The United Kingdom has issued economic sanctions against Cryptomus, a chameleonic cryptocurrency platform that experts say serves as a major conduit for payments related to ransomware, narcotics, darknet markets, fraud and cybercrime services that are helping Russia circumvent existing financial restrictions.

Heleket’s support agent on Telegram claims, despite all evidence to the contrary, that it is not affiliated with Cryptomus. Image: Richard Sanders.

On October 8, the U.K. issued sanctions (PDF) against Cryptomus — previously known as Certa Payments and Xeltox Enterprises — for processing payments that benefit the Russian government and its financial institutions. The sanctions freeze any Cryptomus assets at U.K. banks, and bar British companies from offering it any business, virtual or otherwise. Also targeted in these sanctions were the virtual payment platforms VexPay, TokenSpot, and the Tsunami Payments service.

As explained in our December 2024 investigation, “How Cryptocurrency Turns to Cash in Russian Banks,” Cryptomus has been a key payment processor for dozens of Russian cryptocurrency exchanges and websites hawking cybercrime services. The company has changed its name several times since emerging in 2022, most recently to “Heleket” last year after Canada levied a record CAD $176 million fine against the platform for anti-money laundering violations.

The blockchain data-crunching firm Chainalysis said the U.K. sanctions target the broader infrastructure supporting Russia’s wartime economy.

“Cryptomus and Heleket have emerged as major payment hubs for illicit activity,” Chainalysis wrote in an October 8 blog post. “Together, these services received more illicit funds across tracked categories than the mixers in our dataset and the number of illicit counterparties surged to more than 900 in a single month in late 2025.”

The Cryptomus support bot on Telegram, responding on Oct. 8 to an inquiry by saying the platform was suddenly unable to process transactions.

In April 2026, the blockchain analysis firm TRM Labs found Cryptomus had processed more than $7 billion in transactions since its inception in 2022, including transactions associated with child sexual abuse material vendors, terrorist financing networks, and human trafficking operations.

TRM noted that Cryptomus’s transaction volume cratered after the platform instituted new “know your customer” or KYC rules, and that the cryptocurrency platform responded to the Canadian fines by launching Heleket as a parallel service that continued to offer no-KYC payment processing.

Richard Sanders is a blockchain analyst and investigator who spent months mapping out Cryptomus’s payment infrastructure and transacting with its customers. Sanders said the U.K. sanctions on Cryptomus are important because Canada’s fine against the platform was ultimately unenforceable and uncollectible.

“A fine doesn’t stop a cryptocurrency payment processor from processing payments,” Sanders wrote on LinkedIn. “Sanctions kill the operation. The distinction matters.”

As our 2024 investigation noted, Cryptomus shared a Vancouver address with dozens of other foreign currency dealers and money transfer businesses — none of which appeared to be physically located there. Following the CAD $176 million fine for alleged money laundering violations, Cryptomus appealed the penalty, launched Heleket as a parallel service, and kept on processing large volumes of sketchy transactions, Sanders said.

Sanders said the sanctions also are notable because they are the first known instance of a “Five Eyes” country (referring to the Australia, Canada, New Zealand, the U.K. and the U.S.) sanctioning a cryptocurrency operation incorporated in another Five Eyes jurisdiction.

Where do entities like Cryptomus and Heleket derive their authority to offer international financial services? The answer appears to involve fake financial institutions overseen by a phony regulatory body positioned in a country that is not widely recognized as a sovereign nation.

Our 2024 investigation found the sole shareholder of Cryptomus’s parent company Certa Payments was a 25-year-old Ukrainian woman who also headed several other firms, including the WS Management and Advisory Corporation, which bills itself as the regulatory body that exclusively oversees licenses for cryptocurrency, foreign exchange and online gaming businesses incorporated in the disputed region in northwest Africa known as the Western Sahara.

That entity’s website — crasadr.com — says it assists applicants with bank setup and formation, online gaming licenses, and the creation and licensing of foreign exchange brokers. One of Certa Payments’ former sites — certa[.]website — also shared a server with 12 other domains, including rasd-state[.]ws, a website for the Central Reserve Authority of the Western Sahara.

The website crasadr dot com, the official website of the Central Reserve Authority of Western Sahara, which lists the entities awarded licenses for cryptocurrencies, gaming and foreign exchange services.

Between February 2022 and September 2025, Bellingcat staff and volunteers collected, geolocated, and shared more than 2,500 incidents of civilian harm following Russia’s full-scale invasion of Ukraine. 

As part of this effort, Bellingcat tested a new machine learning model intended to rank Telegram social media posts on their likelihood of containing incidents of civilian harm. 

This novel methodology dramatically reduced the search and selection time required, freeing researchers to focus on verifying incidents of civilian harm – not just searching for them. 

This piece documents our methodology, ethical considerations and lessons learned in the hope that others researching similar topics can benefit from our work. 

Open source research into civilian harm is still a relatively new field and it presents many challenges – one of the biggest is organising and sorting through the huge volume of user generated content being produced to find what is relevant. 

Machine learning, a form of artificial intelligence that uses algorithms to identify patterns from large amounts of data and make predictions, can make this task more efficient.

With ongoing conflicts involving large amounts of civilian harm occurring in Sudan, and much of the Middle East, this guide aims to offer those covering these conflicts an example of how machine learning can be used to help find and sort incidents. You can also access the Code Notebook for our model here.

We defined “civilian harm” not just as civilian deaths or injuries resulting from armed conflict, but also the broader and delayed effects on civilians from mental trauma, loss of livelihood, displacement, destruction of infrastructure and more. This definition was informed by the Protection of Civilians book on civilian harm. 

Initial Telegram Dataset 

Each Telegram post containing civilian harm which had already been manually verified by researchers was used to build an initial dataset of confirmed cases of civilian harm, which data scientists call positive instances. We collected a total of 5,848 unique URLs for these Telegram posts. For our manual collection we reviewed posts on relevant Telegram channels, working through oldest to newest posts each day. Assuming that a given post made it to our geolocated incidents list, it meant the researcher who flagged it also looked at the posts that appeared before and after it on Telegram and did not flag those ones, so we selected the 10 posts surrounding the verified civilian harm post as our additional dataset of posts that did not contain civilian harm. After excluding any deleted or duplicate posts, we ended up with 48,545 non-civilian harm posts, our negative instances. 

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The choice to overrepresent negative instances aims at better reflecting the real world and increasing data available for model training. 

We enriched each URL with metadata from the Telegram API, such as the time of publication, reactions or textual content. As some of these posts had been deleted, we completed the missing data points with previously preserved versions from our Auto Archiver database, only available for the positive instances.

