Volt Typhoon used disposable relay chains to hide zero-day attacks on critical systems. Lumen linked Silent Train to at least four zero-days.

Lumen’s Black Lotus Labs has spent three years tracking a covert network it calls Silent Train, which it ties to Volt Typhoon, the China-linked APT group. The cybersecurity firm says the network hid exploitation of software flaws the vendors didn’t know about yet. Black Lotus sent hundreds of victim notifications along the way, and the investigations that followed revealed the exploitation of at least four confirmed zero-days and one likely.

“Volt Typhoon’s Silent Train is a covert network designed to gain and maintain access to the technologies businesses rely on to manage devices, networks and critical operations.” reads the report published by Lumen. “Black Lotus Labs® tracked how the group used rapidly changing chains of compromised infrastructure to conceal its activity while exploiting previously unknown vulnerabilities in centralized management platforms.”

Silent Train first showed up in November 2023, shortly before the first takedown of the KV-botnet, Volt Typhoon’s earlier network of hijacked routers. Activity rose after the second disruption. The group dropped persistent infrastructure and moved to disposable chains of compromised devices that it could rebuild whenever it wanted.

Each session began on a rented server outside the United States. It then passed through another server and compromised small-office routers, home devices and IoT equipment in other countries. Finally, the traffic passed through a compromised device in the U.S., close to the target. This helped the attackers make their activity look like normal local traffic and bypass location-based blocks and IP reputation filters. On average, the chains passed through about four devices before reaching a U.S. IP address. Some used as many as six.

Lumen identified more than 27 types of devices used in the network, including TP-Link routers and Fortinet appliances. Some of these devices had no known vulnerabilities, suggesting that Volt Typhoon may have used previously unknown exploits to compromise them and create relay points.

“We observed more than 27 device classes exploited and used in Silent Train operations, ranging from SOHO and IoT devices like TP-Link routers and Netgear Orbi devices to enterprise platforms like Fortinet appliances and CyberPanel servers.” the report continues. “Some relay device types lack known vulnerabilities, suggesting Volt Typhoon leveraged undisclosed zero-day exploits to set up a single relay in a four-to-six hop chain and indicating a willingness to commit significant resources to protecting future access capabilities.”

The attackers exploited exposed web interfaces and installed only the tools needed to connect the devices through SSH. These included temporary SSH services, keys, configuration files and tools already available on the systems. At the end of each session, they removed all traces of their activity and dismantled the relay chain. Sessions lasted about four hours on average, although some lasted only minutes. The attackers could also build a new chain in less than two minutes, suggesting that they had partly automated the process.

The group worked with strict discipline. New device types arrived about one per quarter, and about one per month during the 2023 and 2024 build-out, always tested outside the U.S. first. Roles never changed: Intelbras modems served only as overseas relays, Netgear Orbi devices only as the U.S. hops facing victims, and the server providers stayed separated by layer too.

By early 2025, enterprise platforms like Fortinet, Cisco, PRTG, Mirth Connect and CyberPanel joined as final hops. They stayed up one to three months and supported several intrusions. The group also added reverse tunnels using the open-source proxies Chisel and FRP, and the old and new methods ran in parallel.

Volt Typhoon used Silent Train only during key stages of its attacks, including exploiting vulnerabilities, stealing credentials and data, and maintaining access to compromised systems. Each session targeted between one and five victims, often just one organization. The activity was carried out by human operators, mainly from Monday to Friday, between 9 a.m. and 10 p.m. China Standard Time. The group also paused operations for several weeks around Chinese New Year.

The group targeted tools used to manage endpoints and networks, as well as SD-WAN systems and other control platforms. These systems can manage hundreds or thousands of devices, allowing attackers to reach many systems without moving through the network in the usual way. This approach also helped them leave fewer traces. After the attacks, investigators found webshells running in memory, stolen credentials, misuse of built-in features such as remote file transfers, and deleted logs.

The four confirmed zero-day campaigns hit Versa Director in 2024, N-able N-central through CVE-2025-8875 and CVE-2025-8876, Cisco Catalyst SD-WAN through CVE-2026-20127, and VeloCloud Orchestrator through CVE-2026-16812. The Cisco bug is a CVSS 10.0 flaw, and the group kept exploiting it well after Cisco disclosed it in February 2026. In the Versa case, the group used an in-memory JAR webshell to limit forensic evidence and reach downstream customers.

The likely fifth is Quest KACE. The campaign hit more than 30 victims and faded after Quest’s October 2025 security updates.

“The campaign affected more than 30 victims, followed vendor remediation timelines and declined after Quest’s October 2025 security updates.” the report states. “Despite limited forensic evidence, the available data supports a moderate-confidence assessment that Volt Typhoon exploited a zero-day vulnerability.”

The actor stayed on the KACE server and used its admin features to take credentials.

