We use many technologies to detect email spam. They analyze every part of a message: headers, text, images, and links. But the first thing our email products focus on is the “From” field. If the address contains a domain that’s already on our denylist, it’s an easy call. But spammers know the drill, so they register domains in industrial quantities to make emails, even from a single campaign, arrive from completely different addresses. Clearly, they don’t do that by hand. The process has long been automated, and attackers’ algorithms can register huge numbers of domains in a matter of minutes. But every domain needs a name. Coming up with a unique one for each isn’t feasible. So spammers use domain generation algorithms (DGAs), which create similar names with minor variations, based on date and time, pseudo-random numbers, and so on.
In fact, to block mailings such as these, you don’t need every address on your denylist. We’re good at automating processes, too. For some time now, our email solutions have included a technology that detects domains generated by DGAs, and shuts down entire batch with a single rule. Let me explain how this technology works.
How we detect and block generated domain names in bulk
As soon as any of our technologies flags an email as spam, its address becomes subject to further examination. If it meets a number of criteria, its domain is added to our database of domains we consider “bad”. We built a cascade of classifiers that compares these domains by similarity, groups them into clusters, and creates a short but comprehensive rule that blocks the entire cluster. Our email protection solutions receive this rule with an update. From then on, they block all emails sent from addresses on any domain that matches it — even if those domains have never been used in spam campaigns yet.
Here’s a real-world example. Let’s say the classifier flags several lookalike domains in a collection of “bad” ones it has gathered:
sxkjcc.asia
sxkjcd.asia
sxkjcy.asia
Then, one rule is enough to catch them all: @sxkjc[a-z0-9]\.asia\b
How to protect your company from spam
According to our analysis, 44.9% of emails sent last year were spam. Unwanted, unsolicited emails distract employees, waste working time, and clog inboxes. In some cases, they also contain scam, phishing, or malware content. To protect business customers from this deluge of threats, our solutions for corporate email security have anti-spam technologies that we constantly improve. This includes the technology described above for blocking domains generated by DGAs.
Two critical vulnerabilities have been discovered in the popular PHP programming language. The vulnerabilities, identified as CVE-2026-109618 and CVE-2026-109619, allow attackers to remotely execute code on a server running PHP without authentication. The language’s development team has released updates for all supported branches, but the first attempts to exploit the vulnerabilities were observed even before the patches were published. The exploit has been named PHP2Shell.
The architectures of popular marketplaces, e-commerce platforms, and learning management systems (LMS) are written in PHP. In addition, PHP is used in various content management systems (CMS) such as WordPress, Drupal, Joomla, and so on, which power most websites on the internet.
In this blogpost, we discuss CVE-2026-109618 and CVE-2026-109619 in detail, and explain how companies can protect their infrastructure. Read the Securelist expert blog to learn how events surrounding these vulnerabilities unfolded before the patch was released.
Vulnerable PHP versions
According to PHP web site, versions prior to 8.2.35, 8.3.36, 8.4.27, and 8.5.12 are vulnerable to CVE-2026-109618 and CVE-2026-109619. There are no official fixes for older branches. According to unofficial reports, the vulnerabilities also affect PHP versions starting with 5.4.0.
What makes CVE-2026-109618 and CVE-2026-109619 dangerous
Both vulnerabilities received a score of 9.2 on the CVSS v4.0 scale. They arise because, after a request is interrupted (for example, due to a memory shortage), PHP leaves invalid data in the memory buffer, and subsequently accesses it, allowing code to be executed on the server. One of the confirmed attack chains via CVE-2026-109619 exploits the `session.upload_progress` mechanism (which tracks file upload progress), which is enabled by default in PHP.
By exploiting these vulnerabilities, attackers can remotely execute code on the server with web server process privileges without authentication.
According to PHP developers, all installations of unpatched versions that process web requests are at risk, including PHP-FPM, Apache with mod_php, php-cgi, FrankenPHP, and LiteSpeed. The greatest risk lies with mod_php: researchers demonstrated an exploit for it in which an unauthenticated request triggers the attack even before the application code is executed.
What businesses should do
We’ve compiled key recommendations from the PHP developers and experts at Kaspersky.
Update PHP to versions 8.5.12, 8.4.27, 8.3.36, or 8.2.35. Start with systems accessible from the internet. The source code is available on the PHP website. If PHP is installed from distribution packages or provided by your hosting provider, check for updated packages: Linux distributions and LTS vendors received patches in advance. If the server is managed by a hosting provider, ask them to apply the update.
