The easy access that scammers have to sophisticated AI tools means everything from emails to video calls can’t be trusted.

Imagine you meet someone new. Be it on a dating app or social media, you chance across each other online and get to talking. They’re genuine and relatable, so you quickly take it out of the DMs to a platform like Telegram or WhatsApp. You exchange photos and even video call each over. You start to get comfortable. Then, suddenly, they bring up money.

They need you to cover the cost of their Wi-Fi access, maybe. Or they’re trying out this new cryptocurrency. You should really get in on it early! And then, only after it’s too late, you realize that the person you were talking to was in fact not real at all.

They were a real-time AI-generated deepfake hiding the face of someone running a scam.

This scenario might sound too dystopian or science-fictional to be true, but it has happened to countless people already. With the spike in the capabilities of generative AI over the past few years, scammers can now create realistic fake faces and voices to mask their own in real time. And experts warn that those deepfakes can supercharge a dizzying variety of online scams, from romance to employment to tax fraud.

David Maimon, the head of fraud insights at identity verification firm SentiLink and a professor of criminology at Georgia State University, has been tracking the evolution of AI romance scams and other kinds of AI fraud for the past six years. “We’re seeing a dramatic increase in the volume of deepfakes, especially in comparison to 2023 and 2024,” Maimon says.

“It wasn’t a whole lot. We’re talking about maybe four or five a month,” he says. “Now, we’re seeing hundreds of these on a monthly basis across the board, which is mind-boggling.”

Deepfakes are already being used in a variety of online scams. One finance worker in Hong Kong, for example, paid $25 million to a scammer posing as the company’s chief financial officer in a deepfaked video call. Some deepfake scammers have even posted instructional videos on YouTube, which have a disclaimer as being for “pranks and educational purposes only.” Those videos usually open with a romance scam call, where an AI-generated handsome young man is talking to an older woman.

More traditional deepfakes—such as a pre-rendered video of a celebrity or politician, rather than a live fake—have also become more prevalent. Last year, a retiree in New Zealand lost around $133,000 to a cryptocurrency investment scam after seeing a Facebook advertisement featuring a deepfake of the country’s prime minister encouraging people to buy in.

Maimon says SentiLink has started to see deepfakes used to create bank accounts in order to lease an apartment or engage in tax refund fraud. He says an increasing number of companies have also seen deepfakes in video job interviews.

“ Anything that requires folks to be online and which supports the opportunity of swapping faces with someone—that will be available and open for fraud to take advantage of,” Maimon says.

Part of the reason for this increase is that the barriers for creating deepfakes are getting lower. There are a lot of easily accessible AI tools that can generate realistic faces and a lot of tools that can animate those faces or create full-length videos out of them. Scammers often use images and videos of real people, deepfaked to slightly change their faces or alter what they’re saying, to target their loved ones or hijack their public influence.

Matt Groh, a professor of management at Northwestern University who researches people’s ability to detect deepfakes, says that point-and-click generative AI tools make it much easier to make small, believable changes to already-existing media.

“If there’s an image of you on the internet, that would be enough to manipulate a face to look like it’s saying something that you haven’t said before or doing something you haven’t done before,” Groh says.

It’s not just fake video that you need to be worried about. With a few clips of audio, it’s also possible to make a believable copy of somebody’s voice. One study in 2023 found that humans failed to detect deepfake audio over a quarter of the time.

“ Just a single image and five seconds of audio online mean that it’s definitely possible for a scammer to make some kind of realistic deepfake of you,” Groh says.

Deepfakes are becoming more pervasive in contexts other than outright scams. Social media has been flooded over the past year with AI-generated “influencers” stealing content from adult creators by deepfaking new faces onto their bodies and monetizing the resulting videos. Deepfakes have even bled over into geopolitics, like when the mayors of multiple European capital cities held video calls with a fake version of the mayor of Kyiv, Ukraine. People have started using deepfakes for personal reasons, like bringing back a dead relative or creating an avatar of a victim to testify in court.

So, if deepfakes are everywhere, how do you spot one? The answer is not technology. A number of technology companies, including OpenAI, have launched deepfake detection tools. Researchers have also proposed mechanisms to detect deepfakes based on things like light reflected in a person’s eyes or inconsistent facial movements, and have started investigating how to implement them in real time.

