AI Is Turning Reverse Engineering Into a Very Powerful Tool

For decades, reverse engineering has been a painstaking exercise in technological archaeology.

A researcher who wanted to understand what was hidden inside a piece of hardware might have to obtain its firmware, inspect its circuitry, disassemble software, trace how components communicated and spend days or weeks figuring out how the system worked. The work required specialized knowledge of electronics, programming, operating systems and security.

Artificial intelligence is beginning to change that equation.

In an experiment described on Aug. 23 by cybersecurity researcher Chaz Schlarp, an AI agent based on Claude Opus 5 was given the firmware and update mechanisms for five connected devices: a webcam, a monitor, a microphone, a video capture card and a Wi-Fi lamp.

The researcher asked the agent to investigate their security mechanisms, search for hidden functions and debugging interfaces, identify potential attack surfaces and, where possible, interact with the devices themselves.

According to Schlarp, the agent was able to manipulate the firmware of all five devices.

“In total, analysing the five devices represented around 13 hours of work for the agent and 98 messages from me,” Chaz Schlarp said.

The significance is not simply that an artificial intelligence system found weaknesses in five consumer products. Security researchers have been finding flaws in connected devices for years. The more consequential change is the amount of human expertise and time that AI could remove from the process.

“I would now assume that any peripheral connected to a computer can be infected with malware. Previously, such an operation would have required considerable resources and would have seemed limited to state actors,” the researcher said.

He is now wondering “what an AI-powered computer worm capable of autonomously carrying out reverse-engineering operations could accomplish today.”

That question points toward a broader transformation in cybersecurity. Reverse engineering, once a specialist discipline at the intersection of engineering and computer science, is becoming increasingly accessible to machines that can read code, reason about unfamiliar systems, generate scripts and iterate through experiments.

The same capability that helps a security researcher understand an opaque device can potentially help an attacker find a path into it.

And there are now hundreds of millions, perhaps billions, of such devices.

The modern internet is no longer confined to computers and phones. Cameras, microphones, televisions, routers, medical equipment, industrial controllers, cars, appliances, watches and household lighting systems increasingly contain processors, software and network connections.

The security of these products therefore depends not only on the code running on a server somewhere in a data center, but on firmware buried inside objects sitting on desks, walls, factory floors and vehicles.

That creates a particularly difficult problem: the device may be physically small, but the software ecosystem around it can be extraordinarily complicated.

The National Institute of Standards and Technology has spent years developing guidance for manufacturers because of this problem. In April 2026, NIST published a revised set of foundational cybersecurity activities for IoT manufacturers, warning that internet-connected products often lack cybersecurity capabilities that customers need to mitigate risk. The guidance covers security throughout the product life cycle, from development and sale to maintenance, support and end of life.

The timing matters. The traditional security model assumed that companies could discover vulnerabilities, issue patches and rely on security professionals to investigate suspicious behavior.

AI is compressing the time available for each of those steps.

Verizon’s 2026 Data Breach Investigations Report found that exploitation of vulnerabilities had become the leading initial access vector in the breaches it analyzed, accounting for 31% of breaches. Verizon also said AI was accelerating attacks from months to hours. Third-party supply-chain breaches rose 60%, accounting for 48% of breaches in the report.

The lesson for connected hardware is uncomfortable: a vulnerability does not have to remain obscure for long to be dangerous.

Reverse engineering is hardly new.

The basic idea predates computers. Engineers have long taken apart machines to understand how they were designed, determine why they worked or failed, reproduce components or improve upon them. Military powers used captured equipment and foreign technology to learn about their adversaries, while manufacturers studied competitors’ products.

As technology became more complex, reverse engineering became a strategic instrument of industrial and military competition.

Research published in International Security examining military technology argues that reverse engineering and imitation have played a significant role in technological competition, while also showing that increasing complexity has made sophisticated systems harder to reproduce. The authors examined historical cases including the British-German naval rivalry and China’s attempts to reproduce advanced U.S. military technology.

Computers changed the practice again.

Instead of simply examining physical components, researchers could study software, operating systems and binary code. A program could be disassembled into instructions. Engineers could observe what it did without having access to its source code.

The practice eventually became the subject of important legal battles.

In 1992, the U.S. Court of Appeals for the Ninth Circuit ruled in Sega Enterprises Ltd. v. Accolade Inc. that intermediate copying associated with reverse engineering could, under certain circumstances, constitute fair use. Several years later, in Sony Computer Entertainment v. Connectix, the court similarly examined the reverse engineering of Sony’s PlayStation BIOS and concluded that intermediate copying could be necessary to gain access to unprotected functional elements.

The disputes reflected a fundamental tension that has never disappeared: understanding how something works can be essential to competition, interoperability and security, but the technology being examined may be protected by intellectual-property law.

Security research introduced another dimension.

Researchers began taking apart routers, cameras, smart locks and other connected devices to discover weaknesses before criminals could exploit them. Firmware became particularly important because it sits close to the hardware and often controls functions that cannot easily be observed from the outside.

The security community has repeatedly found the same categories of problems.

OWASP’s Internet of Things Top 10 identifies weaknesses including hardcoded passwords, insecure network services, insecure ecosystem interfaces, inadequate firmware-update mechanisms, outdated components, insecure data storage, inadequate device management, insecure default settings and insufficient physical hardening.

The fact that many of these problems remain familiar years later is significant.

A manufacturer can create a sophisticated device and still leave an old administrative interface, an insecure update process or an embedded credential somewhere inside it.

For an attacker, reverse engineering is often the bridge between knowing that a device exists and understanding how to compromise it.

AI could make that bridge much shorter.

Firmware has always presented a particular challenge because it is often invisible to consumers.

