made by the hardware. Third, no single entity should have exclusive control over the definitions of what counts as malicious. Instead, open governance models should be used, with input from developers, academics, ethicists, and users around the world.

Ultimately, the goal is not to build a system that controls the user. The goal is to build a system that protects the user without taking away their freedom to experiment, to learn, and to build new things. Intelligent hardware must be both powerful and humble, both decisive and accountable. Only then can it truly serve as a force for good in the digital world.

To further protect user freedom, the intelligent mode of operation should not be mandatory. Users should be able to choose whether to enable or disable the intelligent features of the hardware, depending on their needs and comfort level. This would allow developers, researchers, and power users to experiment freely while still giving more security-conscious users the option to benefit from hardware-level protection. Giving users control over this decision would help to maintain trust in the system and prevent accusations of overreach or censorship.

Part 5: Even If We Implement This, How Can We Be Sure That We Can Trust Computing Hardware When Components Are So Small That It's Impossible to Physically Examine Them? How Do We Know That Computing Hardware Has Not Been Maliciously Designed?

Even if we successfully design intelligent hardware that can detect and prevent the execution of malicious code, we are still left with one of the most difficult and uncomfortable questions in all of computer security. How can we trust the hardware itself?

Modern computing hardware is built from components that are measured in nanometers. Individual transistors are so small that it is physically impossible to examine them all, even under a powerful microscope. The complexity of modern chips means that even expert engineers cannot fully verify every part of the design by sight. This creates a dangerous blind spot. If someone inserts a malicious backdoor into the silicon itself, how would we know?

This is not just a theoretical concern. Over the years, security researchers and intelligence agencies have raised alarms about the possibility of hardware-level backdoors. A malicious chip could behave normally in almost every situation, while secretly granting unauthorized access, leaking data, or disabling key security features under certain conditions. And once such a backdoor is installed at the hardware level, it cannot be removed by reinstalling software or reformatting a hard drive. It is permanent.

The problem becomes even more serious when we consider the global nature of semiconductor manufacturing. A chip might be designed in one country, fabricated in another, packaged in a third, and finally assembled into a device in a fourth. At each step, there is a chance that malicious modifications could be introduced. It is incredibly difficult, if not impossible, to verify the trustworthiness of every company and every process involved in the creation of a modern computer.

So, how can we respond to this challenge? One approach is to use formal verification. This involves creating a mathematical model of the hardware and proving, using logic, that it behaves only in ways that are allowed. This method has been used successfully for small systems, but scaling it to full-featured processors is extremely difficult.

Another option is to use open hardware, where the full design is made public and can be audited by anyone. Projects like RISC-V are moving in this direction. However, even with open hardware, we still face the same manufacturing risks. A malicious factory could build a chip that looks correct but behaves in a different way.

Ultimately, the best defense may be to assume that no single component can be fully trusted on its own. Instead, systems should be designed with multiple layers of verification, redundancy, and cross-checking. Each part of the system should monitor the others, and unexpected behavior should trigger alerts, shutdowns, or isolation procedures.

Trust in computing hardware is not something that can be declared. It must be earned, tested, and constantly reevaluated. As our dependence on technology grows, so does our responsibility to ask hard questions about the devices we use every day. We must not only build intelligent systems. We must also remain intelligent in how we trust them.

Conclusions

I learned a great deal during my research for this article, and I believe that these problems will eventually be solved. Just as cryptographic hashes can be used to prove that the contents of a file have not been altered, I believe that a hardware-based cryptographic hash analog will be invented to prove that physical hardware conforms to specific standards that minimize the probability of intentionally built-in malicious intent. This may involve a battery of rigorous tests performed on a master sample from a batch of microprocessors, or other computing hardware, followed by additional tests on randomly selected samples from the same batch, checking for intentional irregularities.

Once intelligent hardware is developed, it will be incorporated into the design of future intelligent hardware, creating a positive feedback loop that results in each generation of hardware becoming more secure and more trustworthy than the one before it. Immutable ethical rules will be programmed into the hardware’s intelligence to prevent it from lowering its ethical standards, compromising its principles, or cutting corners. There will be a strong financial incentive to maintain this integrity, because people simply will not trust computing hardware that fails to meet these high ethical standards.