Quantum computers are coming, with a potential computing power almost beyond comprehension.
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Marketed as ChatGPT enhancement and productivity tools, the extensions allow the threat actor to access the victim's ChatGPT data.
The post Chrome, Edge Extensions Caught Stealing ChatGPT Sessions appeared first on SecurityWeek.
The cybercriminals in control of Kimwolf — a disruptive botnet that has infected more than 2 million devices — recently shared a screenshot indicating they’d compromised the control panel for Badbox 2.0, a vast China-based botnet powered by malicious software that comes pre-installed on many Android TV streaming boxes. Both the FBI and Google say they are hunting for the people behind Badbox 2.0, and thanks to bragging by the Kimwolf botmasters we may now have a much clearer idea about that.
Our first story of 2026, The Kimwolf Botnet is Stalking Your Local Network, detailed the unique and highly invasive methods Kimwolf uses to spread. The story warned that the vast majority of Kimwolf infected systems were unofficial Android TV boxes that are typically marketed as a way to watch unlimited (pirated) movie and TV streaming services for a one-time fee.
Our January 8 story, Who Benefitted from the Aisuru and Kimwolf Botnets?, cited multiple sources saying the current administrators of Kimwolf went by the nicknames “Dort” and “Snow.” Earlier this month, a close former associate of Dort and Snow shared what they said was a screenshot the Kimwolf botmasters had taken while logged in to the Badbox 2.0 botnet control panel.
That screenshot, a portion of which is shown below, shows seven authorized users of the control panel, including one that doesn’t quite match the others: According to my source, the account “ABCD” (the one that is logged in and listed in the top right of the screenshot) belongs to Dort, who somehow figured out how to add their email address as a valid user of the Badbox 2.0 botnet.
The control panel for the Badbox 2.0 botnet lists seven authorized users and their email addresses. Click to enlarge.
Badbox has a storied history that well predates Kimwolf’s rise in October 2025. In July 2025, Google filed a “John Doe” lawsuit (PDF) against 25 unidentified defendants accused of operating Badbox 2.0, which Google described as a botnet of over ten million unsanctioned Android streaming devices engaged in advertising fraud. Google said Badbox 2.0, in addition to compromising multiple types of devices prior to purchase, also can infect devices by requiring the download of malicious apps from unofficial marketplaces.
Google’s lawsuit came on the heels of a June 2025 advisory from the Federal Bureau of Investigation (FBI), which warned that cyber criminals were gaining unauthorized access to home networks by either configuring the products with malware prior to the user’s purchase, or infecting the device as it downloads required applications that contain backdoors — usually during the set-up process.
The FBI said Badbox 2.0 was discovered after the original Badbox campaign was disrupted in 2024. The original Badbox was identified in 2023, and primarily consisted of Android operating system devices (TV boxes) that were compromised with backdoor malware prior to purchase.
KrebsOnSecurity was initially skeptical of the claim that the Kimwolf botmasters had hacked the Badbox 2.0 botnet. That is, until we began digging into the history of the qq.com email addresses in the screenshot above.
An online search for the address 34557257@qq.com (pictured in the screenshot above as the user “Chen“) shows it is listed as a point of contact for a number of China-based technology companies, including:
–Beijing Hong Dake Wang Science & Technology Co Ltd.
–Beijing Hengchuang Vision Mobile Media Technology Co. Ltd.
–Moxin Beijing Science and Technology Co. Ltd.
The website for Beijing Hong Dake Wang Science is asmeisvip[.]net, a domain that was flagged in a March 2025 report by HUMAN Security as one of several dozen sites tied to the distribution and management of the Badbox 2.0 botnet. Ditto for moyix[.]com, a domain associated with Beijing Hengchuang Vision Mobile.
A search at the breach tracking service Constella Intelligence finds 34557257@qq.com at one point used the password “cdh76111.” Pivoting on that password in Constella shows it is known to have been used by just two other email accounts: daihaic@gmail.com and cathead@gmail.com.
Constella found cathead@gmail.com registered an account at jd.com (China’s largest online retailer) in 2021 under the name “陈代海,” which translates to “Chen Daihai.” According to DomainTools.com, the name Chen Daihai is present in the original registration records (2008) for moyix[.]com, along with the email address cathead@astrolink[.]cn.
