The great thing about standards is there’s so many to pick from, right?. (Insert obligatory XKCD #927 here.) Several companies have developed naming schemes to refer to groups of attacks, and now Google has too.

Sometimes, malware, ransomware, or exploit groups name themselves: “Shinyhunters”, “LapSus$”, “Cl0p”, “Lockbit”, and so on. For the groups that don’t advertise their presence, identification and naming can be more difficult. Mostly state-run affairs that didn’t want to draw attention to themselves, these Advanced Persistent Threats (APT) groups were originally simply given numbers. APT28 refers to the Russian GRU Intelligence Directorate suspected of hacking the Democratic Party email servers, APT38 is a North Korean governmental agency involved in financial and crypto currency theft.

Multiple companies and agencies have developed naming schemes to make referring to threat groups easier, typically including a regional identifier as well. CrowdStrike naming uses name groups like “Bear” group for suspected Russian actors, “Panda” for China, “Spider” for unattributed crime groups, while Microsoft uses “Blizzard”, “Typhoon”, and “Tempest”.

Google, deciding there weren’t enough classifications already, now introduces “RELIC”, “CASTLE”, and “COMET”. Of course, each naming organization has dozens of other classification groups as well, but now the next time you hear about an attack being attributed to “Scattered Spider” you know it’s the CrowdStrike name for a crime group.

Critical OpenWRT DHCP Flaw Fixed

The Hacker News reports on a major release of OpenWRT which includes fixes to the odhcpd server, the embedded DHCPv4 and DHCPv6 server written by OpenWRT.

The bug is a straight-forward stack style attack where a buffer is allocated, but not length-checked against the data required to reply to a malformed request. OpenWRT runs on a wide range of devices, but one of the most popular legacy platforms still in use, the MIPS processor architecture, lacks most modern security protections against stack-based attacks, making this attack sting more than it might on other architectures.

The latest OpenWRT releases also fix issues in the uhttpd embedded web server, multiple issues in the LUCI web configuration interface that allowed attackers to inject cross-site scripting content and Linux kernel security fixes. If you run a direct OpenWRT build on your equipment, update! These bugs will have an extremely long tail however, with tens of thousands, or likely more, OpenWRT-based devices that will never see updates.

The company behind several of the findings, Hacker House, say they used multiple AI tools and both open and frontier models to discover the bugs, then manual review and testing to confirm before reporting.

Cisco to Stop Assigning (some) CVEs

Cisco has announced it is moving to a twice-monthly patch cycle. Additionally, Cisco will no longer assign CVEs to each bug in the release.

Cisco says this is in reaction to the rapid increase in bugs found by AI tools, and that “Assessing security risk CVE-by-CVE and applying point mitigations is no longer fit for purpose.” Cisco will still assign a CVE to a bug that “requires compensating controls” or is known to be exploited.

It seems like it will be business as usual for the most severe bugs, but it will be interesting to see what happens when exploits are found for bugs which did not get a CVE in a prior release.

Microsoft Suggests Three-Day Patch Race

Microsoft is now recommending a 3 day patch window for new patches. This is, to say the least, an “aggressive” schedule for applying new patches, given the recent track record of issues introduced by patches.

A update that crashes is inconvenient on a home computer — even more so if it’s your parent’s computer — but could be catastrophic when it brings down an entire corporate fleet. Most large organizations have their own internal patch schedules and internal testing requirements before patches are deployed, delaying the patch process further.

Systems like the CISA KEV database, a list of vulnerabilities known to be actively exploited, are in place to help identify the most important issues. With repeated record-breaking numbers of vulnerabilities pushed in Patch Tuesday and the decreasing support of the KEV and related vulnerability indexing systems, the load on IT departments and administrators is becoming impossible.

The AI “bugpocalypse” is finding record numbers of vulnerabilities, while also closing the timeline between bug to exploit to hours in some cases. The time between bug and exploit then drives the patch cycle, which means less testing. With less testing, the patches are less stable, leading to less trust in rapidly installing them.