Versions 8.1 and older no longer receive updates from the PHP project. Check with your distribution vendor or LTS support provider to see if backported fixes are available, or switch to a supported branch. For PHP 7.4, 8.0, and 8.1, the developers have prepared minimal source code backports for manual compilation. These can also be found on the PHP website.
Restart PHP-FPM or the web server so that the processes load the new code, and verify the version using the `php -v` command. If PHP was installed from distribution packages, check the package changelog: the version number may remain the same.
Check the logs from October 20. A warning sign is when the web server process is performing actions it shouldn’t (running command shells, curl, wget, and the like). Kaspersky solutions help detect malicious activity in a timely manner. Attempts to exploit the described RCE vulnerabilities on Linux and Windows machines can be detected using Kaspersky NEXT EDR Expert, while exploitation of PHP2Shell in network traffic is detected by Kaspersky Anti Targeted Attack (KATA) with the NDR module.
If an immediate update is not possible
Only the immediate installation of patches will fully resolve the issue, but if that isn’t possible, it’s recommended to implement temporary measures. PHP developers provide step-by-step instructions for each vulnerability.
Microsoft’s October Patch Tuesday update stands out for two reasons. First, the total number of patches has reached four digits for the first time. Second, to help companies better understand the details of the vulnerabilities, Microsoft has decided to group them into risk cohorts. A cohort brings together vulnerabilities that are exploited under the same conditions and affect the same systems. They replace neither the severity rating, nor the individual bulletins for each CVE. While they’re unlikely to be suitable for comprehensive patch prioritization, they will help quickly assess which part of an infrastructure is currently most at risk.
Statistics corner
Of the 1,414 vulnerabilities addressed in the October Patch Tuesday release, 1,102 directly affect the Windows operating system, 134 — Microsoft Office, 36 — SharePoint Server, 22 — SQL Server, and seven — Exchange Server. The vast majority — 677 — lead to privilege escalation, 317 to remote code execution, 40 to security bypass, and 73 to denial of service (DoS).
There are 86 critical vulnerabilities on the list, of which 68 lead to RCE, 14 to privilege escalation, three to spoofing, and one to data leakage. In addition, two vulnerabilities were disclosed before being patched, and one is being exploited in real-world attacks.
CVE-2026-96951 — vulnerability in Windows Shell
The only zero-day vulnerability patched in the October update that attackers are already exploiting in real-world attacks is CVE-2026-96951 in Windows Shell (CVSS severity score: 8.8). Details on how it’s being exploited aren’t yet known; however, according to Microsoft’s description, it involves command injection that allows bypassing a security feature when accessing the system over a network. Microsoft credits experts at both Fujitsu and Anthropic for discovering the vulnerability, so it was likely found using neural networks.
CVE-2026-108398 and CVE-2026-92707: vulnerabilities disclosed before patches were released
Information about these two vulnerabilities was made public before the October update was released. In theory, this could mean that attackers attempted to exploit them ahead of time. These are:
CVE-2026-108398 — a vulnerability in the .NET Framework that allows bypassing WDAC protection;
CVE-2026-92707 — a vulnerability in the third-party MessagePack-CSharp serialization component, leading to a denial of service (DoS).
However, according to the security bulletins, neither has been observed in actual attacks yet.
What else to pay attention to
Several vulnerabilities patched by the latest update combine potentially serious consequences with relatively simple exploitation. Particular attention should be paid to three flaws that could potentially lead to arbitrary code execution:
Microsoft isn’t the only company that releases regular update packages for newly discovered vulnerabilities.
In its October update, Adobe fixed 174 defects in its products, including critical vulnerabilities (CVSS 10) in ColdFusion and Commerce/Magento. These systems are typically accessible from an external network, making them particularly attractive to attackers.
SAP’s October security bulletin lists 20 vulnerabilities, including CVE-2026-89111 — an SSRF vulnerability in the Cloud SDK for Python. This vulnerability, rated CVSS 10, allows attackers to steal OAuth tokens.
How to keep your company safe?
Recently, thanks to neural networks, the number of vulnerabilities detected and patched has been regularly breaking records. To avoid getting overwhelmed by the flood of patches, we recommend using an automated platform for tracking and assessing vulnerabilities . It helps not only identify and fix vulnerabilities, it also prioritizes them based on actual risks and automates the remediation process.