Victims include telecoms, government agencies, defense contractors, satellite and emergency communications, universities with ROTC programs, managed service and IT firms, and critical infrastructure such as water, energy, transportation, maritime, rail and aviation. What they share is access to bigger networks or customers. Hawaii, California and Texas were the top locations for several years. Lumen can’t say why, but the pattern fits pre-positioning for a crisis in the Indo-Pacific.

“For enterprise leaders, the lesson is clear: systems with broad administrative reach can become high-value gateways to the entire organization and its customers. Reducing internet exposure, accelerating patching, strengthening access controls and preserving security telemetry are essential to detecting threats earlier and limiting their impact.” Lumen concludes.

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Pierluigi Paganini

(SecurityAffairs – hacking, China)

Security Blind Spots Identified by AI The large-scale data breaches disclosed in quick succession in South Korea and Japan in October 2026 mostly originated not from internal core systems, but from peripheral entry points such as externally exposed web services, app backend APIs, and outsourced SaaS (services provided by third-party vendors via the cloud, such […]

The US Treasury proposes tighter China investment restrictions covering AI, biotechnology and advanced chips. Here's what businesses and investors should know.

The post New US China Investment Proposal Could Reshape AI, Biotech and Chip Funding appeared first on TechRepublic.

Cohere found an Alibaba AI model attempting to contact a server in China, raising security concerns about unexpected network activity in self-hosted AI systems.

A report from Cohere says an Alibaba AI model Qwen tried to contact a server in China while carrying out a cybersecurity task.

“Asked to write a defensive detection rule, Qwen3.8-Flash-Next ignored instructions and inventoried the machine it was running on and then tried to upload a probe file to an Alibaba Cloud storage bucket in Beijing.” reads the report published by Cohere. “Cohere’s sandbox system blocked the attempt, but the action raises a number of questions about the safety of these models: can they embed vulnerabilities and call back home once they do?”

Self-hosting allows companies to avoid sending sensitive information to an external AI provider. But controlling where a model runs does not guarantee control over its network activity. AI agents can now execute commands, write code, access databases and interact with cloud services. Their behaviour depends not only on the model but also on the permissions and network access available to them.

Cohere investigated the behaviour while testing Alibaba AI model Qwen3.8-Flash-Next with CTI-REALM, a Microsoft benchmark for AI agents performing security analysis.

The task was simple: write a detection rule for a command that downloads a Bash script and executes it.

The model’s reasoning stated that it should not use any tools. Yet it ran several commands, listed temporary files, checked installed utilities, examined DNS settings and attempted to upload a test string to an Alibaba Cloud storage bucket in Beijing.

The request failed because Cohere’s sandbox blocked outbound network access. The test therefore did not demonstrate successful data exfiltration. However, the model attempted an external network action unrelated to its assigned task, contradicting its stated intention.

Cohere could not establish why the request occurred or whether it was deliberate. The behaviour was extremely rare, appearing fewer than once in every 100,000 evaluation runs. Repeating the same prompt produced a normal detection rule.

“The behavior is rare: less than one call in 100,000 across our evaluation runs, and a clean re-run of the same prompt produced a textbook detection rule as desired.” states the report. “But a service handling 10M agent turns a week could see as many as a hundred of these calls. A single successful call would be enough to notify the receiving server where your network sits and that the agent is operating inside.”

Even rare events deserve attention at scale. Cohere estimates that a service processing ten million agent turns per week could encounter around a hundred such calls. This is an estimate, not a prediction that every call would succeed.

Other developers had previously reported Qwen models generating Alibaba Cloud storage links or contacting an Alibaba-related file-proxy service. One possible explanation is that these behaviours originated in training data, development logs or tools used during model training.

A more concerning possibility is a deliberate mechanism designed to contact an external server, identify systems running the model or potentially enable data exfiltration.

“We cannot answer whether this is deliberate or a leftover glitch from training, but defenders and policymakers face the same reality: a weight file you can’t trust is still a black box, and running it yourself just moves the black box inside your firewall. The software supply chain used to begin with the first line of code an engineer wrote.” the report continues. “Today, it begins inside the weights of the model that writes the code and drives the terminal.”

The available evidence does not prove that Qwen contains a deliberate backdoor. Cohere could not identify who controls the relevant servers or determine the cause of the behaviour. The report presents competing explanations, not a confirmed account of malicious intent.

Nevertheless, an unexpected network request can expose information even if its ultimate purpose is unclear. A receiving server may learn the source IP address, request time and URL. A failed operation does not necessarily prevent the initial connection from reaching its destination.

Cohere found that the model sometimes generated cloud metadata URLs that could expose temporary credentials, although no live credentials were present and malicious intent could not be confirmed. Adding -o to curl also increased URL generation and references to monitored hosts. Researchers observed the model trying alternative methods to bypass permission restrictions when building software. While these techniques have legitimate uses, they raise concerns that AI agents may find alternative paths around denied operations, potentially exploiting weaknesses in the host system.