Feature Engineering

Training a machine learning model requires numerical data, as these models compute a prediction score based on mathematical operations.

We built these by converting raw data from our initial dataset, such as keywords signalling potential civilian harm, into numerical scores (or “features”) that the model could interpret, with the aim of increasing the model’s ability to identify patterns. This process, known as feature engineering, can significantly improve model results because it allows data scientists to suggest explicit context knowledge. 

A full list of features we used to train the model can be found in the code notebook accompanying this piece. Many features were directly inspired by researchers’ input from their experiences manually screening cases of civilian harm by sorting through a set number of Telegram channels and inspecting each post individually.

Several of the features used were directly built from the metadata contained in each Telegram post including media_type, day_of_week; or binary ones: forwarded, edited and reply_to. 

Other features included engagement information: views, forwards, total_reactions, and even individual features for most used emojis including the reaction_crying_face to count emoji.

Converting Text to Numbers 

To embed the experience from the manual collection process, researchers put together a list of keywords both in Ukrainian and Russian that, to them, signalled posts likely to  show civilian harm. For instance, “Шахед” and “КАБ” translated to “Shahed” and “Guided aerial bomb” respectively. We created a numerical feature to count their frequency. 

In addition, we included several generic English-language keywords which meaningfully signalled potential civilian harm, such as “injured”, “school affected” and “hospital affected” that were only used for generating semantic similarity scores. 

A semantic similarity score is a calculation used to determine the proximity in meaning between different words and phrases. To get the semantic similarity between the post text and each of our keywords, we represented each in a list of numbers via a Sentence Transformer model, which converts words into numerical representations called vectors that a computer can understand. 

We then calculated the level of similarity between each vector using cosine similarity, one of the most popular methods for measuring similarity between two pieces of text.

Due to how embeddings work, this calculation results in a figure on a scale from -1 (no semantic proximity) to 1 (same meaning). For example, the words “hurt” and “injured” would have a high similarity score, while “residential” and “injured” would have a negative score as the words are not semantically similar. 

Finally, to enable the model to identify the relevance of each post to civilian harm in Ukraine, we used a multilingual text transformer from the BERT family of language models to represent the entire post’s text as a vector of 768 numerical values. This model can efficiently represent text from many languages in a way that captures meaning: the same sentence in different languages will generate similar embeddings, and trained machine learning models can detect patterns in the embeddings. 

It is important to note that for this initial prototype of a civilian harm detection model, we did not include any features derived from media content such as photos and videos, although that would be a logical next step in attempting to improve model performance.

Selecting, Training and Evaluating Models

With 54,393 rows of 893 numerical features each, we selected four machine learning algorithms to train our predictive models. 

We chose Logistic Regression as a baseline algorithm due to its simplicity. We also selected three other “best in class” models, Random Forest, XGBoost, and LightGBM. These choices centred on the interpretability of the models and their ability to work on tabular data of this size. For example, we avoided neural networks due to a lack of interpretability and because those models work best with a larger dataset. 

To genuinely assess the performance of the trained models, we split our dataset into three parts:  

• A training set – the data the models were trained on (60 percent of the full dataset’s rows)
• A validation set – used for an intermediary evaluation when tuning model parameters (20 percent of all rows)
• A test set – hidden for the final performance assessment, so the models were evaluated on unseen data (remaining 20 percent of rows)

We used a stratified split to divide the dataset instead of a random split. This method ensured the proportion of positive instances (i.e. confirmed cases of civilian harm) remained consistent across all three sets at about 11 percent.

To measure the performance of machine learning models, we ran them through the test set and measured the number of correct and incorrect predictions. Models output a likelihood between 0 and 1 that each Telegram post contains civilian harm, and we tried to find a cut-off threshold that leads to a good balance between flagging almost every post (0.1) or flagging very few (0.9). 

There are two main types of evaluation metrics to gauge a model’s prediction power. Recall asserts what fraction of positive instances (i.e. known civilian harm posts) were correctly flagged as such. Precision measures the fraction of posts flagged as civilian harm that are indeed civilian harm posts.

During the training phase, we tuned the models to maximise average precision (PR-AUC), a metric that summarises precision across all recall levels. While this method also accounts for precision, it prioritises recall, which is preferable for this use case as it steers model selection to reduce the number of civilian harm posts that are skipped. 

The following table sorts models from best to worst PR-AUC against a baseline of a coin-flip predictor. ROC-AUC and F1 are two other evaluation metrics included as sanity checks. Simply put, ROC-AUC measures the probability of ranking two instances, one negative and one positive, correctly; F1 balances precision and recall equally and its best cut-off threshold value.

From these results, we selected XGBoost as our final model as it had the best scores when compared across all metrics.

Interpreting the Model

Because these models are interpretable, we can understand which features are the most useful when predicting whether a post includes civilian harm. The above table shows the top 10 features that most strongly signal the XGBoost model to make a decision:

• semantic_keywords_similarity: the semantic proximity between the post text and manually selected keywords “casualties”, “damage” and “civilian harm”
• bert:  the model was able to discern meaning from the text with the same strength as some of the other features in this list – there are three cases of this in the top 10
• reaction_crying_face: reactions with crying face emojis on the post
• group_of_messages: whether a post contains multiple media files
• keywords_in_text: the number of custom Ukrainian or Russian keywords in the post

These results generally tally with what you might expect when selecting Telegram posts for instances of civilian harm, including that posts that generate a lot of emotional engagement and posts using keywords about civilian harm were among those most likely to contain content related to this topic. Not all models had the same top features as XGBoost. In fact, for the Random Forest model the most important feature was the number of crying face emojis present in a post, a soft pattern highlighted by researchers when this methodology was first imagined.

LLM Results and Comparison

Retroactively, we decided to run a sample of the same test dataset through different large language models (LLMs) to gauge their ability to make these same predictions. 

We aimed to include an LLM-generated score as an extra feature for our trained models, which would be captured as relevant if it correlated with the correct predictions. 

To start, we selected two local models, the 1B and 4B variants of Gemma 3 from Google DeepMind, and two cloud-hosted models, Gemini 2.5 flash and Gemini 3.5 flash. With this selection, we hoped to compare results across a wide range of models’ expected performance. 

We generated a 400-row stratified sample (preserving the same proportion of real civilian harm instances) from the test dataset used for the custom models. For each of the four LLM models, we ran two tests: one where only the Telegram post message was sent, and another including both the message and the engineered features (excluding the text embeddings, as the model had direct access to the text). In the prompt for each model, we asked for a score between 0 and 1. We then evaluated the results as we did for the custom models. 

The above table shows that LLMs can indeed extract value from the engineered features. All four LLMs surpassed the baseline Logistic Regression model in our tests, yet none of them performed better than the other custom-trained models, and XGBoost remained the one with the highest PR-AUC. 