Oracle Patches Almost 1500 CVEs

Moving directly from the impacts of the flood of Microsoft security issues, the July 2026 patch set from Oracle included fixes for 1499 security issues and 1434 CVEs over 334 Oracle products.

Oracle is also pressuring administrators to move to a monthly patch cycle, but acknowledges “transitioning to a monthly security patching cadence may require updates to existing operational processes”. Not to worry – there is a Oracle consulting service you can hire to help you patch your Oracle problems.

Linux Patches 323 CVEs in Two Days

After announcing over 400 CVEs in the Linux kernel last week, Linux mainters have announced an additional 323 in the last three days alone. The vulnerabilities cover Bluetooth, Ethernet, USB gadget mode, WiFi drivers, virtualization systems, SMB file sharing, and of course more.

One benefit to the architecture of the Linux kernel is that typically all the fixes will arrive in a single kernel update, but when almost every week brings critical updates and a new kernel, patch fatigue is a real thing. Either users stop applying every update, or the uptime and testing requirements of a company prohibits constantly updating and changing servers.

Duress Passwords Aren’t Necessarily a Good Thing

The security-hardened Android variant Graphene, is in the news this week for the use of duress passwords.

A duress password is a mechanism where a second PIN or password can be used to initiate a device wipe. The hope is that in a situation where you are compelled to unlock a device, providing a password which then wipes the device is a preferable option.

The legality of a duress password depends on the environment it is used in. The United States Customs and Border Patrol claims that by wiping a device during an inspection at the US border, Samuel Tunick destroyed evidence and property in violation of federal laws. The issue is compounded because the stop happened at a border, where many US laws against search, seizure, and the availability of a lawyer are suspended, even for US citizens.

The inclusion of duress passwords, and their cousin hidden encrypted volumes, can sound like a good idea, but can place users in serious danger when in situations with less strict rules of law. Having the ability to wipe data sounds great in isolation, but protestors, human rights workers, and other targeted groups have faced direct and physical threats under some regimes. Facing retaliation for wiping a device, or facing physical violence before providing an unlock code out of fear that it might wipe a device, can be a real risk for some.

FreeBSD Fixes a WireGuard Bug

FreeBSD has issued a security update pertaining to the WireGuard VPN implementation.

WireGuard is a modern VPN with excellent performance even on low-end systems, and which has implementations on basically every platform. The FreeBSD implementation, however, didn’t fully validate VPN packets, accepting packets without checking one of the cryptographic validation measures.

Attackers are able to inject data to a FreeBSD WireGuard implementation if they are able to guess some attributes of the connection, and able to fully modify the data in the VPN if they are able to intercept the packets.

The bug impacts any FreeBSD user of WireGuard. FreeBSD has released updated kernels.

hackaday.com/2026/07/31/this-w…

Thanks to things like spilled drinks, leaky batteries, and the general ravages of time, older consoles can be tough to keep going. In particular, excessive damage to a motherboard can make repair difficult or near-impossible to execute even if the rest of the console is in otherwise passable condition. Now, there may be greater hope for those looking to resurrect the worst-treated PlayStations out there, thanks to a new open-source motherboard from [xyzz].

The board is up on GitHub with a CC0-1.0 license. It’s aiming to be a drop-in replacement for the stock motherboard, on to which you would transplant all the necessary proprietary hardware from an existing donor board. The list of transplant parts is quite extensive as you’d expect, including the CPU, GPU, RAM, VRAM, BIOS, sound chips, mechanical control chip, crystals, and all the ports, among others. Some generic parts can still be had off the shelf, but you could also transplant them if so desired. There are some headaches with the current design—namely, it doesn’t fit perfectly with the lid switch connector, and some of the footprints are hard to solder. Still, it’s a great start, and early testing shows that it’s already quite functional.

