French energy giant ENGIE, one of the world's largest power and natural gas providers, confirmed that attackers stole customer data after breaching a marketing platform used by business partners. ENGIE operates in renewable energy, infrastructure, and customer energy supply in 30 countries, employs over 90,000 people, and reported €71.9 billion in revenue last year. An ENGIE spokesperson told BleepingComputer that it cut off the attackers' access to the compromised platform and that they didn't breach the company's internal systems. "ENGIE's internal IT systems were not compromised. ENGIE recently identified unusual data access activity within a tool used by partners as part of their customer prospecting activities and the marketing of energy offers," the spokesperson said. The ENGIE spokesperson didn't say how many customers were affected, but told BleepingComputer the incident is limited to customers in France.
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)
The U.S. Cybersecurity and Infrastructure Security Agency (CISA) has added two critical PHP vulnerabilities collectively tracked as PHP2Shell to its catalog of actively exploited security flaws. Threat actors without privileges can exploit the two security issues (CVE-2026-109618 and CVE-2026-109619) in high-complexity attacks to gain remote code execution (RCE) on unpatched systems. The PHP security team patched the two flaws on Wednesday, October 21, one day after urging security teams to install the security updates as soon as they became available. Although details on the PHP2Shell security flaws (including the CVE IDs and mitigation measures shared with critical infrastructure providers) leaked and some proof-of-concept (PoC) exploits were published online before patches went live, the PHP security team said that none were functional.
The University of Copenhagen in Denmark confirmed that hackers accessed one of its test environments containing personal data belonging to 525,000 people. The educational organization detected the breach last Tuesday and disclosed it on Friday. In an update yesterday, the university added another 20,000 staff and students whose PIN codes were exposed. “The University of Copenhagen has confirmed a cyber attack in which hackers gained access to one of the university’s test environments,” the university says. Founded in 1479, the University of Copenhagen is Denmark’s oldest university and one of its leading research institutions. It has approximately 36,800 students and 10,700 employees, with six faculties and more than 200 research centers. The University of Copenhagen is still investigating to determine the extent of the incident, but warns that the following information may have been exposed:
Sansec tracks a Magento skimmer campaign that infected 953 stores, using five domains, bot filtering and malicious code hidden in CMS blocks. A criminal group has infected 953 online stores since July 2025, mostly Magento shops, according to Sansec researchers. The attackers add a small piece of code to store pages. When someone visits a shop, their browser contacts the attackers’ server to get further instructions. The server checks whether the visitor is a real shopper or a security scanner. If it detects a scanner, it hides the malicious activity. “deepveindrilling.com is the newest web address of a criminal group that breaks into online stores. The group adds a small piece of code to the store’s pages.” Sansec’s analysis states. “The group has done this to 953 stores since July 2025, mostly Magento shops. It moves to a new address every few months.” The group’s latest domain is deepveindrilling.com. Sansec found it in the CMS blocks of 83 stores, almost all running Magento. CMS blocks are content sections that Magento adds to store pages. The domain was registered on October 6, 2026, and the first stores began loading it the following evening. This is the fifth domain linked to a campaign that Sansec has tracked since July 2025 and reported in March as the PolyShell mass attack. Most stores using the latest domain had already been targeted. Of the 83 stores, 60 had previously loaded one of the group’s malicious scripts, and 34 had been doing so since October 2025. The domain sits behind Cloudflare, and its front page returns a 404. It only answers script requests from visitors of infected stores. The loader code lives in the cms_block table, and Sansec’s scanner, eComscan, found it there on 148 server installs over the campaign, never in core_config_data or theme files. eComscan first found the new loader at 21:54 UTC on October 7. By the end of October 8, Sansec’s crawler had found it on 74 stores, as two scripts. The first reads a value called fpCode from the visitor’s browser storage, and when the value is set, the script puts the stored code in an invisible div and clicks it. The browser then runs the code as an event handler, without using eval(). Nothing in the block writes fpCode, so the script from deepveindrilling.com has to set it. A visitor who received the payload once runs it again on every later page view, even when the domain is offline or blocked. The March version deleted its stored payload after one run. On October 23 