U.S. federal investigators pursuing the ShinyHunters hacking group that recently stole highly sensitive data from the FBI have identified executives at two different ransomware negotiation firms who maintained unusually close relations with the extortion group, KrebsOnSecurity has learned. Sources close to the investigation say one of the executives in the United States even helped ShinyHunters operate a website that pressured victims into paying a ransom.

Hackers aligned with ShinyHunters have claimed credit for dozens of major data breaches since 2019, when the group was primarily operated by cybercriminals living in France. As those original members were arrested or jailed one by one, control over the ShinyHunters brand eventually passed to “Rey,” identified last year as a teenage hacker from Amman, Jordan. According to the FBI, ShinyHunters has extorted more than $70 million from victims so far this year.

In late September, ShinyHunters hacked the FBI recruitment website fbijobs.gov and taunted the agency by threatening to publish the stolen files, which reportedly included medical and psychological records, Social Security numbers and home addresses, and information about each employee’s unit and specialization.

The FBI jobs site hack has prompted the agency to move with uncommon speed to arrest, detain and question people who had any communications with Rey or other ShinyHunters suspects. Reuters reported on October 3 that Rey had been detained by Jordanian authorities and was cooperating with the FBI.

Rey was able to assume control over the hacking group because one of its alleged senior members had just been nabbed. On the evening of September 15, 2026, authorities in the Netherlands arrested convicted hacker and ShinyHunters suspect Pepijn van der Stap. Dutch police reportedly used flash-bang grenades in an effort to stun and apprehend Van der Stap as he was logged in to various online accounts.

According to a source close to the investigation, the authorities arrested Van der Stap while he was using his Signal instant messaging account, which allowed them to read recent communications with frequent contacts — including ransomware negotiators based in Canada and the United States.

Multiple sources with knowledge of the matter told KrebsOnSecurity that Van der Stap’s messages were the key to unraveling a murder-for-hire plot that emerged immediately following his arrest. The Dutch news outlet RTL reported last month the two murders were allegedly to be committed abroad, and that there are indications Van der Stap gave the order for the attacks (there is no indication those orders were ever carried out).

On October 9, KrebsOnSecurity broke the news that the FBI arrested a 54-year-old Canadian cybersecurity executive named Edward Dubrovsky in connection with the ShinyHunters investigation. Dubrovsky, who recently published a book on handling cyber extortion that recommended always engaging with the hackers, was charged with threatening to disclose stolen or hacked data and interfering with interstate commerce through extortion, according to court records. Politico reports prosecutors have asked that Dubrovsky remain in custody pending trial, and that records related to his case remain under seal.

A source close to the investigation told KrebsOnSecurity that Van der Stap’s Signal messages showed he had frequent conversations with Mr. Dubrovsky and with at least one executive at a large U.S.-based consulting firm, and that these executives often knew the details about new ShinyHunters victims before the victims themselves had even learned of the breach.

That source shared another potentially explosive finding by federal investigators: the Dutch hacker’s chats showed Mr. Dubrovsky knew ShinyHunters had stolen highly sensitive data from FBI servers, and that he never informed the bureau of the data breach prior to ShinyHunters threatening on Twitter/X to publish the agency’s deeply personal personnel files.

SHINYWIRE

KrebsOnSecurity has learned from two sources close to the ShinyHunters probe that FBI investigators believe at least one executive at a U.S.-based consulting firm played an active role in operating the now-defunct ShinyHunters victim extortion website “shinywire[.]com.” KrebsOnSecurity is not naming the U.S. company or the suspected executive(s) for now because they have not yet been charged with a crime, but it is a prestigious firm with more than 1,000 associates.

The Shinywire website was active from approximately May 3, 2026 until October 16 and had entries for nearly 100 organizations hacked by ShinyHunters. Each entry urged victims to engage with the cybercrime group and discuss terms for payment. The pages also included samples of stolen files and promised those files would be published online if the victim ignored their demands.

The following screenshot shows a cached portion of the Shinywire extortion page for Intuitive Surgical, an American medical technology company that was breached in March 2026 after an employee fell for a targeted phishing email.

Victim organizations that refused to engage or negotiate after seven days would find all of their stolen data published on the ShinyHunters victim shaming blog (a website that is only reachable via the anonymity network Tor).

