We identified a couple of vulnerabilities in AI automation platform Dify resulting in cross-tenant sensitive information disclosure and one-click account takeover. These findings reinforce the pattern we documented in our previous n8n blogpost:even though AI automation platforms are increasingly becoming integration hubs for complex workflows, their security posture still lags behind their rapid evolution and operational importance.
Introduction
Dify is an open-source platform for building LLM-powered applications: agents, chatbots, and automated workflows. With over 134,000 GitHub stars and over 10 million docker pulls, it has rapidly become one of the most popular tools in the AI application space, offering both self-hosted and managed cloud deployments.
Our research into Dify uncovered two distinct vulnerabilities that illustrate this risk:
A file handling flaw that enables one-click account takeover through a single malicious link (detailed below).
An insufficient tenant isolation issue in shared environments that exposes other users’ application source code.
Both findings point to the same structural challenge: platforms that centralize trust must also centralize rigor in how they isolate users and handle untrusted input.
The first issue was addressed in Dify 1.13.1. The second was fixed in the sandbox layer by moving from a shared identity to per-execution UIDs, then shipped to Dify users through the newer sandbox image bundled with 1.13.3.
Dify did not respond to any of our disclosure messages and chose to patch silently.
One Click to Account Takeover
The flaw lies in how Dify handles file uploads through workflow tool nodes, such as Image Downloader or Image Toolbox.
SVG is an XML-based image format that can natively embed JavaScript, via <script> tags or event handlers on SVG elements. When a browser renders an SVG file served from a trusted origin, any embedded script executes with full access to that origin’s session context, including cookies, local storage, and API calls.
Dify uses two subdomains:
upload.dify.ai: where user-uploaded files are stored and served
cloud.dify.ai: the main application domain, where users authenticate and manage their workflows
Critically, upload.dify.aiand cloud.dify.ai are configured as DNS aliases. From the browser’s perspective, both subdomains resolve to the same origin. This collapses the intended security boundary: a file that should have been confined to a static asset domain is instead rendered with the full privileges of the application domain.
A malicious SVG uploaded to upload.dify.aicould simply be accessed via cloud.dify.ai, and the browser would execute its JavaScript payload as if it were part of the application itself.
But this design wouldn’t be dangerous if access control was enforced on uploaded files. Each uploaded file receives a unique ID and is stored at a predictable path:
However, these files are publicly accessible with no authentication and no per-user scoping (a.k.a Insecure Direct Object Reference). Anyone who knows the URL can retrieve the file. And that ID is not necessarily secret: it could leak through Referer headers or surface in shared workspace contexts.
Therefore, in this case, the exploitation scenario was straightforward:
The threat actor generates a malicious link leading to a resource in his account
The resource link is shared to another user, and one click leads to account takeover.
The important detail is what happened during execution. The runner generated a temporary script under ${LIB_PATH}/tmp/<uuid>.py, which became /tmp/<uuid>.py from the Python process’s perspective after chroot. The same runner stamped every wrapper script with a single hard-coded sandbox UID:
Identity was fixed through static.SANDBOX_USER_UID.
The wrapper script was written with os.WriteFile(…, 0755).
The file lived under the shared sandbox tmp directory.
Separate tenants executing inside the same sandbox root, under the same effective identity, with readable code artifacts left in a shared /tmp. That is the entire isolation bug.
Our proof of concept simply sampled /tmp during execution and collected newly created files. In a shared cloud deployment, that exposed wrapper scripts belonging to other tenants running on the same sandbox host.
The attacker-side workflow looked like this:
What the Attacker Actually Stole
The leaked file was not the raw user script.
Dify generated a Python wrapper that loaded a native seccomp helper, decoded a Base64 blob, decrypted it, and exec’d the result.
On the Go side, the matching encryption logic was just as direct:
This looks like “encryption,” but it is really a byte-wise Vigenere cipher with a 64-byte repeating key.
Something like that:
Why the Encryption Broke
If Dify had used a modern authenticated cipher and never exposed the key, reading /tmp/<uuid>.py would still have been bad, but it would not immediately reveal source code. Instead, the runner:
generated a random 64-byte key
XORed every plaintext byte with key[i mod 64]
Base64-encoded the result
embedded the ciphertext in the wrapper script
Repeating-key XOR leaks structure across every byte position modulo the key length. Once the key length is known, recovery collapses into a set of small single-byte XOR problems, not a modern cryptanalytic challenge.
Our PoC used exactly that property. The attack strategy:
Lock onto the real key size of 64 bytes.
Score candidate plaintext bytes for “Python-likeness.”
Slide common cribs, import , from , def main( — across the ciphertext.
Reward outputs that decode as UTF-8, contain Python tokens, and successfully parse with ast.parse.