Fortunately the original PlayStation didn’t have any sort of battery on board to leak all over the place and corrode things, but boards still get damaged in a variety of other un-fun ways. Thus, it’s great to have a potential replacement motherboard on offer to bring badly-damaged machines back to life.

We’ve featured other great restoration projects in this vein before, too, like the efforts to recreate the C64 motherboard from a few years ago. As ever, enthusiasts are doing great work to keep these classic machines alive long into the future.

Thanks to [ToolThatIsTom] for the tip.

hackaday.com/2026/07/31/buildi…

Introduction

Once the attacker has breached the corporate network, subsequent stages of the attack often involve leveraging standard domain infrastructure protocols: using Kerberos, running DNS queries, accessing internal services, opening network shares, and other common networking actions. Because this activity is virtually indistinguishable from legitimate network traffic, it is extremely difficult to detect it with traditional network attack detection tools.
Kerberoasting and DNS tunneling have long ceased to be exotic techniques. They are becoming standard methods in modern attacks because they allow attackers to execute critical compromise stages while remaining undetected by traditional security tools. A clear example of this trend is seen in latest campaigns, employing both Kerberoasting and DNS tunneling.

Traditional network security tools perform well when the attack features a distinct and identifiable signature: a characteristic query string, a known malicious traffic pattern, or the source code of an already discovered exploit. While this approach to threat detection remains effective, it cannot always be applied to discovering network attacks that blend seamlessly with legitimate traffic inside a corporate network.

Instead of searching for explicit indicators of attack, Network Anomaly Detection (NAD) analyzes all traffic for suspicious artifacts that deviate from the host’s typical network activity. Within Kaspersky’s solution portfolio, this technology is implemented specifically in the Kaspersky Anti Targeted Attack (KATA) platform.

The system analyzes network traffic data (DNS, DCE/RPC, Kerberos and other packets) and extracts key parameters used to identify anomalous behavior. This approach enables searching for attacks on domain controllers, signs of traffic tunneling and exfiltration, C2 communications, and other scenarios that may point to compromise of network infrastructure.

However, Network Anomaly Detection is not built on a single, universal set of indicators. Each attack scenario employs tailored detection models that account for the specifics of the corresponding network protocol, typical host behavior, and characteristic deviations from that baseline. This article examines two practical examples – detecting Kerberoasting and DNS tunneling – to demonstrate how these principles are implemented in KATA’s NAD rules and why this approach proves more effective than traditional signature-based analysis.

Kerberoasting attack detection by KATA

Why standard tools have a hard time detecting Kerberoasting

The Kerberoasting attack leverages the standard operational logic of the Kerberos protocol. The attacker identifies service accounts configured with a Service Principal Name (SPN), requests a Ticket-Granting Service (TGS) ticket for them, and attempts to crack the password offline using a dictionary attack against the retrieved ticket. If the password is weak or hasn’t been changed in a long time, the adversary can bruteforce it to get it in cleartext. Subsequently, these compromised credentials can be leveraged for both vertical and horizontal movement across the network.

The essence of a Kerberoasting attack is that an adversary possessing a compromised low-privileged account and a valid Ticket-Granting Ticket (TGT) for that account can request TGS tickets with weakened encryption for service accounts with SPNs. Crucially, it doesn’t matter whether the compromised account actually holds access permissions for those services. Having obtained these tickets, the attacker can then take them offline and bruteforce the service account’s password by trying to decrypt the corresponding ticket locally, without generating any network activity. As the encryption key is based on the password hash, the adversary can guess the password upon finding the correct key.

The attacker’s objective is to find a service account that has a simple password. Most likely, this will be an account created manually by the administrators of the infrastructure or a service. This is precisely why attackers are not interested in system service accounts with SPNs (such as CIFS/fileserver.company.local); these are generated automatically and feature highly complex passwords that are impossible to bruteforce.