In a story published October 15, The Globe and Mail wrote that a few months before Edward Dubrovsky was arrested by the FBI, the Canadian cybersecurity executive alleged in court documents he had become collateral damage in a campaign by a former business partner to “destroy” another industry executive.

The story noted that Dubrovsky filed the lawsuit in Ontario this year against Daniel Tobok, the former chief executive of Cypfer, a cybersecurity firm founded in Toronto with offices in the U.S., Britain, the Netherlands, the United Arab Emirates, and Mexico. The two first became business partners in 2017 and later developed Cypfer together, where Mr. Dubrovsky was a managing partner and chief operating officer. But by 2024, the two had a falling out, with Dubrovsky accusing Tobok of gradually excluding him from business matters and making “increasingly capricious business decisions.”

Mr. Tobok reportedly denied the allegations and filed a counterclaim, accusing Mr. Dubrovsky of accessing confidential information and unfairly competing with Cypfer through work at another firm called CyberSteward Inc. He also said Mr. Dubrovsky has misrepresented himself as a Cypfer founder, and is not a shareholder.

GHOSTBYTE

According to interviews with several former colleagues of Mr. Tobok, as far back as eight years ago he was known to ransomware purveyors by a particular nickname: Ghostbyte. In 2018, a Canadian company was hacked by Dharma, a prolific Russian-language ransomware affiliate program that first surfaced in 2016. The victim company told the Dharma ransomware representative they were leaning toward paying because some of the files were unrecoverable after being locked, but that they needed a better guarantee that Dharma would keep their word if paid.

A redacted copy of the Dharma rep’s reply is pictured below, and it shows the ransomware group urged the victim to contact a person at a Canadian IT firm that helps companies seeking such guarantees. The Dharma rep recommended the victim contact the email address ghostbyte@tutanota.com.

“If you want guarantees, you can write to the IT company,” the Dharma reply advised. “Take the phone and negotiate with them. This IT company often solves such problems in Canada. You can contact them and sign a contract for services. You’ll still have to pay.”

It’s unclear if Mr. Tobok ever used that Ghostbyte address offered by the Dharma representative, and he has not responded to multiple requests for comment. However, the breach tracking service Constella Intelligence finds an email address Mr. Tobok has frequently used — dtobok@me.com — is associated with two online accounts where the chosen username was “ghostbyte,” including a Snapchat account.

Incidentally, Constella also finds the Dharma rep’s email address luckygoodlucky@tuta.io is associated with a cryptocurrency address that processed 38 transactions totaling 43 bitcoin, ~$3.5 million at today’s value (the most recent transaction for this wallet is dated February 2019).

In a statement shared on October 14, Cypfer said the company’s business does not include making or facilitating ransomware payments, and that Cypfer learned of Dubrovsky’s arrest when the news first broke in the media.

“We had no prior knowledge of Mr. Dubrovsky’s arrest,” a spokesperson told KrebsOnSecurity via email. “CYPFER has not received any outreach from law enforcement and, to our knowledge, is not the target of any law enforcement investigation.”

The statement said CyberSteward and Cypfer are separate companies with no officers, directors or employees in common, and that Cypfer was recently acquired by the Apros Group, a holding company led by Jason Hogg, a former FBI special agent and former CEO of Aon Cyber Solutions Group. In 2020, Aon aquired Cytelligence, a security consulting firm where Mr. Tobok was founder and CEO and Mr. Dubrovsky was a managing partner.

CONFLICTING INTERESTS

It is not illegal in the United States to pay hackers for an encryption key to unlock servers infected with ransomware, or in exchange for a promise to delete any stolen files and refrain from publishing them online. While victim organizations can land in legal hot water if they pay a ransom to a group that is sanctioned by the U.S. Treasury Department, ShinyHunters is not among the ransomware groups currently sanctioned by the United States or by any other government.

Experts say ransomware victims are typically directed by their insurance provider to work with a specific incident response firm. But not all cyber forensics and breach response companies will agree to facilitate payments to ransomware groups on behalf of their clients. As a result, once victim firms make a decision to pay, the firms that eschew rewarding extortionists are invariably replaced by companies that have no such qualms.

