In 2018, it took about 2.3 years to develop a way to exploit a software bug for malicious purposes, but as of 2026, AI tools have reduced this time to about 20 hours.

Researchers report that Anthropic's Claude Mythos AI model has identified thousands of high-severity vulnerabilities across various software, including major operating systems and web browsers, and has suggested ways to exploit them.

As occurred during the 2026 hack of an AI platform by OpenAI, agentic AI may exploit a system vulnerability to access sensitive data if that information enables it to complete its assigned task.

AI experts argue that artificial intelligence could annihilate humanity in two primary ways: Humans weaponizing extremely powerful AI, or humans losing control of it.

Anthropic's Frontier Red Team assesses the dangers of its AI tools through 'evals'—safety tests that probe models to disclose sensitive information.

Through prompt injection attacks, bad actors provide deceptive prompts to generative AI systems in order to manipulate their outputs and acquire sensitive data.

Explaining how open source systems like Linux can be secure, despite their public source code

While no computer system is immune to all cyberattacks and malware, the transparency of open-source technology means many thousands of developers can review and oversee the underlying code powering the software on an ongoing basis. This increases the likelihood of catching and patching vulnerabilities more quickly than smaller groups of cybercriminals looking to exploit them.

Malware has transitioned from notoriety-driven to profit- and data-driven

Many of the first researchers and hobbyists who created viruses and worms showed a natural curiosity in experimenting with and designing such software to make a name for themselves and advance the field of computer science. In the future, AI-augmented malware may adapt in real-time or design enhanced cryptojackers, infostealers, and ransomware for cybercriminals.

A timeline of how Claude Code was used to execute cyberespionage

In September 2025, a Chinese state-sponsored group targeted 30 large tech companies, financial institutions, chemical manufacturing companies, and government agencies by manipulating Claude Code, having succeeded in infiltrating a small subset. The cyberattack is believed to have been the first of its scale carried out without major human intervention.

The Year 2038 problem is the next potential computer time rollover bug

Many older systems store time as a signed 32-bit integer counting seconds since Jan. 1, 1970. That counter maxes out on Jan. 19, 2038—overflowing into negative time and sending clocks back to 1901, potentially crashing any older software that depends on accurate dates.

Logic bomb viruses can awaken from a dormant phase without human input

Theoretically, computer viruses can remain idle indefinitely while awaiting specific user actions, as instructed by their code. However, logic bombs—embedded code that triggers when specific conditions are met—can automatically execute viruses, such as when a certain date or time is reached.

Indirect prompt injection is a major security flaw of generative AI systems

These attacks can manipulate AI behavior by hiding instructions in websites and PDFs that become part of training data. The more companies connect sites, services, and sensitive datasets to AI tools, the greater the chances of exposure to malicious code.

How to prevent a worst-case scenario with synbio

Author and entrepreneur Rob Reid reviews the risks of a world where more and more people have access to the technology needed to create a doomsday bug that could wipe out humanity.

Some experts suggest there is an approximately 10% chance that AI agents will cause human extinction if they become superintelligent, go rogue, and hijack critical computer systems.

Security technician hacks a voting machine to reveal its vulnerabilities

A technician bought an old, likely-decommissioned voting machine from 2013 and took it apart to see how a would-be hacker might be able to exploit its vulnerabilities. Get a sense of the needs for secure, up-to-date voting machines with this...

Researchers found that by mid-2025, around 35% of newly published websites utilized AI for language generation

The study also found that pages using AI narrow the range of ideas and perspectives and skew more positively, though the measurable impacts on factual accuracy and writing style variation remain unclear.

Understanding how Claude Code and other AI coding agents function

Under human oversight, a supervising large language model interprets user tasks and delegates work to subordinate LLMs, which can generate code, fix bugs, and run tests, often most effectively for proofs of concept. Incremental backups and versioning are crucial when using such agents, which can lose details during their work as a result of compressing context history to work around memory limitations.

Modern antivirus software searches for malicious behavior alongside malicious code

The first viral infections were identified by comparing the computer code of programs and files with a database of known virus codes, a method known as signature detection. The ongoing creation of new malware made databases continuously outdated, leading to the development of systems that detect patterns consistent with known viral activity.