Incidentally, astrolink[.]cn also is among the Badbox 2.0 domains identified in HUMAN Security’s 2025 report. DomainTools finds cathead@astrolink[.]cn was used to register more than a dozen domains, including vmud[.]net, yet another Badbox 2.0 domain tagged by HUMAN Security.
A cached copy of astrolink[.]cn preserved at archive.org shows the website belongs to a mobile app development company whose full name is Beijing Astrolink Wireless Digital Technology Co. Ltd. The archived website reveals a “Contact Us” page that lists a Chen Daihai as part of the company’s technology department. The other person featured on that contact page is Zhu Zhiyu, and their email address is listed as xavier@astrolink[.]cn.
A Google-translated version of Astrolink’s website, circa 2009. Image: archive.org.
Astute readers will notice that the user Mr.Zhu in the Badbox 2.0 panel used the email address xavierzhu@qq.com. Searching this address in Constella reveals a jd.com account registered in the name of Zhu Zhiyu. A rather unique password used by this account matches the password used by the address xavierzhu@gmail.com, which DomainTools finds was the original registrant of astrolink[.]cn.
The very first account listed in the Badbox 2.0 panel — “admin,” registered in November 2020 — used the email address 189308024@qq.com. DomainTools shows this email is found in the 2022 registration records for the domain guilincloud[.]cn, which includes the registrant name “Huang Guilin.”
Constella finds 189308024@qq.com is associated with the China phone number 18681627767. The open-source intelligence platform osint.industries reveals this phone number is connected to a Microsoft profile created in 2014 under the name Guilin Huang (桂林 黄). The cyber intelligence platform Spycloud says that phone number was used in 2017 to create an account at the Chinese social media platform Weibo under the username “h_guilin.”
The public information attached to Guilin Huang’s Microsoft account, according to the breach tracking service osintindustries.com.
The remaining three users and corresponding qq.com email addresses were all connected to individuals in China. However, none of them (nor Mr. Huang) had any apparent connection to the entities created and operated by Chen Daihai and Zhu Zhiyu — or to any corporate entities for that matter. Also, none of these individuals responded to requests for comment.
The mind map below includes search pivots on the email addresses, company names and phone numbers that suggest a connection between Chen Daihai, Zhu Zhiyu, and Badbox 2.0.
This mind map includes search pivots on the email addresses, company names and phone numbers that appear to connect Chen Daihai and Zhu Zhiyu to Badbox 2.0. Click to enlarge.
The idea that the Kimwolf botmasters could have direct access to the Badbox 2.0 botnet is a big deal, but explaining exactly why that is requires some background on how Kimwolf spreads to new devices. The botmasters figured out they could trick residential proxy services into relaying malicious commands to vulnerable devices behind the firewall on the unsuspecting user’s local network.
The vulnerable systems sought out by Kimwolf are primarily Internet of Things (IoT) devices like unsanctioned Android TV boxes and digital photo frames that have no discernible security or authentication built-in. Put simply, if you can communicate with these devices, you can compromise them with a single command.
Our January 2 story featured research from the proxy-tracking firm Synthient, which alerted 11 different residential proxy providers that their proxy endpoints were vulnerable to being abused for this kind of local network probing and exploitation.
Most of those vulnerable proxy providers have since taken steps to prevent customers from going upstream into the local networks of residential proxy endpoints, and it appeared that Kimwolf would no longer be able to quickly spread to millions of devices simply by exploiting some residential proxy provider.
However, the source of that Badbox 2.0 screenshot said the Kimwolf botmasters had an ace up their sleeve the whole time: Secret access to the Badbox 2.0 botnet control panel.
“Dort has gotten unauthorized access,” the source said. “So, what happened is normal proxy providers patched this. But Badbox doesn’t sell proxies by itself, so it’s not patched. And as long as Dort has access to Badbox, they would be able to load” the Kimwolf malware directly onto TV boxes associated with Badbox 2.0.
The source said it isn’t clear how Dort gained access to the Badbox botnet panel. But it’s unlikely that Dort’s existing account will persist for much longer: All of our notifications to the qq.com email addresses listed in the control panel screenshot received a copy of that image, as well as questions about the apparently rogue ABCD account.
Understanding how threat hunting differs from reactive security provides a deeper understanding of the role, while hinting at how it will evolve in the future.
The post Cyber Insights 2026: Threat Hunting in an Age of Automation and AI appeared first on SecurityWeek.