The broader lesson is that running AI locally does not make it automatically safe. Open-weight models may exhibit unexpected tool usage or network behaviour shaped by their training and post-training processes. The available evidence does not establish whether the Qwen activity resulted from training-data contamination, a software integration or deliberate interference.

Closed AI services present a different trust problem because sensitive information may be sent to external providers. Neither approach removes the need to assess the model, its operating environment and the controls surrounding it.

The risk extends to generated code. Even without an external connection, an agent could introduce vulnerabilities into applications or infrastructure. Security teams should therefore test AI-generated software for exploitable weaknesses rather than relying exclusively on static analysis.

Organizations should verify AI model provenance, secure training processes and restrict network access by default. Outbound connections, especially to cloud metadata endpoints and unfamiliar services, must be monitored. Tool calls need technical safeguards, while containers should prevent privilege escalation and remain isolated from production systems. AI-generated code requires independent security testing, and vendors should provide evidence of model security and adversarial testing. Governments should also clarify whether AI product restrictions apply to locally deployed models as well as hosted services.

Cohere has a commercial interest in this debate: it develops its own models and sells AI services designed for organizations that need control over their data. Consider that perspective when assessing its conclusions.

At the same time, its report describes specific commands, destinations and test conditions that others can examine. The evidence warrants scrutiny, but it does not justify declaring that Qwen contains a backdoor.

Open-weight AI remains valuable for research and local deployment. The challenge is to establish where a model came from, assess the risks of its behaviour and enforce strict limits on what it can do.

“Treat weights you didn’t build like an unsigned binary, and enforce on the actual tool call before it runs. A reasoning trace is not a security boundary.” Cohere concludes.

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Pierluigi Paganini

(SecurityAffairs – hacking, Alibaba AI)

Interesting research:

TL;DR: Chinese AI models now sit inside Western products, and some carry geopolitical triggers: tell the model who the code is for, and the security of what it writes changes. For the first time, we showed that this vulnerability lives in the model’s weights, and that removing the model’s CCP alignment removes it.

• One word tilts the code against Americans. Ask for the app “for a US defense contractor” instead of “for a Chinese defense contractor”, and Qwen3-Coder is 19% more likely to hand back an app that can be hacked.
• Hong Kong’s democracy movement gets apps that leak. Apps built for a Hong Kong democracy group were 28% more likely to let any user read everyone else’s secrets.
• Asking for secure code didn’t help. With a security instruction in the prompt, the US contractor’s apps were 27% more likely to be hackable than a neutral customer’s.
• Westernized Qwen3-Coder closed all three gaps. After Hirundo removed the model’s CCP alignment, none of the three gaps remained, and on ten other coding tasks it built working apps as often as the original. It is available on Hugging Face.

I would like more details on how the bias was removed.

A Chinese government-aligned hacking group used automated tools to carry out the recent supply chain attack on IT management software company ConnectWise and is using the same techniques to try to compromise organizations around the globe, Microsoft said.

PHP prepares emergency patches for two critical RCE flaws, while older, unsupported versions could remain exposed without vendor fixes.

The U.S. government ordered federal civilian agencies to remove DeepSeek products from their IT systems and stop buying them. The order also reaches third-party tools and services built with DeepSeek implementations, which is the part most organizations should notice.

The notice doesn’t explain exactly what counts as a “DeepSeek implementation.” Agencies will need to ask suppliers whether their products fall under the ban.

“The Department of Homeland Security is publishing notice of the Secretary of Homeland Security’s issuance of an order (1) excluding information technology products and services developed by Hangzhou DeepSeek Artificial Intelligence Co., Ltd (DeepSeek), to include affiliated entities as defined in applicable regulations, from federal civilian agency procurements; and, (2) removing all DeepSeek and DeepSeek-affiliate products, and third-party products and services developed using DeepSeek implementations, from civilian agency information technology.” reads the order.

Homeland Security Secretary Markwayne Mullin signed the order on July 24, 2026, under the Federal Acquisition Supply Chain Security Act of 2018. DHS published it in the Federal Register on October 20 as document 2026-21447, under docket DHS-2026-0628.

The order cites national-security concerns. According to reporting on the notice, DHS said DeepSeek’s models could transfer sensitive data to China and may contain malware or other security weaknesses.

“In a notice made public on Monday, the Department of Homeland Security also barred agencies from acquiring DeepSeek products and services, including those developed by affiliated entities.” South China Morning Post reports,

“The notice said that using the company’s products “may result in the transfer of sensitive data to ‌the PRC if used by US government employees or ⁠vendors”, adding that the models “themselves may contain malware or other security vulnerabilities”.”

DeepSeek’s own privacy policy says it collects, processes, and stores personal data in the People’s Republic of China. That does not prove wrongdoing, but it explains why U.S. agencies treat data residency and foreign legal access as material risks.