In December 2024, ShinyHunters began extorting the education technology company PowerSchool, stealing private information on 62 million students and 9.5 million teachers in the U.S. and Canada. PowerSchool reportedly paid a $2.85 million ransom demand, and a “frequently asked questions” statement (PDF) about the breach says PowerSchool was assisted in that effort by CyberSteward (ShinyHunters affiliate Matthew Lane, 20, of Sterling, Mass., was sentenced in November 2025 to four years in prison for his admitted role in the PowerSchool hack).

But that ransom payment did not stop future extortion attempts using the same stolen data. By May 2025, individual school districts were reportedly receiving separate ransom demands using samples of the same stolen files. While nobody has alleged ShinyHunters was behind the second wave of extortion against PowerSchool users, the incident drives home the reality that there is no technical way to verify the threat actors have deleted the stolen data as promised, or prevent others from abusing the same records.

A number of leading cyber incident response firms, including Coveware and Unit 221B, have consistently warned victims against paying ShinyHunters, noting that “destroyed” victim data is often kept, resold or used to extort the company’s clients directly later on.

The FBI echoed those sentiments in an alert on May 15, 2026 advising victims against paying the ShinyHunters group. Meanwhile, ShinyHunters members have been harassing and threatening employees at Coveware and Unit 221B for offering this advice.

The following screenshots show just how annoyed the ShinyHunters leaders were with Coveware’s relentless advice against paying the group. The first image is from a conversation on May 6, 2026 that took place between the group’s main account on Telegram and a co-conspirator. In that chat, snippets of which were shared with KrebsOnSecurity by a trusted source, ShinyHunters complained that Coveware was harming the group’s payment rates.

“Go on my website, majority of those companies would’ve paid if it weren’t for Coveware giving them misinformation,” the ShinyHunters account wrote. “Someone (100% Coveware) is going out of their way to make sure i am not paid.”

In another conversation on April 11, 2026, the ShinyHunters Telegram account privately said they were trying to “eliminate the problem,” which they said was Coveware. Coveware declined to comment for this story.

“I’m sure the company will pay but in this specific case I am aware of Coveware and their involvement, and I’ve probably been not getting a lot of other negotiations mostly likely because Coveware keeps getting involved,” the ShinyHunters account bemoaned.

Mark Camillo is chief executive at CyberAcuView, an industry group founded in 2021 by a consortium of major cyber insurance carriers. Camillo said some firms charge a flat retainer fee, while those that specialize in helping companies negotiate a ransom payment tend to charge a percentage of the total ransom amount paid.

That latter dynamic, he said, is an inherent conflict of interest, as the negotiator has every incentive to make things more favorable to the attackers because they are financially incentivized to negotiate and pay out, and to not negotiate down on the price.

“It has come to the industry’s attention that many of the ransomware payers charge a percentage of the final payment which may or may not be disclosed,” Camillo said. “This makes no sense if they are also the negotiator. There needs to be more transparency in the process as no one in the ecosystem should be profiting purely on the size of the ransomware transaction or payment itself.”

Camillo said the firms doing this work would argue the fees are needed because there may be unexpectedly higher costs to secure larger quantities of cryptocurrency or more risk carrying the float, and because cryptocurrency exchanges may charge higher fees for high risk transactions.

“There definitely needs to be more visibility around the transaction and clarity on the increasing value of the crypto and the exchange’s percent in the transaction to get to the bottom of this and remove any misaligned incentives,” he said.

In recent months, the U.S. Department of Justice has prosecuted a number of ransomware negotiators alleged to have conspired with cybercriminals to defraud their clients. On October 7, the DOJ charged MonsterCloud CEO Zohar Pinhasi, 50, with wire fraud and conspiracy for allegedly charging customers to remediate ransomware attacks without paying the hackers. According to charging documents, Pinhasi secretly went behind his customers’ backs and paid hackers anyway, paying ransomware groups $8 million while charging customers $19 million.

In July, Angelo Martino, a 41-year-old ransomware negotiator from Land O’Lakes, Florida, was sentenced to 70 months in prison for conspiring with the Blackcat/ALPHV ransomware group to extort multiple victims. According to the DOJ, Martino was paid by BlackCat attackers to provide confidential information about the negotiating position and strategy of his employer’s clients and enable the ransomware actors to maximize the ransoms paid by the victims.

Artificial intelligence has gone from a technological novelty to a tool so ordinary that kids in elementary school are already using it. Trying to ban children from using AI at this point makes about as much sense as it once did to ban search engines or Wikipedia.