We're taking part in Copyright Week, a series of actions and discussions supporting key principles that should guide copyright policy. Every day this week, various groups are taking on different elements of copyright law and policy, and addressing what's at stake, and what we need to do to make sure that copyright promotes creativity and innovation.
Long before generative AI, copyright holders warned that new technologies for reading and analyzing information would destroy creativity. Internet search engines, they argued, were infringement machines—tools that copied copyrighted works at scale without permission. As they had with earlier information technologies like the photocopier and the VCR, copyright owners sued.
Courts disagreed. They recognized that copying works in order to understand, index, and locate information is a classic fair use—and a necessary condition for a free and open internet.
Today, the same argument is being recycled against AI. It’s whether copyright owners should be allowed to control how others analyze, reuse, and build on existing works.
U.S. courts have long recognized that copying for purposes of analysis, indexing, and learning is a classic fair use. That principle didn’t originate with artificial intelligence. It doesn’t disappear just because the processes are performed by a machine.
Copying works in order to understand them, extract information from them, or make them searchable is transformative and lawful. That’s why search engines can index the web, libraries can make digital indexes, and researchers can analyze large collections of text and data without negotiating licenses from millions of rightsholders. These uses don’t substitute for the original works; they enable new forms of knowledge and expression.
Training AI models fits squarely within that tradition. An AI system learns by analyzing patterns across many works. The purpose of that copying is not to reproduce or replace the original texts, but to extract statistical relationships that allow the AI system to generate new outputs. That is the hallmark of a transformative use.
Attacking AI training on copyright grounds misunderstands what’s at stake. If copyright law is expanded to require permission for analyzing or learning from existing works, the damage won’t be limited to generative AI tools. It could threaten long-standing practices in machine learning and text-and-data mining that underpin research in science, medicine, and technology.
Researchers already rely on fair use to analyze massive datasets such as scientific literature. Requiring licenses for these uses would often be impractical or impossible, and it would advantage only the largest companies with the money to negotiate blanket deals. Fair use exists to prevent copyright from becoming a barrier to understanding the world. The law has protected learning before. It should continue to do so now, even when that learning is automated.
One court has already shown how these cases should be analyzed. In Bartz v. Anthropic, the court found that using copyrighted works to train an AI model is a highly transformative use. Training is a kind of studying how language works—not about reproducing or supplanting the original books. Any harm to the market for the original works was speculative.
The court in Bartz rejected the idea that an AI model might infringe because, in some abstract sense, its output competes with existing works. While EFF disagrees with other parts of the decision, the court’s ruling on AI training and fair use offers a good approach. Courts should focus on whether training is transformative and non-substitutive, not on fear-based speculation about how a new tool could affect someone’s market share.
Workers’ concerns about automation and displacement are real and should not be ignored. But copyright is the wrong tool to address them. Managing economic transitions and protecting workers during turbulent times are core functions of government. Copyright law doesn’t help with those tasks in the slightest. Expanding copyright control over learning and analysis won’t stop new forms of worker automation—it never has. But it will distort copyright law and undermine free expression.
Broad licensing mandates may also do harm by entrenching the current biggest incumbent companies. Only the largest tech firms can afford to negotiate massive licensing deals covering millions of works. Smaller developers, research teams, nonprofits, and open-source projects will all get locked out. Copyright expansion won’t restrain Big Tech—it will give it a new advantage.
Learning from prior work is foundational to free expression. Rightsholders cannot be allowed to control it. Courts have rejected that move before, and they should do so again.
Search, indexing, and analysis didn’t destroy creativity. Nor did the photocopier, nor the VCR. They expanded speech, access to knowledge, and participation in culture. Artificial intelligence raises hard new questions, but fair use remains the right starting point for thinking about training.
In this post, we identify and analyze the top threat actors that have been actively targeting the financial sector between 2024 and 2026.
Table Of ContentsBetween 2024 and 2026, Flashpoint analysts have observed the financial sector as a top target of threat actors, with 406 publicly disclosed victims falling prey to ransomware attacks alone—representing seven percent of all ransomware victim listings during that period.
However, ransomware is just one piece of the complex threat actor puzzle. The financial sector is also grappling with threats stemming from sophisticated Advanced Persistent Threat (APT) groups, the risks associated with third-party compromises, the illicit trade in initial access credentials, the ever-present danger of insider threats, and the emerging challenge of deepfake and impersonation fraud.
The financial sector has long been one of the most attractive targets for threat actors, consistently ranking among the most targeted industries globally.