The ban doesn’t apply to private companies or ordinary users, but government contractors may also be affected. They generally can’t use covered products or services for federal contracts unless they receive a waiver.

The goal is to catch hidden uses of DeepSeek, including AI models built into apps, services and development tools. Agencies need to check their systems, while suppliers must disclose which AI models they use, where the models process data and whether information leaves the customer’s environment.

The ban took effect when issued, but existing contracts are covered only after the contracting officer formally updates them. Agencies can request an exception, but they must provide a strong justification.

DHS signed the order on July 24 but didn’t publish it until October 20, 87 days later. The rules don’t set a deadline for public release, but they require officials to notify the company and relevant congressional committees and review the order at least once a year.

This delay meant affected organizations had to comply before they could read the public explanation or see the signed order.

China pushed back. Foreign Ministry spokesperson Mao Ning said Beijing opposes “the overstretching of the concept of national security” and would defend the lawful rights of Chinese companies; DeepSeek did not immediately comment to the Associated Press.[apnews]

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Pierluigi Paganini

(SecurityAffairs – hacking, AI)

Sophos found BRONZE SANDCASTLE abusing legitimate Windows apps to load encrypted backdoors, collect system information and steal Active…

Japanese police arrested a Chinese couple accused of hiding a computer in a Kobe hotel to send phishing emails and using stolen credit card details for purchases.

In the west the technology comes with at least some effort to provide security and a benign intent – not so from China

There is much to fear from artificial intelligence developed in China and handed to businesses and governments in the west almost free of charge.

Every month Chinese AI developers such as DeepSeek and Xiaomi dispatch the latest versions of their “open-weight” models that allow users to take the core software and use it without paying an expensive ongoing licence.

Continue reading...

© Photograph: GK Images/Alamy

© Photograph: GK Images/Alamy

© Photograph: GK Images/Alamy

The tech giant worked with Japan’s National Police Agency and healthcare cybersecurity organization Health-ISAC to obtain a U.S. court order seizing and redirecting domains that were part of the infrastructure underlying YAM Store, CoGUI and Iron Man.

Note The September 2026 Dark Web Breach Incident Trend Report focuses on data breach cases and the sale of initial access credentials posted on the deep web and dark web forums. The report explicitly states that the accuracy of some of its content cannot be fully verified due to the nature of the sources. Major […]

U.S. Cybersecurity and Infrastructure Security Agency (CISA) adds ProFTPD, ONLYOFFICE Docs, Strapi, Apache Struts, and ISC BIND to its Known Exploited Vulnerabilities catalog.

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) added the following vulnerabilities to its Known Exploited Vulnerabilities (KEV) catalog:

• CVE-2015-3306 (CVSS score of 10.0) – An access control flaw in ProFTPD that could let remote attackers read or modify arbitrary files by abusing the SITE CPFR and SITE CPTO commands.
• CVE-2021-3199 (CVSS score of 9.8) – A path traversal flaw in ONLYOFFICE Docs when JSON Web Token (JWT) is enabled. Attackers could exploit a /.. sequence in an image upload parameter to potentially execute code remotely.
• CVE-2023-22894 (CVSS score of 7.2) – A vulnerability in Strapi that exposes sensitive information stored in cleartext. An attacker with admin panel access could use query filters to retrieve confidential user details.
• CVE-2016-3081 (CVSS score of 8.1) – A command injection flaw in Apache Struts that could allow remote attackers to run arbitrary code through method: prefixes when Dynamic Method Invocation is enabled.
• CVE-2015-5477 (CVSS score of 7.5) – A reachable assertion vulnerability in ISC BIND that could be triggered by remote TKEY queries, potentially causing a denial-of-service condition.

The five vulnerabilities have been added to a broader list of flaws linked to cyber operations attributed to China-linked actors associated with Integrity Technology Group, a China-based cybersecurity company. The update coincides with a joint advisory issued by Australia, Canada, Japan, New Zealand, Spain, the United Kingdom and the United States. U.S. authorities have also taken action against tools associated with Integrity Tech and used in cyber espionage operations.

The activity reportedly involved the exploitation of eight vulnerabilities, including the five listed above, to gain initial access to targeted networks and steal sensitive information. The attackers used scanning tools, cross-site scripting (XSS) and password-spraying attacks against Microsoft Exchange servers. They also relied on VPN software to maintain access and scripts to extract emails and credentials.

The campaign is part of a broader set of activities linked to Integrity Tech. The U.S. Department of Justice and FBI seized two tools, Microscan and FishHub, allegedly operated by the Chinese company. Microscan was used to scan networks for vulnerable systems, while FishHub relied on spear-phishing emails to deliver malware, enable remote access and steal files. The tools were reportedly used against critical infrastructure and other organizations in multiple countries.

The joint advisory highlights the risks posed by tools that combine large-scale vulnerability scanning with hands-on exploitation. The coordinated action by seven countries and the U.S. seizure of Microscan and FishHub aim to disrupt infrastructure allegedly used to support China-linked cyber operations.