Still, Gemini 2.5 Flash performed better than its newer version 3.5 and even achieved a slightly higher best F1 score than any other model. While this is a good result, for the flagging of civilian harm posts, the PR-AUC remains the crucial metric, as it captures the model’s ability to identify infrequent instances of civilian harm while minimising false positives.

Ethical Considerations

Introducing an instrument of automated decision-making into a process of detecting civilian harm brings inherent ethical questions. These include automation bias, or how humans tend to blindly place faith in machine-generated recommendations; algorithmic bias, or how the results of these models echo the same patterns present in the training data, including under- or over-representation of types of civilian harm. 

The decision to test an automated methodology for this particular project came from the fact that there were limited resources for both steps in the process – the detection of potential civilian harm and its actual verification. Historically, we built an enormous backlog of unverified incidents because a lot of time had to be spent on monitoring the most recent events so that potential evidence would be captured and preserved as soon as possible. 

The automation of this process also reduced the exposure of researchers to a significant amount of unpleasant and distressing visual and text content, reducing the burden of exposure to traumatic content. 

For this project, we tried to ameliorate the ethical challenges with a number of strategies including randomly flagging posts not captured by any model, monitoring which features models relied on to make decisions, and by doing historical comparisons of patterns in data. 

Additionally, as stated above, for this initial prototype of a civilian harm detection model we did not include any features derived from the media content itself. In the future, it would be a logical next step in attempting to improve the model performance, to include the media from the posts – but using AI to review actual media comes with additional ethical challenges such as model bias.

Because of the opaque ownership of many LLM companies and their generative nature, the use of LLMs for an extra feature presented additional ethical challenges including privacy and safety concerns considering the sensitive nature of the data. Our model did not rely on LLMs, though we retroactively ran a sample through it. 

How the Model Fits into the Bigger Picture 

After selecting this model, we created a user interface where researchers could view a list of Telegram posts sorted from most to least likely to contain indications of civilian harm. The user interface was designed for quick triage and integration, where a positive confirmation from researchers would instantly send the post to the Auto Archiver (Bellingcat’s tool for preserving digital content) and then transfer it to ATLOS (our internal collaborative verification platform). Bellingcat staff and volunteers could then manually verify incidents. Researcher input was constantly stored so that this data could be used to improve the model in the future. 

Preliminary feedback indicated that the AI model was useful. Not only were we able to reduce time and harm from scouring through dozens of war reporting Telegram channels, researchers also reported that the stream of new posts being added to the verification backlog were capturing real and diverse cases of civilian harm. 

We recognise this model has much room for improvement and is a work in progress. Even though it can illicit diverse civilian harm posts, further tests and improvements (such as improved feature engineering and continuous evaluation) are needed before it can confidently be deployed.

Despite the focus on civilian harm and Telegram (highly popular in Ukraine and Russia), this pipeline is generic and can be adapted to other conflict monitoring tasks. How easily this can be done does depend on how open the social media platform is and whether it is possible to scrape posts from it. Apart from that, it is easy to incorporate new features and data, and cheap to automatically retrain, test and deploy models as the system receives more human input.  

Looking forward, sorting through overwhelming amounts of data in a conflict will continue to be challenging. Hopefully, this methodology can help newsrooms, conflict monitoring organisations, and others find the balance between ethical considerations and resources in order to carry out open source investigations on civilian harm and human rights violations. 

Editor’s note: This article was updated on July 3, 2026, to include a line outlining that the model described is a work in progress.

Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Patreon channel here. Subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

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The post How to Use AI to Help Find Civilian Harm appeared first on bellingcat.

On February 15, 2026, the bulk carrier, Grumant (IMO: 9385879) was pictured at the occupied Ukrainian Port of Feodosia on the Crimean peninsula. Satellite imagery suggests it had already been there for several days.  It appeared to stock up on grain before departing on a two-month-long journey eventually docking at the Port of Benghazi in Libya on April 18.

While there have been previous reports of grain shipments from occupied Ukraine arriving in Libya, this is only the second time a Russian ship has been observed delivering what the Ukrainian government describes as “stolen” grain to the country. The previous case involved the Damas Wave which travelled in January of last year to the port of Misrata which is under the control of the UN-recognised Government of National Unity (GNU). In addition to satellite imagery, Bellingcat deployed a new technique that analysed Grumant’s heading data which was contained in AIS information provided by Lloyd’s List Intelligence, to help confirm Grumant’s presence in Feodosia. 

Bellingcat has been tracking smuggled Ukrainian grain shipments as they find new markets, five of the ships we previously identified have since been sanctioned by the EU while another was sanctioned by the US Department of Treasury.

MapLibre | Protomaps© OpenStreetMap contributors

Bosphorus Strait

Grumant transits the Bosphorus Strait in the middle of the night.

Credit: Yörük Işık.

Black Sea

Grumant enters a region of the Black Sea known for GNSS interference, meaning that Grumant’s publicly reported Automated Identification System (AIS) position is unreliable.

Port of Feodosia

On February 15, a high resolution satellite image confirms the ship is docked at the port of Feodosia at berth No. 1 that is used for bulk and metal cargo. Matching features visible include Grumant’s grey decking, its seven hatches and bright yellow front mast. What appears to be leftover grain can be seen under the two port crates, immediately next to the ship.

Credit: Satellite image ©2026 Vantor.

Black Sea

Grumant exits the area of signal interference, meaning that its reported position on ship tracking services is now reliable again. Its AIS messages indicate it is travelling towards the Bosphorus.

Bosphorus Strait

Grumant transits the Bosphorus Strait towards the Sea of Marmara. Judging by the draft, with no visible red paint on its hull, the ship appears to be fully laden.

Credit: Yörük Işık.

Izmir Anchorage

Grumant arrives in Izmir, Turkey on February 23 and anchors off the coast until March 13.

Over the course of three weeks, Grumant never enters the Port of Izmir. It is not known if it was denied entry. Bellingcat asked the port operators but did not receive a response before publication.

Credit: Planet Labs PBC.

Aliağa

Grumant then loiters off the coast of Aliağa, about 50 km from Izmir. It stays here until March 16, never entering the port. It again is not known if it was denied entry. Bellingcat asked the port operators but did not receive a response before publication.

Near Benghazi

Grumant arrives in Libyan waters and stays off the coast of Benghazi until April 1.

Libyan Waters

Grumant briefly leaves the coast of Benghazi, but returns a few days later.

Benghazi

Grumant leaves the anchorage on April 18 and docks at the port of Benghazi where it unloads the grain. The ship was captured in a Vantor satellite image on April 20.