These institutions manage massive volumes of sensitive data—from high-value financial transactions and confidential customer information to vast sums of capital, making them especially lucrative for threat actors seeking financial gain. Additionally, the urgency and criticality of financial operations increases the chances that victim organizations will succumb to extortion and ransom demands.
Even beyond direct financial incentives, the financial sector remains an attractive target due to its deep interconnectivity with other industries.This means that malicious actors may simply target financial institutions to gain information about another target organization, as a single data breach can have far-reaching and cascading consequences for involved partners and third parties.
To understand the complexities of the financial threat landscape, organizations need a comprehensive understanding of the key players involved. The following threat actors represent some of the most prominent and active groups targeting the financial sector between April 2024 and April 2025:
Despite being a relatively new Ransomware-as-a-Service (RaaS) group that emerged in February 2024, RansomHub quickly rose to prominence, becoming the second-most active ransomware group in 2024. Notably, they claimed 38 victims in the financial sector between April 2024 and April 2025. Their known TTPs include phishing and exploiting vulnerabilities. RansomHub is also known to heavily target the healthcare sector.
Active since March 2023, Akira has demonstrated increasingly sophisticated tactics and has targeted a significant number of victims across various sectors. Between April 2024 and April 2025, they targeted 34 organizations within the financial sector. Evidence suggests a potential link to the defunct Conti ransomware group. Akira commonly gains initial access through compromised credentials, Virtual Private Network (VPN) vulnerabilities, and Remote Desktop Protocol (RDP). They employ a double extortion model, exfiltrating data before encryption.
A long-standing and highly prolific RaaS group operating since at least September 2019, LockBit continued to be a major threat to the financial sector, claiming 29 publicly disclosed victims between April 2024 and April 2025. LockBit utilizes various initial access methods, including phishing, exploitation of known vulnerabilities, and compromised remote services.
Most notably, in June 2024, LockBit claimed it gained access to the US Federal Reserve, stating that they exfiltrated 33 TB of data. However, Flashpoint analysts found that the data posted on the Federal Reserve listing appears to belong to another victim, Evolve Bank & Trust.
This financially motivated threat actor group, originating from Eastern Europe and active since at least 2015, focuses on stealing payment card data. They employ social engineering tactics and create elaborate infrastructure to achieve their goals, reportedly generating over $1 billion USD in revenue between 2015 and 2021. Their targets within the financial sector include interbank transfer systems (SWIFT, SAP), ATM infrastructure, and point-of-sale (POS) terminals. Initial access is often gained through phishing and exploiting public-facing applications.
Emerging in 2022, Scattered Spider has quickly become known for its rapid exploitation of compromised environments, particularly targeting financial services, cryptocurrency services, and more. They are notorious for using SMS phishing and fake Okta single sign-on pages to steal credentials and move laterally within networks. Their primary motivation is financial gain.
This advanced persistent threat (APT) group, backed by the North Korean government, has demonstrated a broad range of targets, including cryptocurrency exchanges and financial institutions. Their campaigns are driven by financial profit, cyberespionage, and sabotage. Lazarus Group employs sophisticated spear-phishing emails, malware disguised in image files, and watering-hole attacks to gain initial access.
Between April 2024 and April 2025, our analysts observed 6,406 posts pertaining to financial sector access listings within Flashpoint’s forum collections. How are these prolific threat actor groups gaining a foothold into financial data and systems? Examining Flashpoint intelligence, malicious actors are capitalizing on third-party compromises, initial access brokers, insider threats, amongst other attack vectors:
Ransomware attacks targeting third-party vendors can have a direct and significant impact on financial institutions through data exposure and compromised credentials. The Clop ransomware gang’s exploitation of the MOVEit vulnerability in December 2024 serves as a stark reminder of this risk.
Initial Access Brokers specialize in gaining initial access to networks and selling these access credentials to other threat groups, including ransomware operators. Their tactics include phishing, the use of information-stealing malware, and exploiting RDP credentials, posing a significant risk to financial entities. Between April 2024 and April 2025, analysts observed 6,406 posts pertaining to financial sector access listings within Flashpoint’s forum collections.
Malicious insiders, whether recruited or acting independently, can provide direct access to sensitive data and systems within financial institutions. Telegram has emerged as a prominent platform for advertising and recruiting insider services targeting the financial sector.