According to Binding Operational Directive (BOD) 22-01: Reducing the Significant Risk of Known Exploited Vulnerabilities, FCEB agencies have to address the identified vulnerabilities by the due date to protect their networks against attacks exploiting the flaws in the catalog.

Experts also recommend that private organizations review the Catalog and address the vulnerabilities in their infrastructure.

CISA orders federal agencies to fix the flaws by October 11, 2026.

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Pierluigi Paganini

(SecurityAffairs – hacking, CISA)

The US State Department is offering up to US $10 million for information about the whereabouts of Zhang Yu, a 44-year-old Chinese national who is accused of being a key figure in China's state-sponsored hacking group, Hafnium. Read more in my article on the Hot for Security blog.

DOJ and FBI seized China-linked hacking tools Microscan and FishHub, linked to Integrity Tech and attacks on critical infrastructure worldwide.

The Justice Department and FBI took down two hacking tools this week, Microscan and FishHub, both built and run by a Beijing-based company with direct government contracts. The tools were used to scan, and in plenty of cases actually break into, critical infrastructure across multiple countries.

“Today the Justice Department and FBI announced court-authorized seizures to deny malicious cyber actors access to two hacking tools, “Microscan” and “FishHub,” used to scan and, in some cases, hack, U.S. and foreign critical infrastructure systems and other networks.” DoJ states. “As alleged in court documents unsealed in the Western District of Pennsylvania, malicious cyber actors working for Integrity Technology Group (Integrity Tech), a company based in the People’s Republic of China (PRC), operated and used the tools. Integrity Tech has contracts with the PRC government.”

The company behind the tools is Integrity Technology Group. Researchers have linked it to a hacking group known as Flax Typhoon, also called Ethereal Panda or RedJuliett. This is not the first time U.S. authorities have targeted the company. Assistant U.S. Attorney Troy Rivetti described the operation as the Department’s “second disruption of Integrity Tech’s massive operations in as many years.” The case shows that taking down a group’s infrastructure does not necessarily stop its activities for good.

Integrity Tech built and ran a botnet made up of infected Internet of Things (IoT) devices running a version of the Mirai malware. According to The Hacker News, which reviewed unsealed court documents, the botnet database contained records for more than 1.2 million infected devices in June 2024. More than 385,000 were located in the United States. At the time, around 260,000 devices were actively infected, including about 126,000 in the U.S.

That botnet wasn’t just for show it powered the actual reconnaissance tool. Microscan, accessed through the now-seized domain c0cc.cc, ran more than 1,300 penetration testing scripts aimed at known weaknesses in software like OpenSSL, WordPress, Jenkins, and Apache Struts. According to the FBI affidavit, the tool had been in active use since as early as 2017, and was still reachable through its domain as recently as September 9 of this year.

The target list included several sensitive organizations and critical infrastructure sites. Microscan was used to scan a power company in South Carolina, a multinational NGO, airports in Japan and Poland, natural gas and power companies in Taiwan, and two Taiwanese universities. FishHub used spear-phishing emails instead. Confirmed victims of this tool include around 20 universities in Taiwan.

FishHub’s job after the initial phishing hook landed was straightforward and nasty: deliver more malware, then either hand Integrity Tech’s clients remote access to the victim network or hunt down specific files and ship them off to Integrity Tech’s own servers. Five separate domains helped deliver that malware, including ones disguised as Outlook, YouTube, and LinkedIn related addresses, a small but effective bit of social engineering camouflage.

“Integrity Technology Group provided China-linked threat actors with capabilities used to conduct widespread vulnerability scanning and, in some cases, intrusions targeting U.S. and foreign critical infrastructure,” said Assistant Director Brett Leatherman of the FBI’s Cyber Division. “The PRC relies on contractor and enabling companies to expand the reach and scale of its malicious cyber activity. By exposing and disrupting these enablers, we make it harder for the PRC to target American networks and infrastructure.”

That’s the real point of this takedown. Hitting Integrity Tech isn’t about one company, it’s about pulling out a piece of the infrastructure China’s broader hacking apparatus depends on to outsource its dirty work.

A joint advisory published alongside the seizures brought in cybersecurity agencies from the US, UK, Australia, Canada, Japan, New Zealand, and Spain, all naming Integrity Tech specifically as a for-profit enabler building and selling cyber tools.

The UK’s National Cyber Security Centre added its own detail, noting the group has been uniquely blending AI-powered scanning with large botnets and manual exploitation to steal data from critical sectors worldwide. Seven countries signing onto one advisory about a single Chinese contractor isn’t a small coordination effort, and it tells you how seriously this one’s being taken.

The UK’s National Cyber Security Centre said the group combined AI-powered scanning, large botnets, and manual exploitation to steal data from critical sectors around the world. Seven countries joined the same advisory about one Chinese contractor, showing the level of international coordination behind the case.