A person buying a webcam sees a camera. A security researcher sees a processor, memory, firmware, communications protocols, update mechanisms, debugging interfaces and potentially undocumented functionality.

The consumer may never know which software is running inside the device or how long the manufacturer intends to maintain it.

NIST’s IoT cybersecurity work has repeatedly emphasized that manufacturers need to build security into devices and provide customers with information and capabilities that allow them to manage security throughout the product life cycle.

Its 2026 guidance goes further by explicitly broadening the focus to maintenance, support, communications with customers and end-of-life considerations.

That is partly because connected devices are unusual computers. They may remain in operation for years after their manufacturers have moved on to newer models.

A smartphone might receive frequent security updates. A network-connected camera mounted outside a building may receive far less attention.

A Wi-Fi lamp may appear trivial. A compromised industrial controller is not.

The underlying principle is the same, however: every additional connected device creates another piece of software that has to be secured.

The European Union Agency for Cybersecurity analyzed 4,875 cybersecurity incidents between July 2024 and June 2025 in its 2025 threat landscape. ENISA said threat groups were reusing tools and techniques, exploiting vulnerabilities and targeting the resilience of interconnected digital infrastructure.

Interconnection is precisely what makes the AI-assisted reverse-engineering problem more consequential.

A vulnerability in a device that exists entirely offline may remain a local problem. A vulnerability in a networked device can become a doorway.

The most important shift may not be that AI can reverse-engineer a device.

It is that AI agents can potentially perform a sequence of tasks without a human directing every individual step.

A conventional AI assistant might explain a firmware function after a researcher asks about it. A more autonomous agent could be instructed to investigate the device, formulate hypotheses, write code to test those hypotheses, analyze the results and continue experimenting.

That turns reverse engineering from a series of human decisions into something closer to an automated research loop.

The technology is moving in this direction outside cybersecurity, too.

On Aug. 27, Anthropic announced a research preview of a framework called the Model Hardware Standard, designed to allow AI agents to operate physical equipment in scientific research and advanced manufacturing. The company said the framework could allow agents to communicate with programmable devices, including laboratory instruments and robotic systems.

The development illustrates a larger convergence: AI is no longer being confined to screens.

It is increasingly being connected to machines.

That can make laboratories more productive. It can automate industrial processes. It can help engineers test equipment and researchers conduct experiments.

But the same ability to understand and operate machines can be turned toward finding ways around their security.

Cybersecurity has always had an asymmetry problem. Defenders have to understand their entire environment; attackers need only find one exploitable weakness.

AI could intensify that imbalance by making the attacker or researcher faster.

Verizon’s latest breach data offers an indication of how quickly the broader threat environment is changing. Vulnerability exploitation reached 31% of breaches in its analysis, while the company described AI as compressing attack timelines from months to hours.

That does not mean AI has suddenly made every connected device vulnerable. Nor does an AI agent manipulating firmware in a controlled experiment mean that autonomous worms capable of compromising arbitrary devices are imminent.

There remain significant technical barriers.

Devices differ enormously in their architectures. Hardware access can be difficult. Firmware may be encrypted or cryptographically signed. Secure boot can prevent unauthorized code from executing. Network protocols can be proprietary. Physical access may be required for some attacks.

Those defenses are precisely why secure engineering matters.

The goal is not to make reverse engineering impossible. It is to ensure that learning how a device works does not automatically provide a path to controlling it.

The future of reverse engineering is therefore likely to involve both sides using AI.

Security researchers will use agents to find vulnerabilities before criminals do. Manufacturers could use them to audit firmware and update mechanisms during development. Regulators may increasingly demand evidence that connected products can be securely maintained after they reach consumers.

Attackers will use the same tools to search for weaknesses at scale.

The resulting arms race could be less about discovering whether a vulnerability exists than about discovering it first.

That changes the economics of security.

If it once took a highly trained researcher weeks to understand a device, the cost of examining that device was itself a barrier. If an AI system can reduce the work to hours, the same analysis can potentially be repeated across thousands of products.

This is where the history of reverse engineering offers an important lesson.

Technology has repeatedly lowered the cost of understanding other technology. What began with physically dismantling machines evolved into analyzing circuits, disassembling software and examining firmware. AI adds another layer: reasoning and experimentation can increasingly be automated.

The result could be a world in which the most important security question is no longer whether a connected device contains a vulnerability.

It is how long that vulnerability can remain undiscovered.

For manufacturers, the answer cannot simply be to hide firmware or make reverse engineering harder. Security through obscurity has repeatedly proved inadequate. The more durable approach is to assume that determined researchers will eventually understand how a device works.

NIST’s latest guidance reflects that philosophy, emphasizing security throughout the product life cycle rather than treating cybersecurity as a feature added shortly before a product reaches consumers.

That means secure boot, authenticated updates, vulnerability disclosure, software inventories, meaningful support periods and mechanisms for retiring devices safely.

It also means designing products on the assumption that their internals will eventually be exposed.

The paradox is that AI may make reverse engineering both more dangerous and more valuable.

The same technology that could help an attacker uncover a hidden debugging interface could help a security engineer discover it first. The same agent that might eventually automate vulnerability research could be used by manufacturers to continuously test their products.

For decades, reverse engineering required a human who knew what to look for. The next generation of systems may be capable of looking for themselves.

Stay ahead of the Stories shaping our world. Subscribe to Impact Newswire and join our 
WhatsApp Channel for updates on global tech, business, and innovation—all in one place.

Dive deeper into the future with the Cause Effect 4.0 Podcast, where we explore the ideas, trends, and technologies driving the global AI conversation.

Got a story to share? Contact Us to reach a global audience with Impact Newswire.