It leaves port on April 23, and heads back towards the Bosphorus.

Credit: Satellite image ©2026 Vantor.

Bosphorus Strait

After spending a few days off the coast of Tuzla, Grumant transits the Bosphorus towards the Black Sea.

Credit: Yörük Işık.

Lloyd’s List Intelligence has previously reported on the expansion of Russia’s grain smuggling operations, beyond the occupied port of Sevastopol to include Feodosia port. 

According to the Ukrainian activism, journalism and hacker group, Kiborg News, Grumant used deceptive shipping practices to deliver grain to Latakia, Syria in 2024. The report included several of Grumant’s shipping manifests, which showed it had repeatedly exported grain from Occupied Crimea to Syria. 

Heading Data Helps Locate Grumant

It is standard maritime practice that ships broadcast Automatic Identification System (AIS) messages which include a ship’s position, heading, and draught (among other information).

Because of longstanding Global Navigation Satellite System (GNSS) interference in parts of the Black Sea, the position data transmitted by an affected ship’s AIS system is often unreliable.

Between February 7 and February 19, 2026, data from Lloyd’s List Intelligence shows the Grumant transmitted 29 AIS messages, with unreliable positions in the vicinity of Feodosia. We know these positions are unreliable as they are erratic and some of them report the ship as being positioned on land.

However, according to the IMO, the heading data transmitted by a ship’s AIS system must come from an onboard compass. A compass is unaffected by GNSS interference, meaning it is a more reliable source of information in these conditions.

Over the same dates, all 29 AIS messages reported the ship’s heading as 267 degrees or 268 degrees. The Port of Feodosia has a heading of 267.5 degrees. The close agreement between the ship’s heading and port heading strongly suggests that Grumant was moored at the port between February 7 and February 19, 2026.

We conducted an extra check of the heading data by reviewing satellite imagery available of berth 1 at Feodosia Port, which suggests that the same vessel was present on several days between February 6 and February 18. Imagery on Feb. 6 shows the port was empty in the morning and occupied in the afternoon. Grumant exited the area of GNSS interference on February 21, and berth 1 at the port was captured on satellite image on February 22 and appeared empty. The low resolution satellite imagery is only used as an additional check to see if a vessel is at the berth.

Bellingcat checked all vessels transmitting AIS in the vicinity of Feodosia Port and found that Grumant was the only one that consistently transmitted a heading matching the Port of Feodosia over the period of interest.

We shared our research with Charlie Brown, a former US Naval Officer and Senior Advisor at United Against Nuclear Iran where he focuses on maritime sanctions enforcement and the tracking of illicit shipping. Brown told Bellingcat that while satellite imagery of vessels remained key for identification, when looking for reliable data in a spoofing environment it made sense to look at the various elements of AIS data to try and find some accurate information, despite GNSS spoofing.

“It’s quite standard for the independent gyro compass to be providing the heading […] I think the majority would not [be subject to spoofing] so it’s a good methodology to parse out the particular data and then make some inferences from that.”

“It’s neat to think of what can be derived from data that would otherwise be dirty or wrong. So there’s still some elements of use in there.”

He added that in theory there are probably some compasses that are subject to spoofing as well. 

He told Bellingcat that it was fair to say the heading data of the Grumant supported identification, but stressed the need to cross-reference with other data sources. 

While in this instance it has been possible to use AIS data to help verify the location of Grumant, it is relatively unusual to have access to this information. 

Ships that call to the occupied territories frequently disable their AIS transponders to do so.

This activity, known as “dark port calls”, is a common tactic for those engaging in illicit or sanctioned trades. 

Grumant does not transmit AIS messages from February 8 to 11, but this is the longest gap in data (see diagram above), with intermittent messages coming through after that point.

It is unclear why Grumant continued to transmit AIS during the period it was loading in Feodosia. 

A review of Lloyd’s List Intelligence data from January 2025 shows that on a previous voyage to the Black Sea the Grumant operated “dark” for 59 days.  

Visual Identification

On February 15, 2026, high resolution imagery showed Grumant docked in the Port of Feodosia. We compared it with other recent images of Grumant to confirm the match. 

The ship in the satellite image has a grey-coloured deck, which is uncommon enough for it to stand out. Many bulk carriers have cranes (including the ships we previously covered such as Krasnodar, Zafar and Zaid), Grumant does not have any. It also has seven hatches (openings for the grain) and a bright yellow front mast that matches the mast of Grumant (see the image of it transiting the Bosphorus). We can match the Grumant in the Feodosia image, not only to pictures of the Grumant shot from the ground, but also to the satellite image from Benghazi.

The length and breadth of the ship also matches that of the Grumant; 180 metres by 22.90 metres. 

Libya’s Relationship with Russia and Ukraine 

Libya has complicated internal dynamics with essentially two administrations in charge of different parts of the country – the Government of National Unity (GNU) in the west and the Libyan National Army (LNA) in the east.

In recent years, Russia has backed the LNA’s General Khalifa Haftar, based out of Benghazi, in the east of the country. But Jalel Harchaoui, a political scientist specialising in Libya with the Royal United Services Institute (RUSI), stressed that the two sides of this conflict, the LNA and the UN-recognised GNU, are not currently fighting. Instead they are in a flawed, multi-year truce.

Therefore, the east-west divide isn’t as clear-cut as during the civil war. While all shipments going to Benghazi and Tobruk are overseen by the LNA, not all shipments going to the city of Misrata (which is run by the GNU) are meant for the GNU-dominated part of the country. 

Harchaoui told Bellingcat: “the Tripoli government is in some regards pro-Ukraine, but if there’s business that can be done with Russia through the very opaque port of Misrata and all the right people get paid, the business is going to take place.”

That observation is potentially significant given at least one previously tracked vessel that went from occupied Ukraine to Libya docked in Misrata.

This was not the case of the Grumant, however, which arrived in an LNA-controlled part of the country. It is not known from open sources alone if the authorities in Libya or at the port in Benghazi knew the grain carried by Grumant had come from occupied Ukraine.

Bellingcat contacted the Benghazi-based LNA government and representatives of the Tripoli-based GNU government via the Libyan Embassy in The Netherlands. We also contacted the Port of Benghazi, Port of Imzir in Turkey as well as the Ukrainian and Russian authorities. Representatives of the LNA did not respond to requests for comment before publication, nor did the Port of Benghazi or Port of Izmir. The Libyan Embassy in The Netherlands replied to Bellingcat after publication, stating that Benghazi and eastern Libya are not under the authority or administrative control of the Government of National Unity and therefore they are not currently in a position to comment on Bellingcat’s findings.