The increasing sophistication and accessibility of AI tools are enabling new forms of fraud. Deepfakes can bypass traditional security measures by creating convincing audio and video impersonations. While still evolving, this threat vector, along with other impersonation tactics like BEC and vishing, presents a growing concern for the financial sector. Within the past year, analysts observed 1,238 posts across fraud-related Telegram channels discussing impersonation of individuals working for financial institutions.
The financial sector remains a high-value target, facing a persistent and evolving array of threats. Understanding the tactics, techniques, and procedures (TTPs) of these top threat actors, as well as the broader threat landscape, is crucial for financial institutions to develop and implement effective security strategies.
Flashpoint is proud to offer a dedicated threat intelligence solution for banks and financial institutions. Our platform combines comprehensive data collection, AI-powered analysis, and expert human insight to deliver actionable intelligence, safeguarding your critical assets and operations. Request a demo today to see how our intelligence can empower your security team.
We're taking part in Copyright Week, a series of actions and discussions supporting key principles that should guide copyright policy. Every day this week, various groups are taking on different elements of copyright law and policy, and addressing what's at stake, and what we need to do to make sure that copyright promotes creativity and innovation.
Copyright owners increasingly claim more draconian copyright law and policy will fight back against big tech companies. In reality, copyright gives the most powerful companies even more control over creators and competitors. Today’s copyright policy concentrates power among a handful of corporate gatekeepers—at everyone else’s expense. We need a system that supports grassroots innovation and emerging creators by lowering barriers to entry—ultimately offering all of us a wider variety of choices.
Pro-monopoly regulation through copyright won’t provide any meaningful economic support for vulnerable artists and creators. Because of the imbalance in bargaining power between creators and publishing gatekeepers, trying to help creators by giving them new rights under copyright law is like trying to help a bullied kid by giving them more lunch money for the bully to take.
Entertainment companies’ historical practices bear out this concern. For example, in the late-2000’s to mid-2010’s, music publishers and recording companies struck multimillion-dollar direct licensing deals with music streaming companies and video sharing platforms. Google reportedly paid more than $400 million to a single music label, and Spotify gave the major record labels a combined 18 percent ownership interest in its now- $100 billion company. Yet music labels and publishers frequently fail to share these payments with artists, and artists rarely benefit from these equity arrangements. There’s no reason to think that these same companies would treat their artists more fairly now.
In the AI era, copyright may seem like a good way to prevent big tech from profiting from AI at individual creators’ expense—it’s not. In fact, the opposite is true. Developing a large language model requires developers to train the model on millions of works. Requiring developers to license enough AI training data to build a large language model would limit competition to all but the largest corporations—those that either have their own trove of training data or can afford to strike a deal with one that does. This would result in all the usual harms of limited competition, like higher costs, worse service, and heightened security risks. New, beneficial AI tools that allow people to express themselves or access information.
For giant tech companies that can afford to pay, pricey licensing deals offer a way to lock in their dominant positions in the generative AI market by creating prohibitive barriers to entry.
Legacy gatekeepers have already used copyright to stifle access to information and the creation of new tools for understanding it. Consider, for example, Thomson Reuters v. Ross Intelligence, the first of many copyright lawsuits over the use of works train AI. ROSS Intelligence was a legal research startup that built an AI-based tool to compete with ubiquitous legal research platforms like Lexis and Thomson Reuters’ Westlaw. ROSS trained its tool using “West headnotes” that Thomson Reuters adds to the legal decisions it publishes, paraphrasing the individual legal conclusions (what lawyers call “holdings”) that the headnotes identified. The tool didn’t output any of the headnotes, but Thomson Reuters sued ROSS anyways. A federal appeals court is still considering the key copyright issues in the case—which EFF weighed in on last year. EFF hopes that the appeals court will reject this overbroad interpretation of copyright law. But in the meantime, the case has already forced the startup out of business, eliminating a would-be competitor that might have helped increase access to the law.
Requiring developers to license AI training materials benefits tech monopolists as well. For giant tech companies that can afford to pay, pricey licensing deals offer a way to lock in their dominant positions in the generative AI market by creating prohibitive barriers to entry. The cost of licensing enough works to train an LLM would be prohibitively expensive for most would-be competitors.
The Digital Millennium Copyright Act’s “anti-circumvention” provision is another case in point. Congress ostensibly passed the DMCA to discourage would-be infringers from defeating Digital Rights Management (DRM) and other access controls and copy restrictions on creative works.