Workflow code is highly structured plaintext: full of repeated syntax, imports, identifiers, indentation, JSON handling, and predictable scaffolding. Even when the exact business logic is unknown, the shape of Python source gives the attacker enough signal to recover key bytes and reconstruct the rest.
The sandbox did not need to leak the key. The ciphertext was enough.
A reduced version of the recovery logic:
The real PoC is more careful, including crib dragging, UTF-8 heuristics, Python-token scoring, AST validation, and more.
Why This Was Recoverable in Practice
Three properties made the attack reliable.
Fixed key size. The vulnerable runner hard-coded key_len := 64, so the PoC did not have to discover a moving target.
Strong plaintext priors. Python source naturally contains ASCII-heavy text, repeated keywords, common import patterns, indentation and punctuation, and valid UTF-8.
Machine-verifiable output. The PoC did not stop at “looks readable.” It strongly preferred candidates that parsed as real Python, turning recovery into a search problem with a sharp scoring function.
The wrapper was written with os.WriteFile(…, 0600).
The file was reassigned with syscall.Chown(…, uid, …).
The embedded prescript stopped using the single global sandbox UID and used the per-run UID instead.
This matters more than any cryptographic tweak. Before the fix, every execution looked like the same sandbox user. After the fix, each execution got its own identity and its own readable artifact set.
Dify did not “fix the encryption.” It fixed the isolation boundary.
The Impact
One-click account takeover: The attacker acts as the victim: modifying workflows, changing settings, inviting collaborators.
Workflow theft: Private workflows (often encoding proprietary business logic, integration architecture, and prompt engineering) become fully accessible.
Credential exfiltration: API keys, OAuth tokens, and model configurations stored in Dify can be extracted, enabling lateral movement into every connected external service.
Full instance compromise: If the victim is an administrator, the attacker gains control of the entire Dify deployment and every integration it orchestrates.
Conclusion
Both vulnerabilities we found in Dify stem from the same oversight: security controls that weren’t designed to keep pace with the platform’s feature growth. As these tools add collaboration, file sharing, and multi-tenant environments, each new surface needs to be hardened with the same rigor as the core application.
What makes this particularly relevant for security teams is the open-source model: Dify is widely self-hosted, meaning unpatched instances may persist long after fixes are released. Organizations running Dify (in any configuration) should verify they are on v1.13.1 or later.
Timeline
January 14, 2026: initial disclosure sent
March 17, 2026: Dify 1.13.1 released, addressing the first issue
March 19, 2026: dify-sandbox 0.2.13 released with UID-based tenant isolation
March 20, 2026: follow-up sandbox patch stabilizes the UID-based design inside the chroot
March 25, 2026: Dify 1.13.3 released, bundling the fixed sandbox at 0.2.14
TL;DR: Researchers recently disclosed CVE-2026-42945, a critical heap-based buffer overflow vulnerability affecting both NGINX Open Source and NGINX Plus. The flaw exists within the ngx_http_rewrite_module component and can allow unauthenticated attackers to trigger denial-of-service conditions and potentially achieve remote code execution (RCE) using specially crafted HTTP requests.
Imperva Threat Research Group analyzed the vulnerability and associated exploitation techniques. Imperva customers using Cloud WAF or On-Prem WAF are protected against attack attempts targeting this issue.
The Vulnerability
CVE-2026-42945 is a heap-based buffer overflow vulnerability in the ngx_http_rewrite_module component of NGINX Open Source and NGINX Plus. The issue, nicknamed NGINX Rift, occurs when specific rewrite-rule patterns are processed using unnamed Perl-Compatible Regular Expression (PCRE) capture groups such as $1 or $2, combined with replacement strings containing a question mark (?) and followed by additional rewrite, if, or set directives.
Under vulnerable conditions, specially crafted HTTP requests can trigger heap corruption within the NGINX worker process. Public research indicates this can reliably cause worker crashes and denial-of-service conditions, while some researchers also demonstrated potential paths toward remote code execution under favorable memory-layout conditions.
The vulnerability was discovered through autonomous analysis of the NGINX codebase and reportedly remained dormant for nearly two decades. Researchers described the issue as arising from a state mismatch in rewrite processing logic that ultimately results in unsafe memory handling during URI rewriting operations.
In practical terms, an attacker sends a crafted HTTP request designed to reach a vulnerable rewrite rule. During processing, attacker-controlled URI data can overflow allocated heap memory inside the worker process. Depending on the target environment and mitigations such as ASLR, exploitation may result in:
Worker process crashes
Repeated restart loops
Application-layer denial of service
Potential remote code execution within the NGINX worker context
The flaw affects:
NGINX Open Source versions 0.6.27 through 1.30.0
NGINX Plus R32 through R36
Patched releases include:
NGINX Open Source 1.30.1 and 1.31.0+
NGINX Plus R32 P6 and R36 P4
Because rewrite directives are extremely common in real-world NGINX deployments, particularly in reverse proxies, API gateways, load balancers, authentication flows, and URL routing logic, exposure may extend across a substantial portion of internet-facing infrastructure. NGINX was the most widely deployed web server on the internet as of 2025, supporting 32.4% of all websites with known web servers, so the exposure surface is extremely broad across enterprise, cloud, SaaS, and e-commerce environments.