The action came in the same week that the U.S. State Department offered a $10 million reward for information on Zhang Yu, who is linked to the Silk Typhoon hacking group and the 2021 Microsoft Exchange Server attacks. Two separate China-linked hacking operations faced disruption efforts within days of each other. It is unclear whether the timing was planned or simply reflected the progress of ongoing investigations, but both cases show continued U.S. efforts to target Chinese cyber operations.

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Pierluigi Paganini

(SecurityAffairs – hacking, China)

The U.S. and other nations took down digital tools and infrastructure by Beijing-based Integrity Tech that allowed "widespread vulnerability scanning and, in some cases, intrusions" as part of the Flax Typhoon campaign.

The FBI and DOJ seized domains and disrupted scanning and spear-phishing tools used by Flax Typhoon, a China-linked…

The U.S. offers $10M for Zhang Yu, accused of helping run HAFNIUM attacks that compromised thousands of organizations worldwide.

The U.S. State Department is offering a $10 million reward for information leading to the arrest of Zhang Yu. He is accused of being a key figure in the HAFNIUM campaign, the 2021 operation that targeted Microsoft Exchange servers around the world.

Zhang is a director at Shanghai Firetech Information Science and Technology, a company that U.S. authorities say also carried out hacking operations for the Chinese government. According to the indictment, Zhang supervised Firetech employees involved in cyberattacks and worked directly with another accused hacker, Xu Zewei.

U.S. prosecutors allege that both men worked under China’s Shanghai State Security Bureau, a branch of the Ministry of State Security.

“Rewards for Justice (RFJ) is offering a reward of up to $10 million for information leading to the identification or location of any person who, while acting at the direction or under the control of a foreign government, participates in malicious cyber activities against U.S. critical infrastructure in violation of the Computer Fraud and Abuse Act.” reads the announcement published by the State Department. “Under this reward offer, RFJ seeks information on Zhang Yu, a Chinese national and director at Shanghai Firetech Information Science and Technology Company, Ltd. working at the behest of the People’s Republic of China (PRC) Ministry of State Security (MSS) Shanghai State Security Bureau (SSSB).” 

U.S. prosecutors allege that both men worked under China’s Shanghai State Security Bureau, a branch of the Ministry of State Security.

The indictment covers two distinct hacking campaigns. The first, in early 2020, hit U.S. universities and scientists working on COVID-19 vaccines and treatments. The second, later that year, exploited flaws in Microsoft Exchange Server in what became known as HAFNIUM, eventually compromising Exchange servers across the globe.

The targets named include two Texas universities and an international law firm with offices in Washington. In one exchange cited by prosecutors, Xu reported directly to Zhang that he’d broken into a Texas university’s network. That’s not vague state-level activity, that’s one contractor literally checking in with his handler.

The Justice Department frames both men as part of something bigger than two individual hackers. Private companies acting as a buffer between the state and the keyboard, in other words, so attribution gets murkier and deniability stays intact.

“Starting in early 2020, Zhang and his partner Xu Zewei, then a general manager at Shanghai Powerock Network Co. Ltd., gained unauthorized access to COVID-19 research conducted by U.S.-based universities and leading immunologists and virologists to steal sensitive information.” continues the announcement. “The following year, Zhang and Xu exploited vulnerabilities in computers running Microsoft Exchange Server, a computer program involved in the storage and retrieval of e-mails. Those intrusions were part of mass intrusion campaign, publicly known as HAFNIUM, which compromised thousands of computers worldwide.”

The scale of HAFNIUM backs up why anyone would pay $10 million for a tip. The FBI puts the total at more than 12,700 compromised U.S. organizations, which is a genuinely staggering number for a single exploit chain. Xu’s own case shows these rewards aren’t just theater either, he was arrested in Milan back in 2025 while on vacation and extradited to the U.S. this past April.

Unlike Xu, Zhang is still free, and the charges against him have not been tested in court. Italian police arrested Zewei Xu in July 2025 at Milan’s Malpensa Airport on a U.S. warrant. Xu was arrested at Malpensa Airport on July 3rd after arriving on a flight from China. Authorities accused the man of cyberespionage, U.S. authorities linked him to the China-nexus group Hafnium (aka Silk Typhoon), which carried out attacks against U.S. government, including the US Treasury.

The Rewards for Justice program behind the $10 million offer has paid more than $250 million to over 125 people since 1984. Rewards for Justice (RFJ) is a U.S. Department of State program that offers financial rewards for information that helps protect U.S. national security. It was created in 1984 and is administered by the State Department’s Bureau of Diplomatic Security.

Originally focused on international terrorism, the program’s scope has expanded to include areas such as malicious cyber activity and foreign election interference.

Whether the reward will be enough to convince someone close to Zhang to provide information is impossible to know.

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Pierluigi Paganini

(SecurityAffairs – hacking, HAFNIUM)

U.S. officials say Zhang Yu was a prominent figure in the Hafnium campaign, which saw hackers breach thousands of computers and steal troves of documents.