Section 1201 has been used to block competition and innovation in everything from printer cartridges to garage door openers
In practice, it’s done little to deter infringement—after all, large-scale infringement already invites massive legal penalties. Instead, Section 1201 has been used to block competition and innovation in everything from printer cartridges to garage door openers, videogame console accessories, and computer maintenance services. It’s been used to threaten hobbyists who wanted to make their devices and games work better. And the problem only gets worse as software shows up in more and more places, from phones to cars to refrigerators to farm equipment. If that software is locked up behind DRM, interoperating with it so you can offer add-on services may require circumvention. As a result, manufacturers get complete control over their products, long after they are purchased, and can even shut down secondary markets (as Lexmark did for printer ink, and Microsoft tried to do for Xbox memory cards.)
Giving rights holders a veto on new competition and innovation hurts consumers. Instead, we need balanced copyright policy that rewards consumers without impeding competition.
aiFWall is a firewall protection for AI deployments built to use AI to improve its own performance.
The post aiFWall Emerges From Stealth With an AI Firewall appeared first on SecurityWeek.
Of course, organizations see risk. It’s just that they struggle to turn insight into timely, safe action. That gap is why exposure management has emerged, and also why it is now becoming a foundational security discipline. What the diagram makes clear is that risk doesn’t stay flat while organizations deliberate. From the moment an exposure is discovered and is reachable, exploitable, and known – the clock starts ticking. As time passes, environments change, dependencies grow, and attackers adapt faster. Remediation workflows fall behind. Manual coordination, unclear ownership, and fear of disruption all extend what is increasingly referred to as ‘exposure […]
The post Why Exposure Management Is Becoming a Security Imperative appeared first on Check Point Blog.
Impacting Anthropic’s official MCP server, the vulnerabilities can be exploited through prompt injections.
The post Anthropic MCP Server Flaws Lead to Code Execution, Data Exposure appeared first on SecurityWeek.
The two bugs, an arbitrary file read and an SSRF bug, can be exploited without user interaction to leak credentials, databases, and other data.
The post Chainlit Vulnerabilities May Leak Sensitive Information appeared first on SecurityWeek.
A simple payload allowed attackers to create a new event leaking summaries of the victim’s private meetings.
The post Weaponized Invite Enabled Calendar Data Theft via Google Gemini appeared first on SecurityWeek.
Information sharing is necessary for efficient cybersecurity, and is widespread; but never quite perfect in practice.
The post Cyber Insights 2026: Information Sharing appeared first on SecurityWeek.
We've known that social engineering would get AI wings. Now, at the beginning of 2026, we are learning just how high those wings can soar.
The post Cyber Insights 2026: Social Engineering appeared first on SecurityWeek.
In this post, we break down the 91,321 instances of insider activity observed by Flashpoint in 2025, examine the top five cases that defined the year, and provide the technical and behavioral red flags your team needs to monitor in 2026.
Table Of ContentsEvery organization houses sensitive assets that threat actors actively seek. Whether it is proprietary trade secrets, intellectual property, or the personally identifiable information (PII) of employees and customers, these datasets are the lifeblood of the modern enterprise—and highly lucrative commodities within the illicit underground.
In 2025, Flashpoint observed 91,321 instances of insider recruiting, advertising, and threat actor discussions involving insider-related illicit activity. This underscores a critical reality—it is far more efficient for threat actors to recruit an “insider” to circumvent multi-million dollar security stacks than it is to develop a complex exploit from the outside.
An insider threat, any individual with authorized access, possesses the unique ability to bypass traditional security gates. Whether driven by financial gain, ideological grievances, or simple human error, insiders can potentially compromise a system with a single keystroke. To protect our customers from this internal risk, Flashpoint monitors the illicit forums and marketplaces where these threats are being solicited.
In this post, we unpack the evolving insider threat landscape and what it means for your security strategy in 2026. By analyzing the volume of recruitment activity and the specific industries being targeted, organizations can move from a reactive posture to a proactive defense.
Last year, Flashpoint collected and researched:
On average, 1,162 insider-related posts were published per month, with Telegram continuing to be one of the most prominent mediums for insiders and threat actors to identify and collaborate with each other. Analysts also identified instances of extortionist groups targeting employees at organizations to financially motivate them to become insiders.