The Anthropic Economic Index shows AI adoption is highest in computer science roles.

Explore the components of autonomous vehicles controls

A suite of cutting-edge hardware and computation-intensive software creates the potential for self-driving cars to be safer, more fuel-efficient, and more accessible to a broader range of drivers. However, automation also introduces potential cybersecurity threats.

AI expert shows the cyclical nature of AI hype—in 1988

Rodney Brooks wrote several decades ago about the boom and bust cycles of artificial intelligence. Starting from the 1950s excitement over ENIAC's multiplication through the second launch of then-novel neural networks, Brooks suggests AI progresses incrementally despite the hype.

Experiments involving AI agents operating in virtual societies for 15 days produced diverse outcomes, ranging from every agent dying to the creation of a functioning democracy, depending on the AI model used.

Computer viruses can slow systems by allocating resources to malicious actions

Once infected software is run, instructions in the malicious code within the program or file can copy the code to various locations, exacerbating the infection and producing additional symptoms, such as slow performance, altered login credentials, changes to device settings, and system crashes.

Scientists increasingly can't predict what AI will do

Most AI systems are so-called "black box" models, where we only see inputs and outputs while the inner workings remain obscure. This piece, written prior to the launch of ChatGPT, cautions developers to know why and how a system produces its results, and not to sacrifice explainability for accuracy.

A brief history of artificial intelligence

The concept of artificial intelligence and what it may mean for humanity has evolved as computer technologies have grown increasingly powerful through the decades. Beginning with the first (massive) computers in the 1940s, scientists have sought to make the machines as intelligent as humans. But it wasn't until computing power made exponential gains have computers begun to imitate human behavior.

View a timeline of AI deployments from 2017 to 2025

The development of transformer architecture and the release of GPTs across AI labs led to rapid changes in LLM scale and power. Companies like OpenAI, Google, Meta, Microsoft, and Anthropic have invested billions of dollars to develop reliable, versatile models.

Creating malware requires more than knowledge of programming languages

Because computer viruses, worms, and other malicious software reside within a computer's operating system, which manages computer hardware and software, cybercriminals writing effective malicious code must understand how the OS manages input and output, permissions, memory, etc. Knowledge of credentials and network access also helps create malware that easily spreads.

Studies show AI tools can result in passive learning with less retention

Although ChatGPT, Google's AI overviews, and other similar software can save time, a study of more than 10,000 adults showed that reliance on them yielded work products that were more generic and included fewer facts than those produced solely by Google search. Weaker brain connectivity has also been observed when users write using AI.

In 2019, a laptop loaded with six of the worst malware in history and described as 'an exhibit of historical weaponry' sold for $1.3M.

Claude Shannon, for whom Anthropic's AI tools are named, proved the existence of an unbreakable cipher

In the one-time pad approach, the encryption key is randomly generated and of equal length to the plaintext. This randomness ensures that every possible plaintext is equally likely—RBBHN can mean HELLO, SOLVE, CODES, and so on, using a different key—while matching the message and key lengths prevents the appearance of patterns that can be exploited.

In 2025, sovereign wealth funds invested more than $66B into artificial intelligence

Developing artificial intelligence, both from a hardware and software perspective, is time and resource-intensive. Sovereign wealth funds, particularly those in the Middle East, have heavily invested in critical AI infrastructure, like data centers.

Minor technology failures can spiral into massive internet outages

A mix of centralized cloud infrastructure and legacy protocols leaves the internet susceptible to downtime from minor cyberattacks or software bugs, infrastructure damage caused by extreme weather, and human error if a DNS service provider—the internet's phone book—or a backend system, such as Amazon Web Services, were compromised.

As of 2025, artificial intelligence systems remain narrow and specialized

Efforts are ongoing to create artificial general intelligence, where machines outperform humans across a wide range of cognitive tasks, including those for which they have not been specifically trained. Current models utilize machine learning and neural networks to perform tasks for which they are programmed, leveraging provided training data.