We should note that the TGS ticket requests made by attackers are identical to standard, legitimate requests. Every domain naturally exhibits a high volume of Kerberos traffic. Therein lies the primary challenge of detecting Kerberoasting: legitimate service ticket requests (TGS-REQ) are indistinguishable from those issued by attackers. Consequently, the primary detection method relies on correlating indirect indicators rather than signature matching. Key indicators include an anomalous request source (atypical host or user account), a surge in requested SPNs within a short time window, attempts to obtain service tickets for sensitive or privileged service accounts, and off-hour timing or unusual request volume when benchmarked against the historical profile of both the user and the host.

Most of these indicators can be detected using NAD technology, which helps analysts cut through high volumes of Kerberos traffic to establish a concrete hypothesis: who initiated the Kerberoasting attack, which service accounts are at risk, and why this activity deviates from the baseline.

In the context of this attack, the network anomaly stems from a single host – likely using a single user account (cname) – receiving TGS tickets ("msg_type": "KRB_TGS_REP") for numerous unique services with SPNs (sname) within a short timeframe. These service accounts are non-system accounts.

Example of a TGS-REQ – TGS-REP event pair from network session attributes

To detect this anomaly, the NAD rule titled “Signs of a Kerberoasting attack” implements the following logic:

1. From Kerberos network sessions during the search depth period, select only those with a successful Kerberos TGS-REP response, subject to the following conditions:
   
   • The IP address that initiated the session must not be excluded in the excl_sip variable.
   • The requesting client name (cname) must not be included in the excluded users list (excl_users variable).
   • The SPN (sname) must not be excluded within the rule. System SPNs are omitted from detection logic because they exist across most corporate environments and hold no interest for adversaries in this attack vector; including them in the total count of unique SPNs could lead to predefined threshold being exceeded, triggering false positives.

   
   

2. Extract the cname (the name of the client requesting the TGS-REQ) and sname (SPN itself) from these qualifying sessions.
3. Group the sessions by the source IP address and client account name (cname), while aggregating sessions with unique SPNs.
4. Generate an alert if a single IP address using a single client account receives TGS-REP responses for N unique SPN names within the specified search depth window, where N equals or exceeds the threshold variable count_spns.
5. Within the event regeneration window, group under the initial alert all subsequent alerts associated with the same client IP address. This avoids creating duplicate event records by incrementing the aggregation counter (Total appearances).

We should note that this type of logic cannot be implemented using IDS signatures. Consider creating a Suricata rule designed to detect Kerberos TGS-REP packets. To minimize false positives, we’ll exclude system SPNs (which carry highly complex passwords) and apply a threshold for the number of responses a single client can receive. However, such a rule cannot evaluate the uniqueness of the requested SPNs; it can only track packet counts. As a result, this signature would produce a high volume of false positives because any domain naturally generates large amounts of identical legitimate TGS-REP messages.

Furthermore, adding exclusions and tuning thresholds to fit your specific infrastructure environments is significantly more practical when managed through user variables in the interface rather than directly modifying the underlying structure of the IDS rule itself.

Creating a Network Anomaly Detection rule

Network Anomaly Detection (NAD) rules are written as SQL queries executed against KATA’s ClickHouse database. Below, we demonstrate how to add and deploy a rule.

To begin working with NAD rules, navigate to the “Custom rules” section of the interface and select “Intrusion detection”. Under the “Network Anomaly Detection” tab, you can create a new rule.

The Network Anomaly Detection page UI

When adding a new rule, an analyst can select an appropriate rule template from the prebuilt set supplied with product updates. They can also manually modify the rule added from the template (converting it to a custom rule while keeping the original template intact) or author a rule from scratch using the provided guide.