The telecommunications industry observed the most insider-related activity in 2025. This is due to the industry’s central role in identity verification and its status as the primary target for SIM swapping—a fraudulent technique where threat actors convince employees of a mobile carrier to link a victim’s phone number to a SIM card controlled by the attacker. This allows the threat actor to receive all the victim’s calls and texts, allowing them to bypass SMS-based two-factor authentication.
Flashpoint analysts identified 12,783 notable posts where the level of detail or the specific target was particularly concerning.
Top Industries for Insiders Advertising Services (Supply):
Top Industries for Threat Actors Soliciting Access (Demand):
The following cases highlight the variety of ways insiders impacted enterprise systems this year, ranging from intentional fraud to massive technical oversights.
| Type of Incident | Description |
| Malicious | Approximately nine employees accessed the personal information of over 94,000 individuals, making illegal purchases using changed food stamp cards. |
| Nonmalicious | An unprotected database belonging to a Chinese IoT firm leaked 2.7 billion records, exposing 1.17 TB of sensitive data and plaintext passwords. |
| Malicious | An insider at a well-known cybersecurity organization was terminated after sharing screenshots of internal dashboards with the Scattered Lapsus$ Hunters threat actor group. |
| Malicious | An employee working for a foreign military contractor was bribed to pass confidential information to threat actors. |
| Malicious | A third-party contractor for a cryptocurrency firm sold customer data to threat actors and recruited colleagues into the scheme, leading to the termination of 300 employees and the compromise of 69,000 customers. |
| Malicious | Two contractors accessed and deleted sensitive documents and dozens of databases belonging to the Internal Revenue Service and US General Services Administration. |
Potential insiders often display technical and nontechnical behavior before initiating illicit activity. Although these actions may not directly implicate an employee, they can be monitored, which may lead to inquiries or additional investigations to better understand whether the employee poses an elevated risk to the organization.
Flashpoint has identified the following nontechnical warning signs associated with insiders:
The following are technical warning signs:
As 2026 unfolds, insider threat actors will continue to be a major threat to organizations. Ransomware groups and initial access threat actors will continue recruiting interested insiders and exploiting human vulnerabilities through social engineering tactics. Following Telegram’s recent bans on many illicit groups and channels, Flashpoint assesses that threat actors are likely to migrate to different platforms, such as Signal, where encrypted chats make their activity harder to monitor.
As AI technologies continue to advance, organizations will be better equipped to identify and mitigate insider risks. At the same time, threat actors will likely increasingly abuse AI and other tools to access sensitive information.
Is your organization equipped to spot the warning signs? Request a demo to learn more and to mitigate potential risk from within your organization.
The post Insider Threats: Turning 2025 Intelligence into a 2026 Defense Strategy appeared first on Flashpoint.
Vibe coding generates a curate’s egg program: good in parts, but the bad parts affect the whole program.
The post Vibe Coding Tested: AI Agents Nail SQLi but Fail Miserably on Security Controls appeared first on SecurityWeek.
Written by: Nic Losby
Mandiant is publicly releasing a comprehensive dataset of Net-NTLMv1 rainbow tables to underscore the urgency of migrating away from this outdated protocol. Despite Net-NTLMv1 being deprecated and known to be insecure for over two decades—with cryptanalysis dating back to 1999—Mandiant consultants continue to identify its use in active environments. This legacy protocol leaves organizations vulnerable to trivial credential theft, yet it remains prevalent due to inertia and a lack of demonstrated immediate risk.
By releasing these tables, Mandiant aims to lower the barrier for security professionals to demonstrate the insecurity of Net-NTLMv1. While tools to exploit this protocol have existed for years, they often required uploading sensitive data to third-party services or expensive hardware to brute-force keys. The release of this dataset allows defenders and researchers to recover keys in under 12 hours using consumer hardware costing less than $600 USD. This initiative highlights the amplified impact of combining Mandiant's frontline expertise with Google Cloud's resources to eliminate entire classes of attacks.
This post details the generation of the tables, provides access to the dataset for community use, and outlines critical remediation steps to disable Net-NTLMv1 and prevent authentication coercion attacks.
Net-NTLMv1 has been widely known to be insecure since at least 2012, following presentations at DEFCON 20, with cryptanalysis of the underlying protocol dating back to at least 1999. On Aug. 30, 2016, Hashcat added support for cracking Data Encryption Standard (DES) keys using known plaintext, further democratizing the ability to attack this protocol. Rainbow tables are almost as old, with the initial paper on rainbow tables published in 2003 by Philippe Oechslin, citing an earlier iteration of a time-memory trade-off from 1980 by Martin Hellman.