Ukraine Continues to Pursue the “Shadow Grain Fleet”

“The port of Feodosia, located in the temporarily occupied Autonomous Republic of Crimea, is not under Ukrainian control, and any commercial activity conducted there is illegal,” the Ministry for Development of Communities and Territories of Ukraine and the Ministry of Foreign Affairs of Ukraine told Bellingcat in a joint response. 

They told us the loading of grain exported from the temporarily occupied territories is an illegal act and Russia was using ports as logistics centers to export stolen Ukrainian agricultural products.

“The expansion of such routes to third countries, in particular to North Africa, demonstrates Russia’s ongoing efforts to circumvent international sanctions and monetize resources stolen from the occupied Ukrainian territories.” 

The Ukrainian Ministry of Foreign Affairs sent information about Grumant’s (IMO: 9385879) “illegal activities” to the diplomatic missions in Great Britain, the Republic of Turkey and the Republic of Tunisia over the course of March to May this year, the ministries told Bellingcat. 

Ukraine is continuing to pursue legal action against Russia’s “shadow grain fleet” they told us. For instance, earlier this month a Swedish court approved the transfer of the Russian “shadow grain fleet” vessel CAFFA to Ukraine for investigation after it was arrested in Swedish waters. 

This case has set a new precedent, going beyond sanction and fines previously handed out to such vessels, and allowing for the detention and confiscation of a shadow fleet vessel in European jurisdictions, the ministries said.

According to Russian court documents Grumant’s previous owner Murmansk Shipping Company was dissolved and “Decision/Reshenie” LLC were listed as the International Safety Manager and operator of Grumant. Decision/Reshenie were also listed as the operator of Grumant in another court document, from an unrelated case. 

Bellingcat attempted to contact Decision/Reshenie to ask about Grumant’s grain shipment from Feodisia Port to Benghazi Port, but they had not responded at time of publication.

Youri van der Weide, Galen Reich, Yörük Işık and Bridget Diakun contributed to this report.

Cover image: Planet Lab image shows Grumant anchored off Izmir, Turkey on February 27. Credit: Planet Labs PBC.

Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Patreon channel here. Subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

The post Heading Off: New Technique Helps Track Grain Smuggling Expansion to Libya appeared first on bellingcat.

Authorities in the Netherlands have arrested the co-owners of two related Internet hosting companies for operating IT infrastructure used by Russia to carry out cyberattacks, influence operations and disinformation campaigns inside the European Union. The two men were the focus of a 2025 KrebsOnSecurity story about how their hosting companies had assumed control over the technical infrastructure of Stark Industries Solutions, an Internet service provider sanctioned last year by the EU as a frequent staging ground for cyber mischief from Russia’s intelligence agencies.

An investigator with the Tax Intelligence and Investigation Service (FIOD), the Dutch financial crimes agency, during the raid. Image: FIOD.

The Dutch daily news outlet de Volkskrant reports that the Dutch financial crime agency FIOD on May 18 arrested a 57-year-old from Amsterdam and a 39-year-old from The Hague, charging them with violating sanctions law by directly or indirectly making economic resources available to EU-sanctioned entities.

The Dutch investigation focuses on Stark Industries, a sprawling hosting provider that materialized just two weeks before Russia invaded Ukraine. As detailed in this May 2024 deep-dive, Stark quickly became the source of massive distributed denial-of-service (DDoS) attacks against European targets, and emerged as a top supplier of proxy and anonymity services that showed up time and again in cyberattacks linked to Russia-backed hacking groups.

That report identified two Moldovan brothers — Ivan and Yuri Neculiti and their company PQHosting — who were providing one of Stark’s two main conduits to the larger Internet. In May 2025, the EU sanctioned PQHosting and the Neculiti brothers for aiding Russia’s hybrid warfare efforts. But as KrebsOnSecurity observed in September 2025, those sanctions failed to target Stark’s remaining connection to the Internet — an Internet service provider based in the Netherlands called MIRhosting.

MIRhosting is operated by Andrey Nesterenko, a 39-year-old Russian native who runs the business out of the Netherlands.  News that PQHosting and the Neculiti brothers were about to be sanctioned by the EU leaked in the media nearly two weeks before the sanctions were announced last year. During that time, the Stark network assets were transferred from PQHosting to a new entity called the[.]hosting, under the control of the Dutch entity WorkTitans BV.

And as our September 2025 report showed, WorkTitans was controlled by Nesterenko and a 57-year-old from Amsterdam named Youssef Zinad. On top of that, WorkTitans was getting connectivity to the larger Internet solely through MIRhosting, where Zinad had worked previously.

On May 18, Dutch financial crime investigators arrested Nesterenko and Zinad, and searched three businesses in Enschede and Almere and two data centers in Dronten and Schiphol-Rijk. A statement from the Dutch authorities said they also seized laptops, telephones and more than 800 servers.

A message to the-hosting customers immediately after 800 of its servers were seized by Dutch authorities. The message says that unfortunately data stored on the server has been lost and cannot be recovered.

De Volkskrant said it reviewed data showing WorkTitans and MIRhosting were the most-used networks in pro-Russian attacks on Danish government bodies between November 13 and 19, 2025, the week of Denmark’s municipal elections.

The publication wrote that prior to Nesterenko’s arrest, the MIRhosting founder denied that he knew his servers had been misused by pro-Russian cybercriminals. “He said he had ended all services with the Neculiti brothers when the EU sanctions came into force in May 2025,” and the he “reserved all rights to take action against ‘harmful and incorrect publications,” de Volkskrant wrote.

MIRhosting released a statement saying it has initiated an internal investigation into the alleged facts concerning the elections in Denmark, and that it has temporarily paused services to WorkTitans as a precautionary measure while the matter is being reviewed further.

“Based on our preliminary findings, there are no indications that the services over which we exercise control were actually used to influence the Danish elections,” the statement reads. “No anomalies or spikes were observed in our network traffic during the period mentioned in the publication; had large-scale DDoS attacks occurred, such activity would have been evident. Furthermore, prior to the media publication, we had not received any complaints, abuse reports, or official requests regarding suspicious activities or misuse of our network. Meanwhile, our regular operational activities continue, and our service to our other clients remains fully intact.”

Born in Nizhny Novgorod, Russia, Mr. Nesterenko grew up as a piano prodigy who performed publicly at a young age. In 2004, Nesterenko founded MIRhosting’s parent Innovation IT Solutions Corp., which has the notable distinction of being the company responsible for hosting stopgeorgia[.]ru, a hacktivist website for organizing cyberattacks against Georgia that appeared at the same time Russian forces invaded the former Soviet nation in 2008. That conflict was thought to be the first war ever fought in which a notable cyberattack and an actual military engagement happened simultaneously.