Today we’re breaking down how chatbots actually work, and how to teach your kid to use AI as a study aid rather than a substitute for thinking for themselves

Explain to your child (and yourself) how AI works

Before explaining anything about artificial intelligence to your child, it helps to understand how it actually works and where its limits are.

Most of the popular chatbots that kids (as well as adults) use are what’s known as large language models, or LLMs. To build such a model, researchers train it to spot patterns across huge amounts of text. The model breaks text down into small units called tokens (each token can be a sequence of words, a punctuation mark, a single word or character, or a whole sentence), then predicts which token is most likely to come next.

The same thing happens when a chatbot converses with a person. A language model doesn’t actually reason on its own. It’s calculating which words and phrases are most likely to follow one another in order to produce a response that fits your query. That means chatbots will sometimes give incomplete or flat-out wrong answers, no matter how often they’re updated or retrained. A vivid example: just two years ago, popular chatbots confidently assured users that the word “strawberry” has two letters “r” instead of three. Mistakes like these are commonly known as hallucinations.

Many chatbots can also search the internet for information. In that case, the AI pulls in data from an outside source, but it still uses its own internal algorithms to put the answer together. And search results themselves can include outdated posts, advertising content, tabloids, and other questionable sources.

How to help your child use AI wisely

Talk to them about how AI can get things wrong

We’ve already seen that artificial intelligence isn’t immune to errors. Unfortunately, hallucinations aren’t the only issue. Even when a chatbot is working from accurate information, it can still present it in a misleading way by leaving something out, putting the emphasis in the wrong place, or drawing a questionable conclusion. Sometimes the result depends on factors a user would never think to consider when asking a question.

One surprising factor is simply what language you’re using. Most of the data that major models are trained on is in English, so these systems tend to perform better in English. The same AI model might answer a question correctly in English but get it wrong when asked the exact same thing in, say, Serbian. This gap shows up most in fields such as science, medicine, technology, and law, where there’s simply far more high-quality English-language material for the model to draw on.

Another quirk of chatbots is that they get worse as you feed them more information. Studies show that the more text and data a model has to work with, the harder it is for it to keep track of details, connect information from different parts of a conversation, and answer questions that require real depth of understanding. Therefore, uploading an entire book to a chatbot and asking detailed questions about it is a surefire way to trigger hallucinations.

It’s also worth questioning not just what the AI tells you, but the way you ask your questions in the first place. Chatbots often try to please the user, and will shape their answer to match the user’s apparent point of view. For example, the same chatbot might give opposite answers to “Why is the Earth overpopulated?” and “Why isn’t the Earth overpopulated?”, each time building its response around the assumption baked into the question. One recent study found that even the most advanced language models failed to challenge false premises in 26–42% of cases.

Let AI help with learning, not do the learning

Many a former student knows that copying others’ work is an art unto itself — one that takes exceptional skill and at least some engagement with the material (sometimes even a fair amount). These days, unfortunately, even that skill is at risk of disappearing: the temptation to copy an answer that took only a few seconds to generate is simply too strong. But chatbots are far from straight-A students themselves.

In one study, researchers had three popular chatbots sit a chemistry exam, and compared their scores to those of real students from 2009–2023. The students came out ahead. Chatbots will sometimes land on the correct answer, but arrive at it via a chain of flawed reasoning, so their answers could just as easily be wrong.

So it’s worth explaining to your child that having AI write essays from scratch, or solve equations without then double-checking them, is a dead end. On top of that, in a real job they’ll be judged on actual skills and knowledge, and chatbots won’t help them fake the expertise. What AI can genuinely help with, without getting in the way of learning, is explaining difficult topics and working through mistakes. Here are some examples:

If a topic is hard to make sense of

• “Explain this topic in simple terms: {insert topic}”
• “Explain this topic using a real-life example: {insert topic}“
• “Here’s the part I don’t understand: {insert text}. Walk me through it step by step”
• “Explain this topic in simple terms first, then in more technical terms: {insert topic}“
• “Here’s a question: {insert question}. Don’t give me the answer. Ask me guiding questions so I can work it out myself”