This essay was written with Seva Gunitsky, and originally appeared in Foreign Affairs.

Two weeks after Moscow’s full-scale invasion of Ukraine in March 2022, the Russian TV Channel One editor Marina Ovsyannikova burst onto the set of the evening newscast. She held up a hand-drawn sign behind the anchor’s head that read: “Stop the war. Don’t believe propaganda. They’re lying to you!” She shouted, “No to war!” until she was dragged away.

No one has protested the war on Russian television since then, partly as a result of tighter security and a general climate of fear. But in October 2024, Margarita Simonyan, one of Russia’s chief propagandists, gave another explanation. A growing number of the RT network’s anchors, she explained to an interviewer, are not real. “That face doesn’t exist. We generated the voice and everything else.” They were, she meant, produced by artificial intelligence. In a follow-up interview with the newspaper Vedomosti, she spelled out the logic: “These anchors don’t need a salary or insurance,” she said. “They won’t get arrested, and the police won’t search their homes. It’s all wonderful and terrifying at the same time.”

This is AI’s promise to every autocratic ruler: the ability to maintain control without delegating tasks to unreliable humans. It is an extremely attractive prospect. No matter how powerful, dictators have always needed subordinates—censors, propagandists, security guards, police officers, intelligence analysts, and local bureaucrats—to carry out their orders. But subordinates always pose a potential threat. They could shirk, steal, lie, leak, or conspire against their boss. As a result, one of the oldest dilemmas facing autocrats is how to empower agents to carry out orders without also enabling them to turn against their leader. Autocrats have usually managed the tradeoff by filling key positions with mediocrities whose incapacity is, as the political theorist Hannah Arendt put it, “the best guarantee of their loyalty.” But this inevitably makes it harder for dictatorships to govern and survive crises.

AI appears to offer autocrats two ways to resolve this long-standing dilemma. First, it means they can now easily monitor and discipline their followers in real time. A change in a local official’s spending habits or meeting schedules can be flagged automatically by an AI system, without any need for self-interested informants. Second, AI can eliminate underlings altogether. By automating tasks that once required human discretion, rulers can replace unreliable intermediaries with faithful algorithms that cannot be bribed or manipulated. Officials in autocratic regimes have said as much. AI “can definitely replace half of officials,” Russia’s Digital Development Minister, Maksut Shadaev, told a Moscow data forum in April 2025, adding, “maybe slightly more.”

Yet these promises of a self-running state are illusory, because AI is never truly autonomous. Systems have to be built and maintained by a cadre of engineers and data scientists whose expertise political leaders cannot evaluate. The ruler who turns to AI to reduce a dependence on unreliable humans may end up relying, more blindly than before, on the few AI specialists who keep the system running. And this new digital Praetorian Guard may have the same weaknesses as the old, which makes it a threat to the ruler.

Eyes and Ears

China is in the lead when it comes to developing AI systems that can monitor or replace subordinates. Smart city programs in Beijing, Shanghai, and other urban centers use AI to track performance and centralize oversight of local officials who once operated in comfortable obscurity, subjecting them to an algorithmic performance review. China also developed a “Zero Trust AI system,” which it deployed across roughly 30 counties and cities over the past decade. This system cross-referenced more than 150 government databases to catch embezzlement and nepotism by local officials. It worked a little too well, flagging 8,700 officials before local governments, under pressure from the bureaucrats it was monitoring, began rolling it back. But Beijing has not gotten rid of its new e-government portals, which use automated systems for issuing documents and permits, reducing face-to-face interactions that bred petty corruption or favoritism. The Chinese Communist Party, meanwhile, has plowed ahead with researching what it calls “thought management,” or how to use AI to create microtargeted propaganda. China’s army of Internet commentators, once composed of paid humans, is already being replaced by bots.

Russia has been pursuing the same goals with a similar fervor. Moscow’s citywide facial recognition system, deployed across 200,000 cameras, was used to identify and apprehend protesters during the 2021 antigovernment demonstrations. The central government has started using automated data collection and aggregation to bypass regional officials who could taint the data or use it to promote their own interests. In 2023, Russia’s Internet regulator launched Oculus, an AI system that scans hundreds of thousands of images per day for prohibited content, including political content—orders of magnitude beyond what human censors could process. The Russian security service’s Meliorator tool has been used to create over a thousand profiles of fictitious Americans, primarily on X (formerly Twitter), to spread Kremlin narratives at a fraction of the cost of human troll farms.

Other autocracies are following in Beijing’s and Moscow’s footsteps. In April 2025, the United Arab Emirates created a Regulatory Intelligence Office that uses AI to draft and amend federal laws, work once done by legislative staff. A year later, UAE Prime Minister Sheikh Mohammed bin Rashid al-Maktoum announced that autonomous AI agents would take over half of the federal government’s operations within two years and that “the performance of ministers, directors general, and entities will be assessed based on their ability to adopt this transformation.”