Responding to questions shared via email, Nesterenko said MIRhosting does not support cybercrime, sanctions evasion, or illegal activity, and that the allegations and arrest by Dutch authorities have been extremely harmful to him and his company.

“The transition to the.hosting was not intended to evade sanctions,” Nesterenko wrote. “The hardware and customer portfolio had already been transferred to WorkTitans before the sanctions appeared. Closing or damaging a legitimate Dutch infrastructure company will not stop cybercrime, but it will harm many people who have done nothing wrong.”

Far less is public about the 57-year-old Zinad, who reportedly has been keeping a low profile since our story last year. De Volkskrant reported that Zinad blocked access to his LinkedIn account, had gone months without responding to emails, WhatsApp messages and phone calls, and told a colleague that illness was forcing him to lead a somewhat more reclusive life.

Mr. Zinad’s now-defunct LinkedIn profile. It was full of posts for MIRhosting’s services.

Mr. Nesterenko claims Zinad was never an employee of MIRhosting.

“He helped me and MIRhosting with certain business tasks under a normal business-to-business arrangement between companies,” Nesterenko explained.

However, in previous emails to KrebsOnSecurity, Nesterenko carbon copied Mr. Zinad (who had a @mirhosting.com email), explaining that he was part of the company’s legal team. Also, the Dutch website stagemarkt[.]nl lists Youssef Zinad as an official contact for MIRhosting’s offices in Almere.

Mr. Zinad has never responded to requests for comment. Nor did de Volkskrant have any luck tracking him down. The publication said it repeatedly asked Mr. Zinad (referred to here as simply “Z”), but he reportedly avoided every form of contact.

“‘I am unavailable but will respond to your message as soon as possible,’ reads an automated reply on WhatsApp on 2 October 2025,” de Volkskrant reported. “It is the only response de Volkskrant would receive in months. He did not pick up his phone and did not call back. When an acquaintance asked him via LinkedIn to contact the reporter, he blocked access to his LinkedIn page. At an address in Almere where Z.’s personal limited company is registered, no one was present in April. The corner house’s blinds were drawn, and a pile of rubbish bags lay outside next to a container, as if someone had recently left. A neighbour said he knew the man but did not know where he was staying. Z. was later arrested at a residence in Amsterdam.”

A sprawling academic cheating network turbocharged by Google Ads that has generated nearly $25 million in revenue has curious ties to a Kremlin-connected oligarch whose Russian university builds drones for Russia’s war against Ukraine.

The Nerdify homepage.

The link between essay mills and Russian attack drones might seem improbable, but understanding it begins with a simple question: How does a human-intensive academic cheating service stay relevant in an era when students can simply ask AI to write their term papers? The answer – recasting the business as an AI company – is just the latest chapter in a story of many rebrands that link the operation to Russia’s largest private university.

Search in Google for any terms related to academic cheating services — e.g., “help with exam online” or “term paper online” — and you’re likely to encounter websites with the words “nerd” or “geek” in them, such as thenerdify[.]com and geekly-hub[.]com. With a simple request sent via text message, you can hire their tutors to help with any assignment.

These nerdy and geeky-branded websites frequently cite their “honor code,” which emphasizes they do not condone academic cheating, will not write your term papers for you, and will only offer support and advice for customers. But according to This Isn’t Fine, a Substack blog about contract cheating and essay mills, the Nerdify brand of websites will happily ignore that mantra.

“We tested the quick SMS for a price quote,” wrote This Isn’t Fine author Joseph Thibault. “The honor code references and platitudes apparently stop at the website. Within three minutes, we confirmed that a full three-page, plagiarism- and AI-free MLA formatted Argumentative essay could be ours for the low price of $141.”

A screenshot from Joseph Thibault’s Substack post shows him purchasing a 3-page paper with the Nerdify service.

Google prohibits ads that “enable dishonest behavior.” Yet, a sprawling global essay and homework cheating network run under the Nerdy brands has quietly bought its way to the top of Google searches – booking revenues of almost $25 million through a maze of companies in Cyprus, Malta and Hong Kong, while pitching “tutoring” that delivers finished work that students can turn in.

When one Nerdy-related Google Ads account got shut down, the group behind the company would form a new entity with a front-person (typically a young Ukrainian woman), start a new ads account along with a new website and domain name (usually with “nerdy” in the brand), and resume running Google ads for the same set of keywords.

UK companies belonging to the group that have been shut down by Google Ads since Jan 2025 include:

–Proglobal Solutions LTD (advertised nerdifyit[.]com);
–AW Tech Limited (advertised thenerdify[.]com);
–Geekly Solutions Ltd (advertised geekly-hub[.]com).

Currently active Google Ads accounts for the Nerdify brands include:

-OK Marketing LTD (advertising geekly-hub[.]net⁩), formed in the name of Olha Karpenko, a young Ukrainian woman;
–Two Sigma Solutions LTD (advertising litero[.]ai), formed in the name of Olekszij (Alexey) Pokatilo.

Google’s Ads Transparency page for current Nerdify advertiser OK Marketing LTD.

Mr. Pokatilo has been in the essay-writing business since at least 2009, operating a paper-mill enterprise called Livingston Research alongside Alexander Korsukov, who is listed as an owner. According to a lengthy account from a former employee, Livingston Research mainly farmed its writing tasks out to low-cost workers from Kenya, Philippines, Pakistan, Russia and Ukraine.

Pokatilo moved from Ukraine to the United Kingdom in Sept. 2015 and co-founded a company called Awesome Technologies, which pitched itself as a way for people to outsource tasks by sending a text message to the service’s assistants.

The other co-founder of Awesome Technologies is 36-year-old Filip Perkon, a Swedish man living in London who touts himself as a serial entrepreneur and investor. Years before starting Awesome together, Perkon and Pokatilo co-founded a student group called Russian Business Week while the two were classmates at the London School of Economics. According to the Bulgarian investigative journalist Christo Grozev, Perkon’s birth certificate was issued by the Soviet Embassy in Sweden.

Alexey Pokatilo (left) and Filip Perkon at a Facebook event for startups in San Francisco in mid-2015.

Around the time Perkon and Pokatilo launched Awesome Technologies, Perkon was building a social media propaganda tool called the Russian Diplomatic Online Club, which Perkon said would “turbo-charge” Russian messaging online. The club’s newsletter urged subscribers to install in their Twitter accounts a third-party app called Tweetsquad that would retweet Kremlin messaging on the social media platform.

Perkon was praised by the Russian Embassy in London for his efforts: During the contentious Brexit vote that ultimately led to the United Kingdom leaving the European Union, the Russian embassy in London used this spam tweeting tool to auto-retweet the Russian ambassador’s posts from supporters’ accounts.