But those models often cannot reliably detect different kinds of AI fakes. OpenAI’s model, for example, is specifically designed only to report content generated with the company’s own Dall-E 3 tool but not other image generation models.

There’s also the risk that scammers can abuse AI detectors by repeatedly tweaking their content until it fools the software.

“ The major thing we have to understand is that the technology we have right now is not good enough to detect those deepfakes,” Maimon says. “ We’re still very much behind.”

For now, as video deepfakes get more popular, the best way to detect one relies on humans. Studies on deepfake detection show that people are best at distinguishing whether videos are real or fake, as opposed to just audio or text content, and are in some cases even better than leading detection models.

Groh’s team conducted one study which found that taking more time to determine whether an image was real or fake led to a significant increase in accuracy, by up to eight percentage points for just 10 seconds of viewing time.

“ This sounds almost so simple,” Groh says. “But if you spend just a couple extra seconds, that leads to way higher rates of being able to distinguish an image as real or fake. One of the ways for any regular person to just be a little bit less susceptible to a scam is to ask, ‘Does this look actually real?’ And if you just do that for a few extra seconds, we’re all going to be a little bit better off.”

Deepfakes’ popularity could be a double-edged sword for scammers, Groh says. The more widespread they are, the more people will be familiar with them and know what to look for.

That familiarity has paid off in some cases. Last summer, a Ferrari executive received a call from someone claiming to be the CEO of the company. The person convincingly emulated the CEO’s voice but abruptly hung up the call when the executive tried to verify their identity by asking what book the CEO had recommended just days earlier. The CEO of WPP, the world’s biggest advertising agency, was also unsuccessfully targeted by a similar deepfake scam.

“I think there’s a balancing act going on,” Groh says. “ We definitely have technology today that is generally hard for people to identify. But at the same time, once you know that there’s a point-and-click tool that allows you to transform one element into something else, everyone becomes a lot more skeptical.”

Deepfake Scams Are Distorting Reality Itself

In the very near future, victory will belong to the savvy blackhat hacker who uses AI to generate code at scale.

In the near future one hacker may be able to unleash 20 zero-day attacks on different systems across the world all at once. Polymorphic malware could rampage across a codebase, using a bespoke generative AI system to rewrite itself as it learns and adapts. Armies of script kiddies could use purpose-built LLMs to unleash a torrent of malicious code at the push of a button.

Case in point: as of this writing, an AI system is sitting at the top of several leaderboards on HackerOne—an enterprise bug bounty system. The AI is XBOW, a system aimed at whitehat pentesters that “autonomously finds and exploits vulnerabilities in 75 percent of web benchmarks,” according to the company’s website.

AI-assisted hackers are a major fear in the cybersecurity industry, even if their potential hasn’t quite been realized yet. “I compare it to being on an emergency landing on an aircraft where it’s like ‘brace, brace, brace’ but we still have yet to impact anything,” Hayden Smith, the cofounder of security company Hunted Labs, tells WIRED. “We’re still waiting to have that mass event.”

Generative AI has made it easier for anyone to code. The LLMs improve every day, new models spit out more efficient code, and companies like Microsoft say they’re using AI agents to help write their codebase. Anyone can spit out a Python script using ChatGPT now, and vibe coding—asking an AI to write code for you, even if you don’t have much of an idea how to do it yourself—is popular; but there’s also vibe hacking.

“We’re going to see vibe hacking. And people without previous knowledge or deep knowledge will be able to tell AI what it wants to create and be able to go ahead and get that problem solved,” Katie Moussouris, the founder and CEO of Luta Security, tells WIRED.

Vibe hacking frontends have existed since 2023. Back then, a purpose-built LLM for generating malicious code called WormGPT spread on Discord groups, Telegram servers, and darknet forums. When security professionals and the media discovered it, its creators pulled the plug.

WormGPT faded away, but other services that billed themselves as blackhat LLMs, like FraudGPT, replaced it. But WormGPT’s successors had problems. As security firm Abnormal AI notes, many of these apps may have just been jailbroken versions of ChatGPT with some extra code to make them appear as if they were a stand-alone product.

Better then, if you’re a bad actor, to just go to the source. ChatGPT, Gemini, and Claude are easily jailbroken. Most LLMs have guard rails that prevent them from generating malicious code, but there are whole communities online dedicated to bypassing those guardrails. Anthropic even offers a bug bounty to people who discover new ones in Claude.