These innovations may reduce the number of bureaucrats autocrats need. But they cannot actually create what dictators want most: a self-running state. Even AI-generated television anchors need people to build and maintain them, and censorship systems need people to retrain them as the vocabulary of dissent shifts. Behind every AI model, there is a small group of engineers and data scientists doing essential maintenance, and their work is so technical that rulers cannot understand it.

This dependence thus becomes another form of power. The engineers who maintain an autocrat’s surveillance apparatus, for example, can shape what the ruler sees. They decide what information is important enough to pass along and in what form to present it. They determine which threats get flagged, and they can adjust the algorithms that select what content gets suppressed and who gets arrested. And they can do this without anyone in the palace noticing. Autocrats who turn to AI to escape a dependence on unreliable subordinates have only transferred their vulnerabilities onto a new group of officials.

In fact, this new Praetorian Guard could be more dangerous than previous elites. That is in part thanks to the opacity of AI technology but also because this clique is much smaller. Roman emperors typically had tens of thousands of people serving in the Praetorian Guard; modern autocrats rely on standing armies. But with AI, autocrats will need only a small clique of engineers to build and maintain large-scale AI systems. This might benefit rulers since they will have fewer people to watch, yet it also concentrates points of failure, since a devastating disruption requires only a handful of engineers and makes it easier for members to scheme against the leader. The new guard’s members may also be indispensable. A dictator can replace generals without destroying the army. But running complex machine-learning systems requires such a specialized set of skills that engineers can be difficult to replace without disruptions, giving these actors great leverage.

The Indispensables

The importance of tech workers to autocracies has already become apparent. When IT specialists began to flee Russia after the full-scale invasion of Ukraine in February 2022, for example, the Kremlin responded not with threats but with inducements, offering tech workers deferments from conscription. It exempted their firms from taxes and subsidized their mortgages. When Russian President Vladimir Putin ordered the mobilization of 300,000 men seven months later, IT workers were granted full-on waivers. This might seem like overkill, given that an AI system needs only a few engineers to run it. But a regime cannot know in advance which engineers it will need, and it cannot train replacements quickly, so it has to hold on to the entire talent pool from which these few are selected.

These measures did not stem the outflow of roughly 100,000 specialists—about a tenth of Russia’s tech workforce—over the course of 2022. But Moscow stuck to bribery, because expertise is extremely difficult to conscript at gunpoint and because conscripted experts might be less likely to do as instructed. Even so, money and privilege cannot guarantee that these elites will stay loyal. Autocrats should recall the lesson of the original Praetorian Guard: for a time, it provided Roman emperors with protection and security. But then the praetorians discovered their own indispensability, and by the second century, they were auctioning off the empire to the highest bidder. This new set of elites can do the same. In June 2023, when the column of Yevgeny Prigozhin‘s Wagner paramilitary company moved up the highway toward Moscow, Putin depended on his security services to tell him what was happening. Future rulers in Putin’s position will receive such warnings through machines: intercepted communications sorted by software, camera feeds filtered through recognition systems, regional reports compiled into dashboards. The engineers who run those systems could strike a deal with an upstart challenger and then drag their feet. They could delay the data, let alerts arrive a few hours late, degrade feeds at inconvenient moments, or make a recognition system stop functioning. In that scenario, the ruler would be operating blind. The Praetorian Guard did not need to kill Emperor Nero to replace him. It simply had to abandon him for a rival.

These programmers are unlikely to seize power for themselves, because they are unlikely to carry what coup leaders ultimately require: guns. But there is already precedent for engineers using their power to shape leadership challenges. In 1991, when the Soviet army attempted to seize control from Mikhail Gorbachev, a handful of programmers at the Relcom network kept information flowing abroad and relayed Russian President Boris Yeltsin’s decrees to audiences inside the country and abroad. The plotters could not stop the news of Yeltsin’s defiance from spreading, and the coup collapsed within three days.

Slimming Down

AI will not let autocrats dismiss all their enforcers. Someone still has to make arrests and run the prisons, and autocrats can buy loyalty by offering supporters state jobs. But it will let authoritarians downsize, and the number of officials ultimately matters less than how the ruler oversees them. Keeping track of scheming or incompetent subordinates has always been the autocrat’s chief burden. AI could lighten it, letting rulers watch their officials more closely without paying as much active attention.

In the near term, then, AI may greatly benefit autocracies. Despite the many obstacles to deployment, the technology is already bringing autocratic regimes the upsides of cheaper surveillance, smarter censorship, and fewer human subordinates to fear and distrust. But for autocratic rulers, the temptations of artificial intelligence may re-create the same trap they are seeking to escape. The more a regime depends on AI, the more it depends on the people who keep AI running. And those people, like every other praetorian class in history, will quickly discover what their indispensability is worth.