Some of the techniques associated with exploitation include:
Heap corruption via malformed URI rewriting operations
Application-layer denial of service through worker crashes
Potential memory manipulation leading to remote code execution
Automated internet-wide scanning for exposed NGINX deployments
Unlike traditional volumetric DDoS attacks, exploitation of CVE-2026-42945 targets the application processing layer directly, allowing attackers to disrupt services using relatively small numbers of malicious requests.
Bottom Line
CVE-2026-42945 demonstrates how long-lived vulnerabilities in foundational internet infrastructure can remain undiscovered for years while silently exposing a massive attack surface. By abusing rewrite-processing logic inside ngx_http_rewrite_module, attackers can trigger heap corruption using crafted HTTP requests, leading to denial-of-service conditions and potentially remote code execution.
Because NGINX is deeply embedded within modern web infrastructure, including reverse proxies, API gateways, SaaS applications, and cloud environments, organizations should prioritize patching affected systems immediately and review rewrite-rule configurations for vulnerable patterns involving unnamed PCRE captures.
Imperva Cloud WAF and On-Prem WAF customers are protected against related attack activity.
Many developers today are using Claude Code, with a growing portion running it through Amazon Bedrock. For enterprise teams, Bedrock offers major advantages: keeping data inside a VPC, leveraging AWS credits, and integrating with existing IAM controls, monitoring, and security policies. Bedrock adoption also grows significantly among larger organizations and enterprise environments – but this setup can also introduce security risks or unintended configuration mistakes in real-world usage.
If you’re running Claude Code with AWS Bedrock, there’s something you need to know: the AWS credentials you configure for Bedrock don’t stay confined to Bedrock. They might be shared with every shell command, every MCP server, and every subprocess that Claude Code spawns. And depending on how those credentials are scoped, that could mean full access to your entire AWS account.
The Problem in a Nutshell
When you set up Claude Code for Bedrock, you store your AWS credentials in ~/.claude/settings.json:
These environment variables get loaded into the Claude Code process. So far, so normal. The issue is that Unix processes inherit environment variables from their parent. Every time Claude Code runs a shell command, spawns an MCP server, or launches any subprocess, those child processes get your AWS credentials too.
That means any AWS CLI command executed through Claude Code authenticates as your IAM principal. Not just for Bedrock, but for everything that principal has permissions to do.
How This Goes Wrong in Practice
The security boundary here is entirely on the IAM policy side, Claude Code itself applies no restriction. If your IAM user only has `AmazonBedrockLimitedAccess`, the blast radius is minimal. But in practice, credentials often have broader permissions than intended. None of the scenarios below require an attacker or a sophisticated exploit, they’re everyday mistakes that happen when AWS credentials are broader than they need to be.
Reusing your everyday IAM user
You already have an IAM user you use for daily development, like deploying lambdas, reading S3, or managing EC2 instances. Instead of creating a dedicated user for Claude Code, you drop those same credentials into settings.json because it’s faster. Now Claude Code has access to everything you do: production databases, customer data in S3, IAM itself. You meant to give it Bedrock access, but you actually gave it your entire AWS footprint.
Operating on the wrong environment
You’re working on a staging project, but the credentials in settings.json belong to your production account. You ask Claude Code to “delete the old test data from S3” or “terminate the idle instances.” Claude Code generates the right AWS CLI commands for the task, but runs them against production. There’s no visual indicator in Claude Code telling you which AWS account or environment is active. The approval prompt shows aws s3 rm, and you click accept because the command looks correct for what you asked.
Permissions drifting over time
You start with a tightly scoped IAM user for Bedrock only. Months later, someone on your team attaches AmazonS3ReadOnlyAccess for a one-off migration script and forgets to remove it. Then PowerUserAccess gets added during an incident for quick debugging. The Claude Code credentials silently gain more power over time, and nobody audits what it can actually do because “it’s just the Bedrock user.”
Shared credentials across a team
A team lead sets up an IAM user for Claude Code and shares the credentials in a wiki or Slack channel for the team to use. Now multiple developers are running Claude Code with the same identity. There’s no way to distinguish who did what in CloudTrail logs. If one developer’s session is compromised through prompt injection, the blast radius covers everyone using those credentials, and attribution is impossible.
The Attack Scenarios
This isn’t just a theoretical concern. There are several realistic ways this can go wrong:
Accidental over-provisioning is the most likely scenario. A developer uses Claude Code normally, unaware that a “clean up old files” prompt could generate AWS CLI commands touching production S3 buckets or EC2 instances.
