Threats Tagged 't1518'

A resurgence in Node.js abuse has been observed since February 2026, targeting government departments, technology companies, and hotels. Attackers leverage the legitimate, signed node.exe binary to execute malicious JavaScript payloads, evading signature-based detection. In one intrusion at an Asian technology company, attackers downloaded the official Node.js installer after repeated payload blocks and used it to run an implant communicating with Ethereum blockchain gateways via EtherHiding techniques. The same threat actors compromised a U.S. fintech firm, deploying the Rust-based C2Looper backdoor linked to ransomware operations. Multiple attacks involved ModeloRAT, associated with initial access broker Woodgnat, connected to ransomware families including Qilin, Interlock, Rhysida, Akira, 8Base, Black Basta, and Embargo. Attackers employ ClickFix techniques for initial access and combine living-off-the-land tools with commodity malware.

MediumMalwareUnited States#node.js abuse#cobalt strike#adaptixc2

Two distinct multi-stage network intrusion campaigns are actively targeting Latin American organizations, with attackers leveraging artificial intelligence tools to enhance their capabilities. The Mexican transportation campaign (CL-CRI-1131) impacted transportation organizations and government entities in Mexico and Ecuador, utilizing living-off-the-land techniques and self-hosted NextChat instances. The Brazilian financial campaign (CL-CRI-1163) employed custom RATs and tunneling tools including a Go-based SOCKS5 proxy. Both operations demonstrate technical overlaps including shared SOCKS5 infrastructure and reliance on commercial large language models like Claude and GPT-4.1 for operational orchestration. Attackers used AI to generate scripts and troubleshoot execution failures, evidenced by iterative file naming patterns and exposed NextChat interfaces. However, fundamental operational security failures, including exposed staging directories and unsecured interfaces, provided defenders clear visibility...

MediumMalwareBrazilEcuadorMexico#phishing campaign#socktz#nextchat

BraZetsu is a sophisticated Python-based Windows malware framework attributed to the Brazilian threat actor Exilware, functioning as a comprehensive toolkit for Initial Access Brokers. Unlike standard infostealers, BraZetsu transforms compromised systems into commercial assets through deep reconnaissance capabilities targeting Iberian and Latin American corporate, financial, industrial, and law enforcement environments. The framework scans for standardized financial remittance files in Brazilian CNAB format, extracts detailed browser histories, and employs AI-enhanced data triage for target prioritization. Operating through a modular architecture with stealth techniques, BraZetsu powers the Infected Marketplace where Exilware commercializes initial access to compromised hosts. The platform allows criminal customers to remotely execute secondary malicious payloads on purchased access, creating a persistent threat-multiplier effect. Tracked since February 2026, BraZetsu demonstrates rapid technical progressi...

MediumMalwareUnited StatesArgentinaBrazil+5#brazetsu#python malware#ai-powered cybercrime

The Gryxa toolkit is a malware toolkit developed with significant assistance from an AI coding agent, enabling a threat actor with limited development skills to create sophisticated persistent attack infrastructure. It operates across hundreds of hosts and uses multiple persistence mechanisms such as scheduled tasks, Windows event subscriptions, and redundant file copies to resist removal. Gryxa also monitors Windows logs and host artifacts after remediation attempts, potentially exposing defender tools and accounts. The actor iteratively improved the toolkit through numerous failed installations, enhancing its resilience. Organizations face challenges in remediation, especially on devices outside centralized management, as Gryxa can rebuild faster than manual response efforts.

JSCeal is a sophisticated cryptocurrency-focused stealer malware delivered as compiled V8 bytecode executed by a bundled Node.js runtime. It uses multiple layers of JavaScript obfuscation and compilation to evade analysis. The malware includes capabilities such as keylogging, browser and credential theft, screenshot capture, and HTTPS traffic interception via a local man-in-the-middle proxy. Check Point Research developed a static deobfuscation pipeline to analyze JSCeal without execution, enabling detailed understanding of its behavior and evolution. The malware targets multiple platforms including macOS and continues to evolve with new payload encryption and targeting techniques.