Neither Mr. Perkon nor Mr. Pokatilo replied to requests for comment.

A review of corporations tied to Mr. Perkon as indexed by the business research service North Data finds he holds or held director positions in several U.K. subsidiaries of Synergy University, Russia’s largest private education provider. Synergy has more than 35,000 students, and sells T-shirts with patriotic slogans such as “Crimea is Ours,” and “The Russian Empire — Reloaded.”

The president of Synergy University is Vadim Lobov, a Kremlin insider whose headquarters on the outskirts of Moscow reportedly features a wall-sized portrait of Russian President Vladimir Putin in the pop-art style of Andy Warhol. For a number of years, Lobov and Perkon co-produced a cross-cultural event in the U.K. called Russian Film Week.

Synergy President Vadim Lobov and Filip Perkon, speaking at a press conference for Russian Film Week, a cross-cultural event in the U.K. co-produced by both men.

Mr. Lobov was one of 11 individuals reportedly hand-picked by the convicted Russian spy Marina Butina to attend the 2017 National Prayer Breakfast held in Washington D.C. just two weeks after President Trump’s first inauguration.

While Synergy University promotes itself as Russia’s largest private educational institution, hundreds of international students tell a different story. Online reviews from students paint a picture of unkept promises: Prospective students from Nigeria, Kenya, Ghana, and other nations paying thousands in advance fees for promised study visas to Russia, only to have their applications denied with no refunds offered.

“My experience with Synergy University has been nothing short of heartbreaking,” reads one such account. “When I first discovered the school, their representative was extremely responsive and eager to assist. He communicated frequently and made me believe I was in safe hands. However, after paying my hard-earned tuition fees, my visa was denied. It’s been over 9 months since that denial, and despite their promises, I have received no refund whatsoever. My messages are now ignored, and the same representative who once replied instantly no longer responds at all. Synergy University, how can an institution in Europe feel comfortable exploiting the hopes of Africans who trust you with their life savings? This is not just unethical — it’s predatory.”

This pattern repeats across reviews by multilingual students from Pakistan, Nepal, India, and various African nations — all describing the same scheme: Attractive online marketing, promises of easy visa approval, upfront payment requirements, and then silence after visa denials.

Reddit discussions in r/Moscow and r/AskARussian are filled with warnings. “It’s a scam, a diploma mill,” writes one user. “They literally sell exams. There was an investigation on Rossiya-1 television showing students paying to pass tests.”

The Nerdify website’s “About Us” page says the company was co-founded by Pokatilo and an American named Brian Mellor. The latter identity seems to have been fabricated, or at least there is no evidence that a person with this name ever worked at Nerdify.

Rather, it appears that the SMS assistance company co-founded by Messrs. Pokatilo and Perkon (Awesome Technologies) fizzled out shortly after its creation, and that Nerdify soon adopted the process of accepting assignment requests via text message and routing them to freelance writers.

A closer look at an early “About Us” page for Nerdify in The Wayback Machine suggests that Mr. Perkon was the real co-founder of the company: The photo at the top of the page shows four people wearing Nerdify T-shirts seated around a table on a rooftop deck in San Francisco, and the man facing the camera is Perkon.

Filip Perkon, top right, is pictured wearing a Nerdify T-shirt in an archived copy of the company’s About Us page. Image: archive.org.

Where are they now? Pokatilo is currently running a startup called Litero.Ai, which appears to be an AI-based essay writing service. In July 2025, Mr. Pokatilo received pre-seed funding of $800,000 for Litero from an investment program backed by the venture capital firms AltaIR Capital, Yellow Rocks, Smart Partnership Capital, and I2BF Global Ventures.

Meanwhile, Filip Perkon is busy setting up toy rubber duck stores in Miami and in at least three locations in the United Kingdom. These “Duck World” shops market themselves as “the world’s largest duck store.”

This past week, Mr. Lobov was in India with Putin’s entourage on a charm tour with India’s Prime Minister Narendra Modi. Although Synergy is billed as an educational institution, a review of the company’s sprawling corporate footprint (via DNS) shows it also is assisting the Russian government in its war against Ukraine.

Synergy University President Vadim Lobov (right) pictured this week in India next to Natalia Popova, a Russian TV presenter known for her close ties to Putin’s family, particularly Putin’s daughter, who works with Popova at the education and culture-focused Innopraktika Foundation.

The website bpla.synergy[.]bot, for instance, says the company is involved in developing combat drones to aid Russian forces and to evade international sanctions on the supply and re-export of high-tech products.

A screenshot from the website of synergy,bot shows the company is actively engaged in building armed drones for the war in Ukraine.

KrebsOnSecurity would like to thank the anonymous researcher NatInfoSec for their assistance in this investigation.

Update, Dec. 8, 10:06 a.m. ET: Mr. Pokatilo responded to requests for comment after the publication of this story. Pokatilo said he has no relation to Synergy nor to Mr. Lobov, and that his work with Mr. Perkon ended with the dissolution of Awesome Technologies.

“I have had no involvement in any of his projects and business activities mentioned in the article and he has no involvement in Litero.ai,” Pokatilo said of Perkon.

Mr. Pokatilo said his new company Litero “does not provide contract cheating services and is built specifically to improve transparency and academic integrity in the age of universal use of AI by students.”

“I am Ukrainian,” he said in an email. “My close friends, colleagues, and some family members continue to live in Ukraine under the ongoing invasion. Any suggestion that I or my company may be connected in any way to Russia’s war efforts is deeply offensive on a personal level and harmful to the reputation of Litero.ai, a company where many team members are Ukrainian.”

Update, Dec. 11, 12:07 p.m. ET: Mr. Perkon responded to requests for comment after the publication of this story. Perkon said the photo of him in a Nerdify T-shirt (see screenshot above) was taken after a startup event in San Francisco, where he volunteered to act as a photo model to help friends with their project.

“I have no business or other relations to Nerdify or any other ventures in that space,” Mr. Perkon said in an email response. “As for Vadim Lobov, I worked for Venture Capital arm at Synergy until 2013 as well as his business school project in the UK, that didn’t get off the ground, so the company related to this was made dormant. Then Synergy kindly provided sponsorship for my Russian Film Week event that I created and ran until 2022 in the U.K., an event that became the biggest independent Russian film festival outside of Russia. Since the start of the Ukraine war in 2022 I closed the festival down.”

“I have had no business with Vadim Lobov since 2021 (the last film festival) and I don’t keep track of his endeavours,” Perkon continued. “As for Alexey Pokatilo, we are university friends. Our business relationship has ended after the concierge service Awesome Technologies didn’t work out, many years ago.”