When it comes to protecting voice calls, security professionals typically focus on the security of the data carrying the audio — above all on encryption. Sooner or later though, the voice data stops being a stream of bits and becomes an analog signal that goes straight to the speakers. You might think that if someone is using headphones, nobody can listen in. But headphones are electronic devices, and their components have physical characteristics that can give away closely guarded secrets without any cyberattack at all. In a recent paper, researchers from two Hong Kong universities came up with InjectEave — an attack that exploits these properties to intercept the already-decoded analog audio signal.
How the InjectEave attack works
The electronic components in headphones convert data into analog form and amplify the signal to drive the speakers. This process generates weak electromagnetic emissions. Capturing those emissions and extracting information from them is a field unto itself, with many experts working in it. The main issue those experts face lies in the fact that stray emissions from headphones are too faint to intercept and analyze in practice.
The authors of InjectEave propose adding a radio transmitter that beams a signal at a specific frequency toward the target device. Those weak emissions from the headphones’ analog components modulate the transmitter’s signal. The altered signal can be intercepted from several meters away — provided the receiving antenna is sensitive enough. The attacker then analyzes the changes in the signal to reconstruct the original audio.
Technical details of the InjectEave attack
The main obstacle to intercepting electromagnetic emissions directly — without an external transmitter — is the relatively low signal frequency. For example, the audio amplifier in headphones operates at frequencies from 20Hz to 20kHz. By contrast, electromagnetic emissions are easiest to intercept at frequencies measured in megahertz. The researchers were able to find a transmitter frequency that would also “amplify” the low-frequency signal of interest.
The researchers ran a series of experiments. They used antennas to transmit the “amplifying” signal and receive the result, and a spectrum analyzer to capture and process the data. The authors have posted the raw recordings of the intercepted signals on a dedicated microsite, so anyone can check how well the attack works. In most cases, getting a usable result didn’t even require complex processing or recognition. Conversations picked up from wireless headphones are clearly intelligible despite the elevated noise level.
In total, the authors tested three models of wireless headphones and two models of basic wired headsets. The effective eavesdropping distance varied from one to six meters depending on the model. With a wired telephone, they were able to demonstrate two-way interception. They could hear not only the sound from the speakers but also the potential victim’s speech, which the headset’s built-in microphone and analog-to-digital converter had captured.
Advantages and limitations of the method
Any attack that requires getting very close to the target is challenging by definition. The need to tune the frequency of the “amplifying” radio signal for each specific device makes the attack even harder. In their experiments, the researchers had to use frequencies from one to eight megahertz.
Despite these caveats, the main achievement of this attack is its being able to target the quirks of the analog components in an electronic device. These components inherently produce stray emissions, and there’s almost no way to reduce them.
Similar surveillance methods that intercept a digital signal can be stopped by adding a layer of encryption. When it comes to the analog side of a device, protecting the signal itself is much harder. This research is another reminder of how complex and unpredictable even very simple, familiar devices can be. Sometimes even the strongest protection for digital data is powerless once that information is converted to analog form.
Threat intelligence teams are heading into 2027 with more than ever to monitor, driven by an acceleration in the AI threat landscape. However, their expanded mandate typically doesn’t come with a proportional budget windfall. Instead, teams are under pressure to get greater value from the intelligence, systems, and analyst expertise already available to them.
New independent research commissioned by Flashpoint shows where that pressure is being felt most. Alert noise and false positives are the most frequently cited challenges affecting threat intelligence program effectiveness, followed by analyst bandwidth, changes in the threat landscape, and integration across security tools. At the same time, AI-related threats have emerged as the leading area where practitioners want to expand intelligence coverage.
These findings point to some of the decisions that will shape threat intelligence programs in the year ahead: where to focus finite analyst capacity, how intelligence requirements need to evolve, and where AI can meaningfully support the work. Organizations are already evaluating agentic AI across threat intelligence workflows, but the research also shows that greater autonomy comes with higher expectations around accuracy, control, and measurable value.
For security leaders planning their 2027 threat intelligence strategy and budgets, this report examines:
Which operational challenges are putting the greatest pressure on analyst capacity and threat intelligence programs today.
How intelligence requirements are changing as AI-enabled threats create new coverage priorities.
How threat intelligence budgets are trending in the next 12 months, and where incremental budgets will go.
Where practitioners see the strongest applications for agentic AI, and which AI-powered workflows face a higher threshold for trust and autonomy.
What teams need to prove before expanding AI investment, including the metrics practitioners are using to evaluate time savings, accuracy, risk reduction, and ROI.
