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UNC4057 LOSTKEYS Malware Targets Western NGOs

UNC4057 LOSTKEYS

The Russian government-backed outfit COLDRIVER targets Western and non-governmental organisations with its latest spyware, LOSTKEYS.

The Russian government-backed threat organisation COLDRIVER (also known as UNC4057, Star Blizzard, and Callisto) has stolen data from NGOs and Western targets using LOSTKEYS, a new virus. The Google Threat Intelligence Group (GTIG) been tracking COLDRIVER for years, including its SPICA malware in 2024, and believes LOSTKEYS is a new tool.

COLDRIVER focusses on credential phishing targeting well-known targets. People at NGO or personal email addresses are generally targeted. They steal login passwords, emails, and contact lists after gaining access to a target's account. COLDRIVER may also access system files and infect devices with malware.

COLDRIVER has attacked journalists, think institutes, NGOs, and past and current Western government and military advisors. Plus, the gang has kept targeting Ukrainians. COLDRIVER's principal goal is to acquire intelligence for Russia's strategic goals. In several cases, the gang hacked and leaked NGO and UK official data.

January, March, and April 2025 saw the discovery of LOSTKEYS malware. The malicious application may take files from a hard-coded set of folders and extensions and transmit the attacker system details and active processes. COLDRIVER normally utilises credentials to access contacts and emails, although they have utilised SPICA to access target system documents. LOSTKEYS has a unique purpose and is utilised in certain scenarios.

The multi-step LOSTKEYS infection chain begins with a tempting website featuring a fake CAPTCHA. After the CAPTCHA is “verified,” the PowerShell code is transferred to the user's clipboard and the page invites them to execute it using Windows' “run” prompt. The “ClickFix” approach includes socially engineering targets to copy, paste, and run PowerShell scripts. Google Threat Intelligence Group said many APT and financially driven attackers use this method, which has been well documented.

PowerShell does the first stage's second step. In numerous instances, the IP address 165.227.148[.] provided this second step.68. The second step computes the display resolution MD5 hash and stops execution if it matches one of three specified values. This step may avoid virtual machine execution. The request must contain IDs unique to each observed instance of this chain to proceed. In every observation, the third stage comes from the same host as the previous phases.

Base64-encoded blobs decode into additional PowerShell in the third phase. This step requires retrieving and decoding the latest LOSTKEYS payload. It does this by downloading two additional files from the same host using different identities for each infection chain. The first-downloaded Visual Basic Script (VBS) file decodes the second file. Each infection chain is decoded with two keys. One unique key is in the decoder script, while stage 3 saves the second. Keys are used to replace cypher the encoded data.

The final payload is LOSTKEYS VBS. File theft and system data collection are its purposes.

Two more LOSTKEYS samples dated December 2023 were uncovered during this behaviour investigation. These previous PE files posing as Maltego files change greatly from the execution chain starting in 2025. It is unclear if these December 2023 samples are related to COLDRIVER or if the malware was reused from another operation into January 2025. Exchanged Indicators of Compromise (IOCs) include binary hashes and C2 addresses like njala[.]dev and 80.66.88[.]67.

Google Threat Intelligence Group uses threat actor research like COLDRIVER to improve product security and safety to safeguard consumers. Once detected, hazardous websites, domains, and files are added to Safe Browsing to protect users. Government-backed attacker warnings alerted Gmail and Workspace users. Potential targets should enrol in Google's Advanced Protection Program, enable Chrome's Enhanced Safe Browsing, and update all devices.

Google shares its findings with the security community to raise awareness and help targeted companies and people. Sharing methods and approaches improves threat hunting and sector user protections. The original post comprises YARA rules and compromise indicators and is available as a Google Threat Intelligence collection and rule bundle.

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Code Interpreter And GTI For Gemini Malware Analysis

Using Google Threat Intelligence and Code Interpreter to Empower Gemini for Malware Analysis

What is Code Interpreter?

A tool that converts human-readable code into commands that a computer can understand and carry out is called a code interpreter.

What is code obfuscation?