Upon selecting a template, the analyst can review the rule description and either adjust or leave the default values for the following settings:

• Search depth (the lookback window over which the SQL query will run)
• Schedule (the execution frequency for running the query against the specified search depth)
• Event regeneration period (the timeframe during which identical alerts will be aggregated into a single record rather than displayed as distinct events)

UI for creating a new NAD rule

To ensure the rule functions correctly, we recommend navigating to the “SQL-specific query” tab before deployment to review the variables used within the rule – a description for each variable is available by hovering over the question mark icon.

The variables are lists of IP addresses, dates, strings or numeric values that define the network infrastructure – such as domain controllers, DNS servers, time ranges, critical segments, and other entities. This allows you to tailor each rule to different network environments and incorporate specific infrastructure characteristics without modifying the underlying logic.

In our example, using variables allows you to adjust the “Signs of a Kerberoasting attack” rule as follows without altering the underlying SQL query:

• Exclude the source IP address of the TGS-REQ requests from the scope of verification (you can specify a single address, a subnet mask, or a dictionary containing addresses and subnets) as well as the requesting client account (accepts a single value or a dictionary with multiple values).
• Adjust the threshold value required to trigger an alert based on the number of unique SPNs in the TGS-REQ messages.

Query contents and variables used in the new rule

On this same page, you can test if the rule is functional prior to saving it.

Rule execution test results

When this rule triggers, an NDR:NAD alert is generated. In the alert card, the analyst can review basic information: IP addresses, ports, and participating network endpoints.

Alert card for the NAD rule

From there, the analyst can navigate to the associated event, which provides a detailed breakdown of the anomaly alongside links to the affected hosts.

NAD rule triggering event

If needed, the analyst can view and export the network sessions associated with the alert. These sessions can be accessed directly from the alert or within the event card via the “Show related” drop-down list.

Network sessions that triggered the rule

Within an individual session, the analyst can inspect standard details including interacting parties, data volume sent and received, and other fields and metrics. On the “Attributes” tab, the analyst can review the specific events recorded within that session.

Network session attributes

Detecting DNS tunneling in KATA

How DNS tunnels work

DNS tunneling is a technique used to transmit data or control malware through firewalls by encoding information within DNS protocol requests and responses. Instead of performing standard name resolution, an infected host transmits data encoded within subdomain strings and receives response data via DNS records. This covert channel can be leveraged for C2 communication, bypassing network restrictions, or data exfiltration.

One method of implementing DNS tunneling involves utilizing TXT records. In this scenario, the client issues DNS TXT record queries for domain names where the right-hand portion of the domain name (the higher-level domains) remains static, while the left-hand portion (the lowest-level subdomain) carries encoded or encrypted data sent from the client to the server. Under this structure, a sample domain name might look like ZFcABQAIBA[.]testlab[.]local, where testlab[.]local serves as the static right-hand portion and ZFcABQAIBA represents the variable left-hand string containing the data transmitted by the client.

In response to these queries, the server delivers commands or messages inside the data field of the TXT response. Because the right-hand portion of the domain name remains static, all client queries are consistently routed to the same C2 server, even if the intermediate DNS resolvers targeted by the client change.

DNS query (left) and corresponding response (right) during DNS tunneling via TXT records

It is rather challenging to identify this malicious activity within DNS traffic without generating false positives. DNS traffic is permitted across almost all corporate networks, long domain names occur routinely in both internal and external environments, and TXT records are frequently leveraged for legitimate operational purposes.

Suspicion is established through a combination of indicators: a high volume of long, seemingly random subdomains associated with a single top-level domain, high request frequency, an unusually large number of unique names, non-standard record types, and significant data transfer volumes within a single DNS session.

By analyzing DNS traffic for threat detection, we identified three primary fields of interest:

• Requested DNS name
• DNS record type
• TXT data field within the response

As shown in the image above, all of these fields are present in the DNS response. In a real-world scenario, a tunnel of this nature will transmit a volume of data that is abnormally large compared to standard DNS traffic.