“It’s very important to us that we develop our models safely,” an OpenAI spokesperson tells WIRED. “We take steps to reduce the risk of malicious use, and we’re continually improving safeguards to make our models more robust against exploits like jailbreaks. For example, you can read our research and approach to jailbreaks in the GPT-4.5 system card, or in the OpenAI o3 and o4-mini system card.”

Google did not respond to a request for comment.

In 2023, security researchers at Trend Micro got ChatGPT to generate malicious code by prompting it into the role of a security researcher and pentester. ChatGPT would then happily generate PowerShell scripts based on databases of malicious code.

“You can use it to create malware,” Moussouris says. “The easiest way to get around those safeguards put in place by the makers of the AI models is to say that you’re competing in a capture-the-flag exercise, and it will happily generate malicious code for you.”

Unsophisticated actors like script kiddies are an age-old problem in the world of cybersecurity, and AI may well amplify their profile. “It lowers the barrier to entry to cybercrime,” Hayley Benedict, a Cyber Intelligence Analyst at RANE, tells WIRED.

But, she says, the real threat may come from established hacking groups who will use AI to further enhance their already fearsome abilities.

“It’s the hackers that already have the capabilities and already have these operations,” she says. “It’s being able to drastically scale up these cybercriminal operations, and they can create the malicious code a lot faster.”

Moussouris agrees. “The acceleration is what is going to make it extremely difficult to control,” she says.

Hunted Labs’ Smith also says that the real threat of AI-generated code is in the hands of someone who already knows the code in and out who uses it to scale up an attack. “When you’re working with someone who has deep experience and you combine that with, ‘Hey, I can do things a lot faster that otherwise would have taken me a couple days or three days, and now it takes me 30 minutes.’ That's a really interesting and dynamic part of the situation,” he says.

According to Smith, an experienced hacker could design a system that defeats multiple security protections and learns as it goes. The malicious bit of code would rewrite its malicious payload as it learns on the fly. “That would be completely insane and difficult to triage,” he says.

Smith imagines a world where 20 zero-day events all happen at the same time. “That makes it a little bit more scary,” he says.

Moussouris says that the tools to make that kind of attack a reality exist now. “They are good enough in the hands of a good enough operator,” she says, but AI is not quite good enough yet for an inexperienced hacker to operate hands-off.

“We’re not quite there in terms of AI being able to fully take over the function of a human in offensive security,” she says.

The primal fear that chatbot code sparks is that anyone will be able to do it, but the reality is that a sophisticated actor with deep knowledge of existing code is much more frightening. XBOW may be the closest thing to an autonomous “AI hacker” that exists in the wild, and it’s the creation of a team of more than 20 skilled people whose previous work experience includes GitHub, Microsoft, and a half a dozen assorted security companies.

It also points to another truth. “The best defense against a bad guy with AI is a good guy with AI,” Benedict says.

For Moussouris, the use of AI by both blackhats and whitehats is just the next evolution of a cybersecurity arms race she’s watched unfold over 30 years. “It went from: ‘I’m going to perform this hack manually or create my own custom exploit,’ to, ‘I’m going to create a tool that anyone can run and perform some of these checks automatically,’” she says.

“AI is just another tool in the toolbox, and those who do know how to steer it appropriately now are going to be the ones that make those vibey frontends that anyone could use.”

The Rise of ‘Vibe Hacking’ Is the Next AI Nightmare

A major cyberattack on the US electrical grid has long worried security experts. Such an attack wouldn’t be easy. But if an adversary pulled it off, it’d be lights out in more ways than one.

When the lights went out across the Iberian Peninsula in April, everything ground to a halt. Scores of people were trapped in Madrid’s underground metro system. Hospitals in Lisbon had to switch to emergency generators. Internet service as far away as Greenland and Morocco went down.

While the cause remains unclear, the actual damage to the Iberian power grid—and the people it serves—was relatively minor. Less than 24 hours after the outage began, the region’s electricity operators managed to get the grid back online.

Even if things could have been much worse, the outage was both an unnerving reminder of how suddenly things can go offline.

For years, cybersecurity professionals, watchdogs, and government agencies have warned that a malicious cyberattack on the US power grid could be devastating. With ample evidence that state-sponsored hacking groups are eyeing the decentralized and deeply vulnerable power grid, the risk is more acute than ever.