Together, the findings provide a practical benchmark for teams evaluating their own intelligence operations: where to focus limited analyst time, where automation may have the greatest impact, and what to measure as AI becomes part of the workflow.
Executive Summary
As threat intelligence teams plan for 2027, the research shows them managing two simultaneous pressures: intelligence requirements are expanding, while the capacity available to address them remains constrained. AI is intensifying that pressure as adversaries adopt new capabilities, but also becoming part of how organizations are thinking about scaling their ability to defend their organizations.
“2027 will test whether threat intelligence programs can scale their reach as fast as AI is reshaping the threat landscape. The opportunity for agentic AI on the defensive side is significant, and our research shows security leaders are ready to invest. But they aren’t swayed by the hype. They’re holding agentic AI to a high bar, and they want to know where it improves their work, whether they can trust the output, and where human judgment must remain part of the process.”
Josh Lefkowitz Co-Founder and CEO, Flashpoint
Four findings stand out:
The AI threat landscape is accelerating faster than threat intelligence teams can keep up. Alert noise and false positives were the most frequently cited challenge, identified by 65% of respondents, while 55% cited insufficient analyst bandwidth and half pointed to integration gaps across their security tools.
Threat intelligence budgets are rising to close the gap. Half of respondents expect their budgets to increase over the next 12 months, including 35% anticipating meaningful increases of 10% or more. Another 40% expect spending to remain essentially flat. No respondents anticipate a decrease. Where budgets are growing, organizations are directing investment toward areas including AI-related threat coverage, additional intelligence feeds and modules, and broader program development.
AI-related threats are the leading priority for expanded coverage and new investment. Practitioners pointed to deepfake detection, AI-accelerated vulnerability exploitation, and emerging AI-enabled threat vectors as areas where their intelligence requirements are expanding.
Agentic AI adoption is underway, but teams are setting a high bar. Just 25% of organizations interviewed are using agentic AI in production today. Another 20% are piloting it, while 35% are planning adoption within the next 12 to 24 months. Rather than rushing toward autonomy, teams are evaluating where agents can improve workflows while maintaining intelligence quality, appropriate controls, and human oversight.
Taken together, the findings show where threat intelligence teams are feeling the greatest pressure, where they see opportunities to extend their capabilities with AI, and what will determine whether those tools earn a lasting place in the intelligence workflow. The sections that follow examine each of these findings in greater detail and what they mean for threat intelligence programs.
Operational Challenges Are Putting Pressure on Analyst Capacity
The rising AI threat landscape is increasing pressure on organizations to get more capacity from the people and systems they already have. Threat intelligence leaders therefore say the biggest barriers to program effectiveness are operational: too much noise, limited analyst bandwidth, and disconnected tools.
Alert noise and false positives emerged as the leading challenge affecting threat intelligence programs, cited by 65% of respondents. Analyst bandwidth followed at 55%, while half of respondents pointed to both the evolving threat landscape and integration gaps across their security tools.
The impact of these challenges becomes clearer at the workflow level. One healthcare practitioner described receiving alerts about email breaches that were seven or eight years old, with the same data resurfacing each time it was resold and requiring another investigation. Practitioners also described manual steps between intelligence feeds, SIEMs, SOAR platforms, ticketing systems, and response workflows.
Each unnecessary investigation or manual handoff consumes time that could otherwise go toward analysis. That becomes difficult to solve through hiring alone because threat intelligence expertise takes time to develop. As one healthcare threat intelligence leader explained:
“Good analysts take time to develop and train, and we can’t hire our way out of that problem.”
The research suggests an opportunity to look for capacity within the intelligence workflow itself. Reducing repetitive investigations, improving integration between systems, and helping analysts identify relevant intelligence faster can return more of their time to work that depends on context and judgment.
Threat Intelligence Investment Remains Resilient Heading Into 2027
The rise of AI-driven threats is driving increases in the intelligence budgets of many organizations. Among respondents who provided a budget outlook, 50% expect their threat intelligence budgets to increase over the next 12 months, while the remaining 40% expect budgets to stay essentially flat. None anticipate a decrease.
More than a third (35%) expect meaningful growth of at least 10%, suggesting that for many organizations, threat intelligence remains an area of active investment heading into 2027.
Where organizations are increasing investment is equally important. Respondents expecting meaningful growth described spending on AI defense capabilities, expanded threat intelligence feeds and modules, and broader program development. AI-related threat coverage also emerged across both flat and growing budgets as the most frequently cited area where teams want to expand coverage.