Introduction to Infrastructure Pivoting

Pivoting on infrastructure is a handy skill for cyber threat intelligence (CTI) analysts to learn. It can help to reveal the bigger picture when it comes to malware, phishing, or network exploitation campaigns. Infrastructure pivoting essentially is the act of looking for more systems an adversary has created. The main benefit of this pursuit is the identification of additional targets or victims, more tools or malware samples, and ultimately new insights about the adversary’s capabilities.

If done correctly, being able to pivot on adversary infrastructure will be very useful during incident response (IR) engagements. For example, it may lead to being able to attribute the intrusion to a known adversary. This will help others during an IR engagement understand the level of threat posed to the victim organisation.

Receiving Threat Data

To be able to pivot on adversary infrastructure, threat data is needed such as the intelligence shared by threat reports put out by various researchers from public and private sector organisations. This scenario, however, involves relying on the analysis skills of other researchers to explain what the infrastructure is and when they observed it in use.

This blog will examine threat data provided by public sector organisations such as the Computer Emergency Response Team of Ukraine (CERT-UA) as well as cybersecurity vendors such as Deep Instinct, Cyble, and Fortinet. These organisations have shared indicators of compromise (IOCs) uncovered following analysis of adversary intrusion activities or upload to online malware sandboxes, such as VirusTotal, among others.

Introduction to the Ghostwriter Campaign

On 3 June 2024, Fortinet shared a report on malicious XLS macro documents leading to Cobalt Strike Beacons. Analysis of the XLS documents showed that they appeared to be targeting the Ukrainian military and linked to a known Belarusian state-sponsored APT group tracked as Ghostwriter (aka UNC1151, UAC-0057, TA445). On 4 June 2024, Cyble also shared a report on a similar campaign.  

In both reports, if the XLS was opened and the macros were executed by the target, a malicious DLL file was downloaded from an adversary-created domain. In Fortinet’s report, two similar “.shop” domains were mentioned. In Cyble’s report another “.shop” domain was also called out.

Overlapping IOCs

The first pivot on Ghostwriter APT infrastructure that will be demonstrated involves finding indicators of compromise (IOCs) such as domains and IP addresses that appear in multiple threat reports.

The fastest way to realize these overlaps is through continuous collection of reported IOCs into a Threat Intelligence Platform (TIP). This will reveal IOCs that appear in multiple threat reports through tagging and sources of where IOCs come from. Eventually, one domain or IP address will get reported by multiple entities and the connection will make itself apparent.

In Figure 1 (see below) the domain “goudieelectric[.]shop” appeared in both Cyble’s blog and Fortinet’s blog. Analysis of all three domains found that they use the same generic top-level domain (gTLD), registrar, and name servers, as well as have a robots.txt directory configured. These common infrastructure characteristics indicate that all three domains were created by the same adversary.

Figure 1. Three similar domains appearing in two threat reports.

Domain Registration & Hosting Overlaps

When more IOCs are reported in other threat reports it is possible to link them to other known domains, this is due to adversaries reusing the same registrars, name servers, and gTLDs.

In Figure 2 (see below), Deep Instinct reported two more domains that could also be linked to the previous three domains through the mutual use of the PublicDomainsRegistry registrar, Cloudflare name servers, and the robots.txt file.

Figure 2. Five similar domains that appear across three threat reports.

Further, CERT-UA reported three more domains (see Figure 3 below) that could be linked to the infrastructure cluster through this same method as well. This pattern of behaviour is a strong indicator that these domains were created by the same adversary.

Figure 3. Eight similar domains that appear across four threat reports.

Finding Unreported Domains

Since the domains from the above threat reports were collected and linked together through overlapping attributes, it is now possible to use these attributes to find more domains that had gone unreported.

Using a VirusTotal domain attribute query, additional domains can be found by using the following registration pattern:

• Name Servers: CLOUDFLARE
• Registrar: PublicDomainRegistry
• TLD: *.shop

This revealed up to 24 domains that matched this pattern that were likely created by Ghostwriter, a state-sponsored APT group:

• backstagemerch[.]shop
• bryndonovan[.]shop
• chaptercheats[.]shop
• clairedeco[.]shop
• connecticutchildrens[.]shop
• disneyfoodblog[.]shop
• eartheclipse[.]shop
• empoweringparents[.]shop
• foampartyhats[.]shop
• goudieelectric[.]shop
• ikitas[.]shop
• jackbenimblekids[.]shop
• kingarthurbaking[.]shop
• lansdownecentre[.]shop
• lauramcinerney[.]shop
• medicalnewstoday[.]shop
• moonlightmixes[.]shop
• penandthepad[.]shop
• physio-pedia[.]shop
• semanticscholar[.]shop
• simonandschuster[.]shop
• thevegan8[.]shop
• twisterplussize[.]shop
• utahsadventurefamily[.]shop

Note: VirusTotal domain searches are only available to VirusTotal Enterprise users. There are other providers which allow you to search for domain registration patterns such as DomainTools, Validin, and Zetalytics. There also some free OSINT sites such as nslookup.io and viewdns.info that can be useful in certain scenarios.

Finding Related Malware Samples

Using the list of similar domains that were uncovered through the registration pattern search, it is then possible to find additional malware samples communicating with them.

This can be achieved by looking at domains in VirusTotal and checking the Relations tab can show communicating files as shown in Figure 4 below.

Figure 4. Additional malware samples uncovered via the VirusTotal relations tab

Using a VirusTotal graph can help to reveal every communicating file with every domain discovered through the registration pattern search, as shown in Figure 5 below.

Figure 5. All communicating files with every additional domain identified.

URL to the VirusTotal Graph: https://www.virustotal.com/graph/embed/gd2c04407d9ba4b75b2ce73d6155d166d3ef75eaf29894ff5ac287c90400072bc?theme=dark

URL to the VirusTotal Collection: https://www.virustotal.com/gui/collection/2aa6b36a717be8bc49f7925434ca40f3ecb9f628414b491da3e985677508ca08/iocs

Lessons Learned

In conclusion, it is important for CTI analysts to closer inspect the attributes of the IOCs they come across. It is not uncommon for state-sponsored APT groups to make such mistakes when creating their infrastructure to launch attacks from. By exploiting this fact, CTI analysts can learn much more about the adversary’s targets, capabilities, and the behaviours of the humans themselves behind such campaigns.

The importance of this type of work was demonstrated in December 2023 when the US Treasury sanctioned members of the Russian APT group known as Callisto (aka Star Blizzard, BlueCharlie, COLDRIVER, GOSSAMER BEAR). The real world identity of Andrey Korinets was revealed after he was sanctioned for fraudulently creating and registering malicious domain infrastructure for Russian federal security service (FSB) spear phishing campaigns.