In this article AI workloads are becoming high-value control points Case study 1: LiteLLM gateway compromise Case study 2: RAGFlow compromise Case study 3: Kestra compromise Mitigation and protection guidance MITRE ATT&CK techniques observed References Learn more AI is creating a new layer of enterprise infrastructure. Gateways, retrieval platforms, orchestration services, and containerized runtimes now sit between users, applications, data, and models. These systems concentrate credentials, data access, model connectivity, and execution privileges, making them some of the most powerful components in the AI stack. That concentration of trust is also creating new opportunities for attackers. In recent investigations, Microsoft observed activity targeting three distinct AI workloads: a LiteLLM gateway, a RAGFlow deployment, and a Kestra workflow environment. The intrusion paths varied, but the objectives were strikingly similar. Attackers sought to steal credentials, establish persistence, and monetize compromised compute resources. The individual techniques matter, but the broader pattern matters more. Across these cases, attackers treated AI infrastructure as a control plane where credential theft, host compromise, and downstream data access can converge. As organizations continue to deploy AI systems, these platforms are becoming high value targets that deserve the same security scrutiny as other critical enterprise infrastructure. AI workloads are becoming high-value control points The campaign-level signal extends beyond one product. The targeted workloads served different functions, but each exposed assets that could support follow-on abuse, including model-provider keys, proxy-issued virtual keys, database connection strings, tenant configuration, workflow execution, or host compute. Post-compromise behavior varied by workload role. Defenders should inventory exposed AI management surfaces, restrict administrative access, and monitor for gateway-originated execution and secret access. Three observed compromises across AI workloads AI workload Observed activity Attacker objective LiteLLM Observed attacker activity : Python droppers, runtime secret harvesting, PostgreSQL collection, miner deployment, and persistence activity from the LiteLLM gateway context. Microsoft assessment: Initial access likely occurred through exploitation of the exposed LiteLLM gateway surface, consistent with the vulnerability chain involving CVE-2026-42271 and CVE-2026-48710. Credential theft, backend database access, durable host access, and compute monetization. RAGFlow Observed attacker activity : Possible SSRF-style reconnaissance followed several days later by code execution, application-path modification, and placement of a Python hook in the TenantLLM credential-configuration flow. Public research: Describes multiple RAGFlow execution paths; Microsoft does not attribute this intrusion to a specific vulnerability. Intercept newly configured LLM provider credentials and model metadata. Kestra Observed attacker activity : Workflow-origin shell execution, Docker and container-environment discovery, XMRig deployment, and follow-on data collection. Microsoft assessment: Initial access likely involved exploitation of the exposed Kestra orchestration surface, with CVE-2026-49869 providing relevant public vulnerability context. Secret discovery, container-level access, data collection, and rapid compute monetization. Case study 1: LiteLLM gateway compromise Framework role and affected runtime context LiteLLM is commonly deployed as a proxy or gateway between applications and model providers. In that position, the service may hold or retrieve model-provider keys, LiteLLM master keys, virtual-key records, database connection strings, routing configuration, and tenant policy data. Command execution in the gateway runtime therefore exposed a process context close to AI routing and credential material. Figure 1. LiteLLM gateway compromise – attack chain…

Grandoreiro, a notorious banking trojan active since 2016 across Latin America, continues operations despite major law enforcement disruption in 2024. Recent campaigns leverage DLL sideloading techniques, abusing the legitimate Duplicate Files Finder application to execute malicious code. The loader incorporates extensive anti-analysis mechanisms including sandbox detection, virtual machine artifact checks, process blacklisting, and environment profiling to evade automated analysis systems. These defensive checks occur before C2 contact, indicating high priority on avoiding detection. Telemetry from June 2026 shows activity concentrated in Latin America, primarily Mexico, with limited presence in Europe and North America. The malware uses custom string obfuscation combining proprietary decryption with Base64 encoding, and communicates with C2 infrastructure over TCP port 6432 using encrypted requests containing host-specific information.

MediumMalwareMexicoSpain#sandbox evasion#grandoreiro#mexico

The Head Mare APT group exploited a chain of vulnerabilities in TrueConf video conferencing servers to deploy PhantomCore and PhantomGraph backdoors. Attackers connected to unpatched TrueConf servers via port 4307/TCP without authorization, using vulnerabilities KLCERT-26-057 and KLCERT-26-058 to execute arbitrary code with NT AUTHORITY\SYSTEM privileges. They replaced legitimate TrueConf client installers with infected versions containing PhantomCore, and deployed a web shell for persistent access. The PhantomGraph backdoor utilized Microsoft OneDrive as command-and-control infrastructure. Affected TrueConf versions included 5.3.X through 5.3.9, 5.4.X through 5.4.9, and 5.5.X through 5.5.5. Multiple Russian organizations across various industries were targeted, including instrument manufacturing, electronics, transportation, energy, IT, and software development. The vulnerabilities were patched in June 2026.

MediumMalwareBelarusRussia#arcbridge#bridgehead#supply chain attack

In February 2026, an active malware delivery campaign named Powercat was observed distributing infostealer malware disguised as utility or cheat software for popular PC games including Roblox, Minecraft, and Grand Theft Auto V. The multi-stage infection chain begins with an initial executable that profiles victims and establishes persistence, followed by a Java-based loader that deploys the final infostealer payload. The malware targets cryptocurrency wallets (Exodus, Atomic, Monero-Gui), browser data from Chromium-based applications, Discord tokens, and gaming accounts with payment information. It includes surveillance capabilities such as keylogging, webcam capture, and screen recording. The campaign particularly targets children who frequent gaming platforms and pay-to-cheat websites, with evidence suggesting collected personal information may be used for blackmail or coercion into illegal activities.

A vulnerability in the web interface of Cisco Secure Firewall Management Center (FMC) Software could allow an unauthenticated, remote attacker to log in to an affected device using a low-privileged account to access sensitive data within the impacted systems. This vulnerability is due to the presence of static user credentials for a low-privileged account. An attacker could exploit this vulnerability by using the account to log in to an affected system. A successful exploit could allow the attacker to log in to the affected system and access sensitive data as the low-privileged user.  Note: If the FMC management interface does not have public internet access, the attack surface that is associated with this vulnerability is reduced.   Cisco has assigned this security advisory a Security Impact Rating (SIR) of High rather than Medium as the score indicates. The reason is that this vulnerability can be used with other Cisco Secure FMC Software vulnerabilities to elevate privileges.