Math and other exact sciences

• “Give me a hint for the first step, but don’t solve the whole problem: {insert problem}“
• “Look at my solution and tell me where I made a mistake, but don’t fix it for me: {insert problem and solution}“
• “Come up with a similar problem for me to try: {insert problem}“
• “Here’s my answer: {answer}. Ask me questions so I can check whether I solved it correctly”
• “Explain why this formula applies here: {insert problem}“

Essays and written assignments

• “Ask me five questions on this topic so I can come up with my own thesis and arguments: {insert topic}“
• “Here’s a draft of my essay. Try to find gaps in my logic or places that need more detail: {insert draft text}“
• “Look at my essay outline and tell me what’s missing: {insert essay outline}”
• “Point out places in my writing where the idea isn’t clear, but don’t rewrite it for me: {insert text}“
• “Pretend you’re an experienced teacher and try challenging my argument: {insert text}“

Language learning

• “Have a conversation with me in {insert language} at an A2 level. After I respond, correct my mistakes”
• “Give me five sentences to translate using this grammar structure: {insert grammar structure}“
• “Explain this grammar rule and give me some examples: {insert grammar rule}“
• “I’ll write a sentence, and you tell me if it has a mistake. Give me a hint first, and only show me the correct version after my second attempt. {insert sentence}“
• “Write a short dialogue in {insert language} where I have to answer as one of the characters”

Knowledge testing

• “Quiz me with 20 questions on this topic: {insert topic}. Ask one question at a time, and don’t show the answer right away”
• “After each answer, only explain what I got wrong”
• “Start with five easy questions, then ask five hard ones”
• “Make the questions harder each time I answer correctly”
• “Based on my mistakes, tell me which topics I should review”

Fact-check AI responses together

Make sure your child understands that they shouldn’t blindly trust what a chatbot tells them — even when it cites other sources. And asking the same chatbot to double-check its own work isn’t a great idea either; there’s a real chance it will make the same mistakes twice, or even pile up the errors.

Take any chatbot answer and go through it together, breaking it down into individual statements and asking questions about each one. Where did that date or number come from? Is the AI citing a specific person, publication, or study? Is the quote actually something that person said? If the chatbot includes a link, open it and check how accurately the AI actually represented it.

While you’re at it, take a look at the quality of the source itself. If something is written in an overly emotional tone or makes bold claims without backing them up, that’s a good sign to look for information somewhere else.

Protect your child and their accounts

Anything your child shares with a chatbot ends up on that company’s servers, and it’s used to further train the model by default. Over the past few years we’ve seen that even the biggest companies aren’t immune to bugs and data leaks. To lower the risk of your child’s information leaking and to keep their account secure, follow a few simple rules:

• Make sure your child understands that AI isn’t a private diary. Tell them not to share their name, home address, school, or any other sensitive information with a chatbot.
• Adjust the chatbot’s privacy settings. At the very least, turn off memory and any “improve the model” or training features. It’s also worth reading our detailed guides on setting up privacy and security in ChatGPT and DeepSeek.
• Update the password. Most chatbots require an account, so to keep your child’s from being hacked, set a strong password that isn’t reused anywhere else. Whether your child’s password follows the classic “mother’s maiden name plus birth year” formula or uses popular meme words like “skibidi”, we have some bad news: passwords like these get cracked really fast. Instead of coming up with and memorizing passwords, it’s better to let an encrypted password manager generate and store them.
• Turn on two-factor authentication. This adds an extra hurdle for anyone trying to break into your child’s account. Rather than using a phone number as the second factor, it’s better to use login codes generated and automatically refreshed in a password manager.
• Use anti-phishing protection. Beyond fake websites that impersonate legitimate AI services, there’s another risk: chatbots sometimes hand users links to phishing sites (fake pages designed to steal personal information). Kaspersky Premium blocks access to suspicious sites and keeps scammers from stealing your family’s data and money.
• Know what your child is doing online. Along with a password manager and protection from malware and phishing, Kaspersky Premium includes Kaspersky Safe Kids, which lets you set up parental controls, restrict unwanted content, and keep track of how much time your child spends online.

What else to read about keeping kids safe online and at school:

• Keeping kids safe online: a practical guide for parents
• How to help your kid become a blogger without ever worrying about their safety
• How hackers target Gen Z
• Choosing wisely: a guide to your kids’ first gadget
• Back-to-school threats: gadgets
• Back-to-school threats: virtual classrooms and videoconferencing
• Back-to-school threats: social networking
• Back to School Security Tips