Essentially, if an attacker can obtain a Net-NTLMv1 hash without Extended Session Security (ESS) for the known plaintext of 1122334455667788, a cryptographic attack, referred to as a known plaintext attack (KPA), can be applied. This guarantees recovery of the key material used. Since the key material is the password hash of the authenticating Active Directory (AD) object—user or computer—the attack results can quickly be used to compromise the object, often leading to privilege escalation.
A common chain attackers use is authentication coercion from a highly privileged object, such as a domain controller (DC). Recovering the password hash of the DC machine account allows for DCSync privileges to compromise any other account in AD.
The unsorted dataset can be downloaded using gsutil -m cp -r gs://net-ntlmv1-tables/tables . or through the Google Cloud Research Dataset portal.
The SHA512 hashes of the tables can be checked by first downloading the checksums gsutil -m cp gs://net-ntlmv1-tables/tables.sha512 . then checked by sha512sum -c tables.sha512. The password cracking community has already created derivative work and is also hosting the ready to use tables.
Once a Net-NTLMv1 hash has been obtained, the tables can be used with historical or modern reinventions of rainbow table searching software such as rainbowcrack (rcrack), or RainbowCrack-NG on central processing units (CPUs) or a fork of rainbowcrackalack on graphics processing units (GPUs). The Net-NTLMv1 hash needs to be preprocessed to the DES components using ntlmv1-multi as shown in the next section.
Most attackers will use Responder with the --lm and --disable-ess flags and set the authentication to a static value of 1122334455667788 to only allow for connections with Net-NTLMv1 as a possibility. Attackers can then wait for incoming connections or coerce authentication using a tool such as PetitPotam or DFSCoerce to generate incoming connections from DCs or lower privilege hosts that are useful for objective completion. Responses can be cracked to retrieve password hashes of either users or computer machine accounts. A sample workflow for an attacker is shown below in Figure 1, Figure 2, and Figure 3.
Figure 1: DFSCoerce against a DC
Figure 2: Net-NTLMv1 hash obtained for DC machine account
Figure 3: Parse Net-NTLMv1 hash to DES parts
Figure 4 illustrates the processing of the Net-NTLMv1 hash to the DES ciphertexts.
Figure 4: Net-NTLMv1 hash to DES ciphertexts
An attacker then takes the split-out ciphertexts to crack the keys used based on the known plaintext of 1122334455667788 with the steps of loading the tables shown in Figure 5 and cracking results in Figure 6 and Figure 7.
Figure 5: Loading DES components for cracking
Figure 6: First hash cracked
Figure 7: Second hash cracked and run statistics
An attacker can then calculate the last remaining key with ntlmv1-multi once again, or look it up with twobytes, to recreate the full NT hash for the DC account with the last key part shown in Figure 8.
Figure 8: Calculate remaining key
The result can be checked with hashcat's NT hash shucking mode, -m 27000, as shown in Figure 9.
Figure 9: Keys checked with hash shucking
An attacker can then use the hash to perform a DCSync attack targeting a DC and authenticating as the now compromised machine account. The attack flow uses secretsdump.py from the Impacket toolsuite and is shown in Figure 10.
Figure 10: DCSync attack performed
Organizations should immediately disable the use of Net-NTLMv1.
"Local Security Settings" > "Local Policies" > "Security Options" > “Network security: LAN Manager authentication level" > "Send NTLMv2 response only".
"Computer Configuration" > "Policies" > "Windows Settings" > "Security Settings" > "Local Policies" > "Security Options" > "Network Security: LAN Manager authentication level" > "Send NTLMv2 response only"
As these are local to the computer configurations, attackers can and have set the configuration to a vulnerable state to then fix the configuration after their attacks have completed with local administrative access. Monitoring and alerting of when and where Net-NTLMv1 is used is needed in addition to catching these edge cases.
Filter Event Logs for Event ID 4624: "An Account was successfully logged on." > "Detailed Authentication Information" > "Authentication Package" > "Package Name (NTLM only)", if "LM" or "NTLMv1" is the value of this attribute, LAN Manager or Net-NTLMv1 was used.
This project was inspired by and referenced the following research published to blogs, social media, and code repositories.
Thank you to everyone who helped make this blog post possible, including but not limited to Chris King and Max Gruenberg.
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