Case in point: Hackers, believed to be linked to the Chinese government, spent years exploiting vulnerabilities in critical infrastructure across the mainland United States and Guam to obtain access to their systems. The operations, dubbed Volt Typhoon, could have used this access to shut down or disconnect parts of the American power grid—throwing millions into the dark. The effort was, luckily, disrupted and the vulnerabilities patched. Still, it is an unnerving illustration of just how vulnerable the electric system truly is.

We know what such a hack could look like. In 2015, Ukraine experienced the world’s first large-scale cyberattack on an electrical grid. A Russian military intelligence unit known as Sandworm disconnected various substations from the central grid and knocked hundreds of thousands of people offline.

The attack on Ukraine was repaired quickly, but cybersecurity experts have been warning for years that the next one might be more devastating.

Unlike Ukraine, America does not have a single power grid—it has three large interconnections, broken down into a network of smaller regional systems, some of which stretch into Canada. Most of the East is on one grid, most of the West is on another, while Texas and Alaska run their own interconnections. Keeping these networks running is a wildly complicated effort: There are thousands of utility operations, tens of thousands of substations, and hundreds of thousands of miles of high-voltage transmission lines.

Photograph: Michael Tessier

To some degree, this decentralized network is an asset, as it means there is no core vulnerability that risks knocking the entire country offline. But the interconnections mean that a failure in one corner of the grid could cause a cascade that takes down the entire system.

In 2018, researchers from Northwestern University ran large-scale models, gaming out what would happen if parts of the grid failed. They found that, generally, the American power grid was resilient. However, they found that about 10 percent of power lines in the US were susceptible to the kind of failure that could trigger this domino effect under some conditions. A 2022 study that looked at possible disruptions to the Texas grid also found that, in some cases, a relatively small disruption could cause a series of downstream outages “rapidly in succession.”

This means that even if malicious actors manage to take only a small number of nodes in the network offline, it has the potential to do enormous downstream damage.

Insurance underwriter Lloyd’s of London has looked at the effects of such an outage. In this hypothetical, first drafted in 2015 but updated in the years since, Lloyd’s estimates that a Trojan virus that manages to infect just 50 generators—removing 10 percent of the grid’s total power—can trigger this cascade effect and knock out power for most of the East Coast, including New York City and Washington, DC. The Lloyd’s report states that this is an “extreme” but “not unrealistic scenario.”

“Images of a dark New York City make front pages worldwide,” they write, “accompanied by photographs of citizens stuck underground for hours on stranded subway cars and in elevators in the summer heat.”

These rolling blackouts would stretch through 36 states over the course of a day, throwing some 93 million people into the dark. It could take up to three days for half of those people to get back online—while hardware damage and other problems could require up to three weeks to fix.

As the outages continue, more difficulties arise. The analysts warn that an information campaign running parallel to the cyberattack could prompt strikes, protests, or general unrest.

In 2016, then Federal Emergency Management Agency administrator Craig Fugate was summoned to Congress to testify on the possible impacts of a cyberattack on the US electric grid. Water and wastewater systems are some of the first things to go down, he noted. “There is not really a good way to manage that if those systems go offline for extensive periods of time,” Fugate said.

He explained that the emergency response will become a game of triage: distributing enough power, gas, and generators to emergency services and utilities, while also trying to keep consumer-facing supply chains operating.

“Can you get enough life support and infrastructure going to keep the major supply lines up?” Fugate continued. “You are not going to have everything. You are not going to have what the normal consumption rates are.”

Lloyd’s estimates that the total economic costs and losses could hit $1 trillion.

The US Grid Attack Looming on the Horizon

A quantum computer will likely one day be able to break the encryption protecting the world's secrets. See how much faster such a machine could decrypt a password compared to a present-day supercomputer.

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Shift in cyberattack focus puts APAC region under growing pressure.

India, China and the US were the top DDoS attacks targets in Q1 2025, with APAC facing over half of global attacks, according to new data from cybersecurity firm StormWall.

A new report from cybersecurity firm StormWall reveals that nearly half of all DDoS attacks in the first quarter of 2025 were aimed at China, India and the United States. Instead of focusing heavily on the usual targets, attackers are now directing more of their efforts toward infrastructure in the Asia-Pacific region.