A method called “code obfuscation” makes it more difficult to understand or reverse engineer source code. It is frequently used to hide data in software programs and safeguard intellectual property.

Giving security experts up-to-date tools to help them fend off the newest attacks is one of Google Cloud‘s main goals. Moving toward a more autonomous, adaptive approach to threat intelligence automation is one aspect of that aim.

As part of its most recent developments in malware research, it is giving Gemini new tools to tackle obfuscation strategies and get real-time information on indicators of compromise (IOCs). While Google Threat Intelligence (GTI) function calling allows Gemini to query GTI for more context on URLs, IPs, and domains found within malware samples, the Code Interpreter extension allows Gemini to dynamically create and run code to help obfuscate specific strings or code sections. By improving its capacity to decipher obfuscated parts and obtain contextual information depending on the particulars of each sample, these tools represent a step toward making Gemini a more versatile malware analysis tool.

Building on this, Google previously examined important preprocessing procedures using Gemini 1.5 Pro, which allowed us to analyze large portions of decompiled code in a single pass by utilizing its large 2-million-token input window. To address specific obfuscation strategies, it included automatic binary unpacking using Mandiant Backscatter before the decompilation phase in Gemini 1.5 Flash, which significantly improved scalability. However, as any experienced malware researcher is aware, once the code is made public, the real difficulty frequently starts. Obfuscation techniques are commonly used by malware developers to hide important IOCs and underlying logic. Additionally, malware may download more dangerous code, which makes it difficult to completely comprehend how a particular sample behaves.

Obfuscation techniques and additional payloads pose special issues for large language models (LLMs). Without specific decoding techniques, LLMs frequently “hallucinate” when working with obfuscated strings like URLs, IPs, domains, or file names. Furthermore, LLMs are unable to access URLs that host extra payloads, for instance, which frequently leads to speculative conclusions regarding the behavior of the sample.

Code Interpreter and GTI function calling tools offer focused ways to assist with these difficulties. With the help of Code Interpreter, Gemini may independently write and run bespoke scripts as necessary. It can use its own discretion to decode obfuscated elements in a sample, including strings encoded using XOR-based methods. This feature improves Gemini’s capacity to uncover hidden logic without the need for human participation and reduces interpretation errors.

By obtaining contextualized data from Google Threat Intelligence on dubious external resources like URLs, IP addresses, or domains, GTI function calling broadens Gemini’s scope while offering validated insights free from conjecture. When combined, these tools enable Gemini to better manage externally hosted or obfuscated data, moving it closer to its objective of operating as an independent malware analysis agent.

Here’s a real-world example to show how these improvements expand Gemini’s potential. Here, we are examining a PowerShell script that hosts a second-stage payload via an obfuscated URL. Some of the most sophisticated publicly accessible LLM models, which include code generation and execution in their reasoning process, have already been used to analyze this specific sample. Each model “hallucinated,” producing whole fake URLs rather than correctly displaying the correct one, in spite of these capabilities.Image credit to Google Cloud

Gemini discovered that the script hides the download URL using an RC4-like XOR-based obfuscation method. Gemini recognizes this pattern and uses the Code Interpreter sandbox to automatically create and run a Python deobfuscation script, successfully exposing the external resource.

After obtaining the URL, Gemini queries Google Threat Intelligence for more context using GTI function calling. According to this study, the URL is associated with UNC5687, a threat cluster that is well-known for deploying a remote access tool in phishing attacks that pose as the Ukrainian Security Service.

As demonstrated, the incorporation of these tools has improved Gemini’s capacity to operate as a malware analyst that can modify its methodology to tackle obfuscation and obtain crucial information about IOCs. Gemini is better able to handle complex samples by integrating the Code Interpreter and GTI function calling, which allow it to contextualize external references and comprehend hidden aspects on its own.

Even while these are important developments, there are still a lot of obstacles to overcome, particularly in light of the wide variety of malware and threat situations. Google Cloud is dedicated to making consistent progress, and the next upgrades will further expand Gemini’s capabilities, bringing us one step closer to a threat intelligence automation strategy that is more independent and flexible.

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