Data exchange within a DNS tunnel

Thus, in the context of DNS tunneling, a network anomaly occurs when 1) a single query source host sends data embedded in the variable left-hand portion of domain names (rrname) while 2) maintaining a static right-hand portion (rrname) and 3) receives DNS server responses containing TXT records (rtype) with varying data (rdata), while 4) the total volume of data transmitted in the left-hand portion of the requested domain name together with the TXT data response (rdata + rrname) exceeds a predefined threshold.

Request and response events from DNS session attributes

When detecting DNS tunneling, the following nuances must be considered:

• A single tunnel will not be constrained to a single DNS session; data may be transmitted across multiple sessions with the DNS server, or each individual request may occur within a separate session.
• A client DNS query can contain more than one requested domain name.
• A DNS response can contain multiple TXT records, as well as a large volume of various non-TXT record types.
• Traffic between DNS servers must be excluded, as it duplicates client requests and can trigger false positives.
• Although the factors outlined above (an abnormally large or frequently changing left-hand subdomain alongside a static right-hand domain, or an unusually long string in a TXT record) serve as key indicators of DNS tunneling, they can also occur within legitimate network traffic.

These challenges create a high likelihood of false positives when detecting DNS tunneling, particularly when using IDS-based tools. Writing an accurate IDS rule for this type of activity is practically impossible. With rare exceptions, certain DNS tunneling tools possess static markers that can be leveraged for signature-based detection. However, in the absence of such markers, signature methods fail to deliver high detection accuracy without generating an overwhelming number of false positives. In these cases, a comprehensive approach combining multiple correlated indicators is essential to improve overall detection quality.

DNS tunneling detection logic

To add a rule for detecting this anomaly, you can use the prebuilt “DNS data tunneling via TXT records” template in the new rule creation interface. The “SQL-specific query” tab will display the list of variables used:

• user_DNS_servers: a list of internal DNS server addresses within the infrastructure, required for the rule to function correctly and minimize potential false positives
• excl_sip: IP addresses to be excluded from the scope of the rule (you can specify a single address, a subnet mask, or a list containing both addresses and subnets)
• traffic_size: the threshold value for the total volume of data (in bytes) transmitted through the tunnel

Variables used in the “DNS data tunneling via TXT records” rule

The detection logic for this network anomaly is structured as follows:

1. From network sessions using the DNS protocol within the timeframe defined by the rule’s search depth, select only those sessions containing at least one TXT response.
   Additionally:
   
   • The IP address that initiated the session must not be excluded in the excl_sip variable.
   • The source IP address that initiated the session must not belong to the internal DNS servers listed in the user_DNS_servers variable.
   • The DNS names requested by the client must not be excluded within the rule.

   
   

2. Split qualifying DNS sessions into individual log lines, each corresponding to an individual request or response. Retain only DNS responses containing TXT data.
3. Extract DNS names and their associated TXT data from these DNS responses. Retain only unique values.
4. Group all resulting records by the session’s source IP address, aggregating all unique DNS names and TXT data blocks.
5. Generate an alert if the combined size (in bytes) of the unique DNS names and TXT response data for a single IP address within the search depth window exceeds the specified threshold (the traffic_size parameter).
6. Within the event regeneration window, group under the initial alert all subsequent alerts associated with the same client IP address. This avoids creating duplicate event records by incrementing the aggregation counter (Total appearances).

“DNS data tunneling via TXT records” rule triggering event

The primary value of NAD technology in this scenario lies in noise reduction – by minimizing false positives – and faster investigation times. A DNS tunnel rarely presents itself as a single, blatantly malicious request. Instead, it leaves behind a behavioral footprint: repetition, length, domain structure, unusual record types, numerous subdomains branching off an unchanging root domain, and anomalous host behavior. KATA consolidates these indicators into a single alert, presenting the analyst with an actionable attack hypothesis rather than a set of fragmented DNS events.

Prebuilt rules for detecting network anomalies in KATA

KATA users should note that Network Anomaly Detection (NAD) rules are not enabled by default. Rules must be added manually using the procedure described in the preceding sections. This design ensures that analysts can fine-tune rules to fit specific network infrastructures using variables.