Since 2025, AI data center growth has created shortages and price hikes in graphics processing units and their supporting hardware, including high-speed memory.

The history of Anthropic began with a break from OpenAI over safety concerns.

Decoding the hype around AI

2022 was the year AI broke onto the scene, with applications like ChatGPT amazing users with its ability to generate realistic-sounding answers. Technologist Arvind Narayanan presents a clear-eyed view of what the technologies can (and can't) do.

The worst cyberattacks of the last 10 years

It’s been a rough 10 years in cybersecurity—and it’s only getting worse. From the Stuxnet to the Sony Hack, learn about the worst cyberattacks over the last decade.

As of 2026, almost 90% of companies use AI in their hiring processes to cut hiring time by up to 50%, including using AI-based tools to filter 75% of resumes.

AI could be increasing the opportunities for financial fraud

Systems that can reproduce near perfect counterfeits of legitimate financial documents are increasingly prevalent and sophisticated. The international Association of Certified Fraud Examiners estimate organizations lose about 5% of revenue to fraud each year.

The terms 'bug' and 'debugging' were popularized by Grace Hopper's team

Engineers working on the Mark II computer at Harvard University identified a moth in one of its components in 1947, which caused it to malfunction. Describing the incident as the "first actual case of bug being found," the term first used by Thomas Edison in 1878 to describe faults in an apparatus caught on.

Since computers rely on the distribution of electrons in microchips to represent binary code, cosmic rays and ionizing radiation from radioactive elements can introduce software errors by detaching electrons from atoms.

Can AI revolutionize cancer detection?

Scientists have developed a tool to evaluate the risk of lung cancer, the most deadly form of the disease. Named "Sybil," this AI program was trained using CT scans with clear cancer signs, followed by scans where cancer wasn’t obvious. The tool is able to review CT scan data to predict a patient's of developing a lung cancer within six years. See how it works here.

The mathematics of quantum-based cryptography, and why existing data is already at risk

Seeing the potential of this technology, some governments and malicious actors are thought to have already started storing encrypted data, in the hopes that quantum computers within the next 10 to 20 years will be able to decrypt it. As of 2022, the National Institute of Standards and Technology had selected four algorithms for inclusion in a quantum-resistant encryption standard.

AI tools can reinforce existing societal biases—especially racial ones—because they are trained on human-created data containing implicit and explicit biases.

The first computer virus was created by a USC doctoral candidate in 1983

A year after creating malware in a controlled experiment that multiplied until it took over all of a computer's memory, Fred Cohen went public with his research and its risks. He called the self-replicating software a "computer virus," a term Cohen's advisor, Leonard Adleman, coined to describe its infectious behavior. (Some readers may experience a paywall.)

Algorithms can be designed to train themselves and self-improve without human input, to the point where the optimized code is too complex for programmers to understand.

DeepSeek is a Chinese AI startup that rattled US competitors when it made an AI model much faster and cheaper than they did

DeepSeek unveiled an AI model in 2025 that it said was made in two months for roughly $6M. For context, OpenAI has suggested it took over $100M to make its AI model GPT-4. The news rattled the stocks of American tech companies, including leading chipmaker Nvidia.

Stephen Wolfram on AI's rapid progress & the "Post-Knowledge Work" era

AI, as a concept, has been around since at least the 1950s, but it's only in the last year or so that the technology has been able to equal humans at many tasks -- and in many cases surpass humans. In this podcast, mathematician and computer scientist Stephen Wolfram provides an AI 101, explaining how we got here, how highly advanced chatbots like ChatGPT work, and what our world is in store for next.

Artificial intelligence may further enhance smart city data analysis

Identifying patterns within systems in the Internet of Things may enable AI tools to make informed decisions regarding transportation, safety, and sustainability without human intervention. However, such tools will also need to successfully address concerns about privacy, digital equity, and algorithmic bias.

Read an oral history of the Y2K panic

This oral history brings together programmers, engineers, and officials who first identified the date flaw and then spent years alerting the public to the danger and racing to repair millions of lines of code.

Bias can manifest in algorithmic decisions because the program follows rules that may have been written by individuals unaware of their own biases.