Prompt injection is more targeted. An attacker plants malicious instructions in a repository file: a README, a config file, a code comment. When Claude Code reads the file, the injected instruction can influence it to generate AWS CLI commands that exfiltrate data or create backdoor access keys. The user sees an approval prompt but might not catch the malicious intent among legitimate-looking operations.
Compromised MCP servers inherit the full environment as subprocesses. A malicious or supply-chain-compromised MCP server can silently make AWS API calls using your credentials.
What You Should Do
Scope your credentials tightly. The IAM user or role you configure for Claude Code should have the absolute minimum permissions needed, ideally only bedrock:InvokeModel* and related Bedrock actions. Audit what’s attached right now. You might be surprised.
Consider using Bedrock API keys instead of IAM credentials. Claude Code supports AWS_BEARER_TOKEN_BEDROCK, which is inherently scoped to Bedrock operations. API keys can’t be used by the AWS CLI for non-Bedrock operations. This is the most effective mitigation available today and requires no infrastructure changes.
Use temporary credentials. If you must use IAM credentials, prefer STS temporary credentials or SSO-based authentication over long-lived access keys. They at least limit the exposure window.
Pay attention to shell command approval prompts. When Claude Code asks permission to run a command – read it. Look for aws CLI commands that access services beyond what you’d expect. If you see aws s3, aws ec2, aws iam, or similar, think about whether that’s something you intended to allow.
Audit your settings.json. Run aws sts get-caller-identity with the configured credentials and check what policies are attached to that principal. If the answer is anything broader than Bedrock access, tighten it.
The Bigger Picture
This is a classic example of the principle of least privilege being violated through environment inheritance, a well-understood Unix behavior that becomes a security issue when credentials meant for one purpose are implicitly available for all purposes.
Claude Code’s shell command approval prompt provides some protection, but it’s a thin layer. Users lack context about which AWS credentials are active and what permissions they grant. Approval fatigue, the tendency to reflexively accept prompts after seeing enough of them, further erodes this safeguard.
The ideal fix would be credential isolation: Bedrock credentials should be internal to Claude Code and never exposed to shell subprocesses through environment variables. Until that happens, and according to Anthropic, the responsibility falls on you to ensure your credentials are scoped as narrowly as possible.
On May 4th, 2026, The GentlemenRaaS 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.
Figure 1 — Zeta88 advertising The Gentlemen’s RaaS.
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.
Figure 2 — RaaS admin in underground forum.
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.
Figure 3 — Onion page TOX ID.
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.
Figure 4 — Image uploaded by RaaS admin.
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.
Figure 5 — Image uploaded by RaaS admin.
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.
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.
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.
Figure 6 — The Gentlemen RaaS post.
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.
Figure 7 — Account selling The Gentlemen RaaS Data.
In the following days, the same account posted two MediaFire links containing proof files supporting the claimed leak.
Figure 8 — Partial leaks.
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.
Figure 9 — shadow file content.
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.
Figure 10 — Full leaked data screenshot.
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”).
Figure 11 — Image shared in the chats, zeta88 – Admin.
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.
Figure 12 — zeta88 locking message.
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.
Figure 13 — zeta88 – hastalamuerte message.
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.
Figure 14 — Collaboration and profit sharing.
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.
Figure 15 — Organization diagram.
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.
Figure 16 — Searching credentials & accounts.
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.
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.
Figure 17 — Code signing conversations.
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.
Figure 18 — zeta88 “vibe-coded” the Panel.
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.
Figure 19 — AI preferences.
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.
Figure 20 — Chinese LLMs suggestions.
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.”
Figure 21 — Qwen 3.5 post.
zeta88 directs affiliates to use AI as a quick reference—for example, to look up FortiGate internals—rather than asking in the channel.
Figure 22 — Usage of AI as quick reference.
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.”
Figure 23 — Local, self-hosted LLM.
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.
Figure 24 — Screenshot shared in the chats.
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.
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.
Figure 26 — qbit & zeta88 related posts.
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.
Figure 27 — Mention of CVE-2025-33073.
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.
Figure 28 — Transaction link shared.
The specific mentions of how they handle Bitcoin laundering/cash out:
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.
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.
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.
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.
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.
Figure 29 — Agreement to pay 190,000 USD.
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.
Figure 30 — Negotiation playbook.
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.
Figure 31 — Ransom note.
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.
Figure 32 — Forti access to company in Turkey.
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.
Figure 33 — UK company containing information for Turkish company.
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.
Figure 34 — DLS statement discussions.
This served a dual purpose:
Punishing the consultancy (UK), which the actors described as “a very bad company.”
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.
Figure 35 — Initial access proof.
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.
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