Analysts have three ways of creating new rules:

1. Adding a rule from a prebuilt template and adjusting custom variables. In this case, the rule is classified as a system rule.
2. Adding a rule from a prebuilt template and modifying its underlying SQL query (which requires enabling the “Unlock all template values” option) to create a custom rule based on the template. When modified this way, the rule transitions from a system rule to a custom rule.
3. Authoring a custom rule from scratch, which requires a basic understanding of ClickHouse SQL queries and familiarity with the product documentation.

As of this publication, the product ships with 59 prebuilt NAD rule templates (with additional templates delivered via product updates). KATA supports running up to 200 active rules simultaneously.

Prebuilt rules are divided into six categories:

• Large Data Transfers: tracking abnormally large network sessions across various protocols during regular hours, at night, or over weekends.
• Suspicious Connections: detecting suspicious connections that may indicate hazardous activity, shadow IT, evasion of attack detection mechanisms, and other threats.
• Domain Attacks: detecting classic attacks targeting domain network infrastructures using offensive tooling.
• Reconnaissance Activity: identifying suspicious activity within domain protocol sessions (Kerberos, DCE/RPC, LDAP, DNS) resembling domain reconnaissance.
• Connections to Suspicious Resources: detects actions that violate security policies, potential data exfiltration beyond the perimeter, and unauthorized internet access originating from secured network segments.
• C2 Communication: identifies network sessions characteristic of a potential C2 communication channel or tunnel.

The table below lists the rule templates for detecting network anomalies in KATA:

Conclusion

The examples of Kerberoasting and DNS tunneling clearly demonstrate why modern security defenses cannot rely solely on looking for known signatures and indicators of compromise. Both attack techniques abuse protocols that operate inside corporate networks every day. At the individual event level, they may look like legitimate activity, yet in behavioral context, they stand out as clear indicators of compromise.

NAD directly addresses this gap. Instead of relying purely on signature matches across Kerberos or DNS traffic, it highlights deviations from established baselines: who initiated the activity, how frequently it recurred, which services or domains were targeted, and why that matters for a specific infrastructure.

As a result, analysts gain a clear, actionable starting point for investigation. This capability is especially valuable for spotting the signs of APT group activity, which runs stealthily and is designed to blend in with legitimate operations. The importance of this capability will only grow: as attack techniques evolve, detecting suspicious activity at its earliest stages – before it escalates into critical service compromise or a data breach – becomes increasingly vital.

securelist.com/tr/network-anom…

The stack effect — also known as the chimney effect — is the basic principle that hot air not only rises, but if guided through a tube, the rising hot air will create more pressure that will effectively draw air more effectively into the tube. This is not only great for a chimney, but also a useful principle if you seek to cool something like a PC in a more passive manner. The main question is of course how much of a ‘chimney’ you need to see real effects for something like a typical water-cooled CPU’s radiator, as demonstrated by [der8auer] in a recent video.

Although there’s a lot of fun physics behind the stack effect that you can run the numbers on, the more practical demonstration here using 3D printed funnel segments for the radiator and various thermometers provides a very hands-on feeling for what you can expect from this approach.

With just a single segment stacked there is already a clearly noticeable temperature change, with the second segment creating a draft as visualized by the smoke machine. After this he goes for broke with the full stack and a resulting 19°C temperature drop on the CPU. While impressive, at this point the required funnel gets a bit silly, though the same principle has been applied to computer cases before, including the passively-cooled Power Mac G4 Cube and the 90-degrees-rotated SilverStone Raven series of cases, like the RV02.

The basic idea of making use of the fact that hot air rises, and maybe also banking on the stack effect for some free passive cooling, clearly isn’t so crazy.

youtube.com/embed/ZUG73BzZIJ4?…

hackaday.com/2026/07/31/using-…