For teams heading into 2027, the findings suggest that the question is increasingly where to direct threat intelligence investment for the greatest impact. Organizations are evaluating how existing resources can address new intelligence requirements, where additional capabilities are needed, and which investments can demonstrate clear operational value.
AI-Related Threats Are Driving New Coverage Priorities
As organizations decide where to focus threat intelligence investment in the year ahead, AI is the clear priority. AI-related threats were the most frequently cited area where organizations want to expand intelligence coverage, including among teams with relatively flat budgets. Practitioners specifically pointed to deepfake detection, AI-accelerated vulnerability exploitation, and emerging AI-enabled threat vectors.
These priorities reflect activity already visible across the threat landscape. During the first half of 2026, Flashpoint tracked more than 22 million threat actor posts discussing, sharing, or advertising AI toolkits for criminal deployment. Flashpoint analysts observed AI being used across phishing, malware development, evasion, and vulnerability exploitation.
For threat intelligence teams, expanding AI coverage requires more specificity than treating “AI threats” as a single monitoring category. Deepfakes may matter because of executive impersonation or fraud. AI-accelerated exploitation may change vulnerability monitoring and prioritization. New attack tooling may affect the adversaries or TTPs a team already tracks.
The challenge extends beyond visibility into these threats. Organizations also need the expertise to understand how AI is changing adversary behavior and attack chains. That expertise is still developing. As one practitioner put it, “everybody’s learning it at the same time.”
Connecting those developments to existing PIRs and business risks gives analysts clearer collection priorities and helps teams determine what new sources, expertise, and monitoring they actually need.
Those requirements are also expanding at a time when analyst bandwidth is already constrained, creating a second question for threat intelligence leaders: where can AI support the intelligence workflow itself?
Agentic AI Adoption Is Underway
Nearly half of the organizations interviewed are already using or piloting agentic AI in threat intelligence workflows, while another 35% are planning adoption within the next 12 to 24 months. That makes agentic AI an important part of 2027 planning, but despite the attention surrounding it, threat intelligence teams are taking a measured approach. They are looking for clear business value, evidence that AI can maintain the quality of their intelligence workflows, and appropriate human oversight as agents take on more consequential tasks.
The work being handed to agents already varies considerably, from information retrieval and alert prioritization to more active tasks such as blocking IP addresses, updating blacklists, and monitoring suspicious traffic. Organizations in the pilot stage also described use cases spanning detection, validation, context generation, SIEM alert analysis, TIP querying, MITRE ATT&CK mapping, and reversible response actions.
As agents move from retrieving and analyzing information to taking actions that can directly affect security operations, accuracy and trust become increasingly important. One respondent captured the concern succinctly:
“An agent that’s confidently wrong is worse than one that’s slow.”
That concern is reflected across the research. Practitioners raised questions around data leakage, auditability, regulatory requirements, cost, and the level of control agents should have over security workflows. One retail respondent, for example, would only consider read-only AI assistants because of the risks associated with granting agents write permissions.
For teams evaluating agentic AI, that makes the workflow itself an important part of the decision. Tasks with reviewable outputs, reversible actions, and clear success criteria provide a practical starting point. As autonomy increases, teams need equally clear controls around what an agent can access, what it can change, where human approval is required, and how its actions are audited.
What the Findings Mean for Threat Intelligence Programs
As threat intelligence leaders plan for 2027, the research points to a clear set of priorities. Teams have more to monitor, limited analyst capacity, and growing interest in AI as a way to scale the work their programs already perform.
The findings also show that practitioners are selective about where they see that value. PIR coverage monitoring and intelligence briefings can extend coverage and reduce recurring work while keeping analysts involved in the intelligence process. As agents take on more consequential actions, questions around accuracy, permissions, auditability, and human oversight become more important.
For teams setting priorities for the year ahead, that creates a practical path forward: understand where analyst capacity is being consumed, connect new intelligence requirements to the risks that matter to the organization, identify workflows where automation can meaningfully extend the team, and establish clear measures of success before deployment.
The challenge for 2027 will be turning expanding intelligence requirements into an operating model that can scale for threat intelligence programs. That means making better use of existing data, systems, and analyst expertise while being deliberate about where agentic AI can extend those capabilities and where human judgment remains essential.
Methodology
Flashpoint commissioned independent qualitative research among a select group of more than 20 threat intelligence budget owners and influencers at large North American enterprises across financial services and insurance, healthcare and biotech, retail, and information technology and telecommunications during the early fall of 2026.
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