According to StormWall’s analytics center, India topped the chart with 18.1% of the global DDoS traffic, followed by China at 16.2% and the US at 14.7%. Japan (12.3%) and Taiwan (10.2%) rounded out the top five. When it comes to industry-wise attacks, Telecommunications became the top target in APAC, with 136% year-over-year (YOY) growth.

Four of the five most targeted countries now sit in the APAC region, with 57% of all malicious requests focused there. This shows that attackers are moving their focus away from the usual targets in Europe and North America, at least for now. The reasons may be geopolitical, economic, or a combination of both.

“You can always count on US targets attracting a lot of attention,” said Ramil Khantimirov, founder of StormWall. It’s the world’s largest GDP and hackers are always going to target that, but we’ve never seen so much activity in APAC and so densely concentrated.”

According to StormWall’s report shared with Hackread.com ahead of publishing on Thursday, the main force behind these attacks is botnets. These networks of infected devices were responsible for nearly 70% of all DDoS traffic tracked by StormWall in the first quarter of 2025.

Botnets, often run by state-sponsored groups, hacktivists, or organized cyber criminals, allow attackers to direct massive traffic at targeted systems. One such botnet identified in the report, named “Eleven11bot,” used a network of 86,000 compromised IP cameras and DVRs. It was frequently used in politically motivated campaigns.

While APAC faced an increase in DDoS attacks, other regions haven’t been spared. Belgium, though much smaller, experienced significant attacks, with 9.7% of global DDoS activity aimed at its national digital infrastructure, including MyGov.be and the Walloon Parliament. Saudi Arabia was also hit hard, at 6.8%, largely due to pro-Palestinian attacks linked to ongoing regional tensions.

Countries like Italy, Russia, and Switzerland appeared further down the list with lower attack volumes, but experts caution that even minor increases in activity can pose serious threats to unprepared networks.

Via StormWall

StormWall’s data paints a picture of a fast-evolving threat environment, driven by automation, political motives, and shifting strategic interests. While botnet-powered campaigns grow in scale, the need for mitigation services is more important than ever.

However, separate research from NordPass shows that many industries still fall short on basic cybersecurity. In most cases, weak and easily guessed passwords like 123456 and passwords@, are still in use, making systems vulnerable to breaches and turning devices into part of larger botnets.

StormWall Reveals India, China and US Faced Most DDoS Attacks in Q1 2025

kro (Kube Resource Orchestrator) 0.1.0 before 0.2.1 allows users (with permission to create or modify ResourceGraphDefinition resources) to supply arbitrary container images. This can lead to a confused-deputy scenario where kro's controllers deploy and run attacker-controlled images, resulting in unauthenticated remote code execution on cluster nodes.

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.

Attack complexity: More severe for the least complex attacks.

Privileges required: More severe if no privileges are required.

User interaction: More severe when no user interaction is required.

Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.

Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.

Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.

Availability: More severe when the loss of impacted component availability is highest.

GHSA-7633-x85h-5mqh: kro Confused Deputy vulnerability

Hewlett Packard Enterprise (HPE) has released security updates to address as many as eight vulnerabilities in its StoreOnce data backup and deduplication solution that could result in an authentication bypass and remote code execution.
"These vulnerabilities could be remotely exploited to allow remote code execution, disclosure of information, server-side request forgery, authentication bypass,

HPE Issues Security Patch for StoreOnce Bug Allowing Remote Authentication Bypass

Organizations need to implement these five essential security controls to safely harness the power of autonomous AI agents while still protecting enterprise assets.

How to Approach Security in the Era of AI Agents

Hibernate Validator before 6.2.0 and 7.0.0, by default and depending how it is used, may interpolate user-supplied input in a constraint violation message with Expression Language. This could allow an attacker to access sensitive information or execute arbitrary Java code. Hibernate Validator as of 6.2.0 and 7.0.0 no longer interpolates custom constraint violation messages with Expression Language and strongly recommends not allowing user-supplied input in constraint violation messages. CVE-2020-5245 and CVE-2025-4428 are examples of related, downstream vulnerabilities involving Expression Language intepolation of user-supplied data.

**Exploitability Metrics**

Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.

Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.

Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.

Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.

User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.

**Vulnerable System Impact Metrics**

Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.

Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).

Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.

**Subsequent System Impact Metrics**

Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.

Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).

Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.

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