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. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Alibaba AI)
OpenAI's compaction feature, which shrinks a long conversation to save space, could be turned into a way to copy a model's hidden reasoning out in plain text, security firm Xint has shown. OpenAI's reasoning models work through a problem before answering, and the service normally returns only the raw reasoning as an encrypted item the user cannot read. The method got past
The Computer Emergency Response Team of Ukraine (CERT-UA) has issued an alert warning of unauthorized remote access to video surveillance systems manufactured by Dahua. "Cases of compromising IP cameras and DVRs with access to the video stream, changing the device configuration and creating third-party accounts have been recorded," the State Special Communications Service of Ukraine (SSSCIP)
South Korean police identified seven suspects linked to a cybercrime operation that breached more than 90 organizations in the country, with ARTEX AI pentesting framework used in most of the attacks. According to local media citing the Seoul Metropolitan Police, the gang leader has a background in penetration testing and worked at a cybersecurity company between 2019 and 2022. In 38 of the incidents, the threat actor demanded ransom payments of 360 million won ($270,000), promising in exchange not to leak stolen data. Authorities believe the threat actor stole 8.2 million records from compromised organizations, including educational institutes, hospitals, large corporations, and even golf courses. Yonhap News says that the gang leader is a self-taught 32 year-old hacker and started attacking organizations in September 2025.
OpenAI has launched GPT-6.1 Terra, a new model focused on computer use, visual reasoning, coding, and everyday work, while costing significantly less than its larger models. Terra is designed as a faster and cheaper option for tasks such as filling out forms, editing videos, interpreting technical drawings, and automating repetitive desktop work. "We’re introducing GPT-6.1 Terra, a fast, efficient model that combines detailed visual reasoning and precise computer use alongside clearer, more natural writing," OpenAI explained in a blog post. "A significant step up from GPT-5.6 Terra, it excels at the communication and computer-based tasks that fill the workday, from drafting proposals and interpreting technical drawings to filling out forms and editing videos." OpenAI says GPT-6.1 Terra's API pricing is 50% lower than GPT-5.6 Terra and GPT-6.1 Sol, at $1 per million input tokens and $5 per million output tokens.
A critical flaw dubbed 'HTTP2Shell' in Brotli, the C decoder library built into Node.js, could allow attackers to corrupt memory and access sensitive data. The security issue is tracked as CVE-2026-111497 and is also known as “Broccoli2Shell” and “Node2Shell.” It affects Brotli versions 1.0.3 through 1.0.9, 1.1.0, and 1.2.0. The vulnerability has been reported under several names because it was initially identified in Node.js before researchers found that the same flaw in the Brotli decoder could affect software across multiple ecosystems. The name HTTP2Shell reflects its impact on HTTP clients and servers, although the vulnerability is not specific to HTTP/2 and can affect other applications that decompress attacker-controlled Brotli data. Apart from Node.js, potentially affected software includes Python HTTP clients such as urllib3[brotli], requests, httpx, and aiohttp[speedups], when configured with a vulnerable Brotli decoder, as well as other applications and tools that use vulnerable Brotli implementations.
Cybersecurity researchers are calling attention to what they say is a "recurring social engineering pattern" across hundreds of cases in which threat actors use Microsoft Teams' external chat creation feature to conduct impersonation scams or to deliver malware. "A consistent pattern of attacker behavior has emerged across these incidents: the use of the same throwaway tenants, the same 'IT
Microsoft released emergency .NET security updates to patch a critical vulnerability known as Broccoli2Shell, also tracked as Node2Shell and HTTP2Shell. Tracked as CVE-2026-111497, this critical-severity flaw was found by Imperva Red Team and stems from an out-of-bounds write in the Brotli decoder, which expands data compressed using Google's widely used Brotli lossless compression algorithm. Successful exploitation lets unprivileged attackers use a maliciously crafted Brotli-compressed stream to trigger crashes or memory leaks and, in some cases, gain remote code execution on devices with vulnerable Brotli binaries or libraries. According to a Microsoft advisory published Sunday, attackers can also exploit this flaw to remotely escalate privileges on systems with unpatched .NET installations that use non-default configurations.
Dutch police arrest two men over AI-voiced bank fraud that allegedly used a fake app to steal card data from 170 victims, causing €380,000 in losses. Rotterdam police have arrested two men over a new type of bank fraud that used AI-generated phone calls, a fake banking app, and NFC technology to steal bank card data. In just three weeks, the scheme allegedly cost 170 victims around €380,000. “Rotterdam police arrested two suspects accused of a new form of bank-helpdesk fraud. The operation allegedly caused around €380,000 in losses for 170 victims in just three weeks.” NL Times reports. “The suspects allegedly used AI-generated phone calls and a fake banking app to copy victims’ bank cards remotely, allowing accomplices to withdraw thousands of euros. Victims came from across the Netherlands and ranged in age; dozens were in their twenties and thirties.” The suspects allegedly used AI-generated calls to trick victims into believing their bank accounts were at risk. They then persuaded them to install a fake banking app and scan their bank cards with their phones. The app used NFC to capture card data, which accomplices could use to withdraw money. Victims came from across the Netherlands and included people of all ages, with dozens in their twenties and thirties. A 23-year-old man from Vaassen, in Gelderland, is suspected of developing the technology and directing the group. A 25-year-old from Ede, also in Gelderland, is suspected of coordinating the operation. On Tuesday, a Rotterdam court ordered both men to remain in custody for at least another 90 days while the investigation continues. The case started in mid-September, when Dutch banks reported the new fraud through the Electronic Crimes Task Force (ECTF). That’s a partnership of banks, police and prosecutors that shares information and expertise to fight financial and digital crime. Within days of consulting the task force, Rotterdam’s cybercrime team had identified the digital infrastructure used by the group. Investigators monitored the servers to understand how the operation worked. They traced the people behind the technology, the phone calls made to victims, and the cash withdrawals. The scam started with a recorded message warning victims about suspicious activity on their bank accounts. “The callers used artificial intelligence (AI) to produce dozens of recordings featuring different voices, banks, companies, and scenarios. Victims were asked to press 1 if they did not recognize the alleged activity.” states the report. “They were then connected to someone posing as a bank employee.” The message asked people to press 1 if they did not recognize the activity. They were then connected to someone pretending to be a bank employee. Following a prepared script, the caller pressured them to act quickly and take steps to block their bank cards. The fake employee told victims to download an app and hold their bank card against the phone to scan it. The criminals presented this as a way to secure the accounts. If a caller ever asks you to scan your card with an app, hang up and phone your bank on the number printed on the card. According to police, the app sent data from the card’s near-field communication (NFC) chip, the technology behind contactless payments, to an app run by an accomplice. That person waited at a cash machine or payment terminal and could withdraw thousands of euros without the victim realizing what was happening. “Police described this technical method as one they had not encountered before.” the report concludes. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, NFC)
This2Shell, That2Shell, Everything2Shell. That's pretty much how the last week went. Critical bugs that let attackers run code remotely are showing up everywhere. Hackers are scanning for targets, some flaws are already being used in attacks, and security teams have more urgent patches than they can keep up with. And that's not even the full story. A leaked key led to a software supply-chain
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