the operator rewrote the blocks again, and the loader appeared on 9 more stores. All 71 stores Sansec re-crawled by October 24 run the new version. It replaces the plain script tag with an inline loader that sends a browser fingerprint to https://deepveindrilling.com/?s=49b7ca5f&fp=. The fingerprint flags automated browsers through navigator.webdriver, Selenium globals and ChromeDriver’s cdc_ properties. It also adds the GPU name from WebGL, CPU cores, memory, screen size, time zone, languages and client hints. The server picks its response from all of that. The filter works. When urlscan.io loaded an infected store with an automated Chrome browser, it got HTTP 200, and Sansec found no added content in the page. Sansec hasn’t captured the final payload yet. Older payloads give a hint of what real visitors get. Sansec Watch, the firm’s monitoring service for content security policies, shows what the earlier stage served. On December 12, 2025, a customer logged in to an infected store got a script from manulfie.click that loaded https://crypto-insider.top/ssl/ and, five minutes later, https://leqdger.click/ssl/. On December 15 and 16, neswnsssn.click inserted the same leqdger.click script on two more stores. leqdger.click is a misspelling of the crypto wallet brand Ledger. It was registered on September 12, 2025 and has since expired. Before October, the second script was the “Script (async)” integration of Keitaro, a commercial ad tracker that doubles as a traffic distribution system, a tool that sorts visitors. It sends the referrer, page title and query string to a base64 encoded URL such as https://additionalver.com/Mb2z8J?&se_referrer=, where the path is the Keitaro campaign alias. The system then decides per visitor whether to return a payload, a redirect or nothing. “When Sansec requested the additionalver.com script as a Windows desktop visitor on October 24, it returned QWE=123;, a statement that does nothing, and set a cookie that marks the visitor for 24 hours. deepveindrilling.com drops Keitaro and moves the bot filtering into the loader.” continues the report. Here are the five loader domains over 15 months: DomainRegisteredFirst seen on storesStoresmanulfie.clickbefore July 2025, re-registered 2026-05-232025-07-09863neswnsssn.clickbefore December 2025, re-registered 2026-05-222025-12-12494lanhd6549tdhse.top2026-03-252026-03-30355additionalver.com2026-06-072026-06-08246deepveindrilling.com2026-10-062026-10-0783 Two other domains were found on a small number of stores: hdfdsi84j3h.top, registered three minutes after lanhd6549tdhse.top, and c85grt98sd.com. Two older domains, neswnsssn.click and manulfie.click, expired and were registered again in May 2026. Both now use the nameservers of abovedomains.com, a domain parking service. Between August and October 2026, Sansec still found the manulfie.click loader on nine stores and the neswnsssn.click loader on six. When visitors open these stores, their browsers send information about the previous page and the current page title to the new domain owner. The browsers also run any script returned by that domain. Sansec Watch received reports of visitors’ browsers connecting to all four earlier domains. One domain, additionalver.com, was linked to around 52,000 reports. The October activity did not rely on a new vulnerability. Of the 83 affected stores, 60 had already been infected. Earlier attacks appear to have exploited file upload flaws in Magento. In March, the group injected its malicious code into 471 stores in just one hour by uploading webshells through PolyShell (CVE-2026-48356). Some infected Magento stores had files uploaded through PolyShell or SessionReaper (CVE-2025-54236). In a separate group of 3,024 stores affected by another skimmer, the infection rate was 32%. One Magento 2.4.7-p1 store contained PolyShell webshells disguised as PNG images, alongside malicious code in a CMS block. The code switched from additionalver.com to deepveindrilling.com on October 7. The store had not installed Adobe’s PolyShell fix, APSB26-73. However, installing all patches does not guarantee a store is safe. Another store running Magento 2.4.7-p10 had no missing core patches but was infected on October 23. Earlier scans had already found a rogue admin account and a GSocket backdoor. Sansec recommends checking CMS content for malicious code, including base64-encoded domains. Administrators should search both the cms_block and cms_page tables for suspicious patterns such as fpCode and se_referrer. They should also inspect uploaded files for webshells, check for unknown admin accounts, and look for suspicious processes. Removing the malicious scripts is not enough: store owners must also identify and fix the vulnerability or backdoor that allowed the attackers in. After cleanup, change the database, admin and API integration passwords. Install APSB26-73 for PolyShell and APSB25-88 for SessionReaper. Sansec Shield blocks both attacks while you schedule the upgrade. Sansec lists the indicators of compromise at the end of its report. “If your store loads any domain from the indicators of compromise, the attacker can write to your database. Deleting the script does not remove that access. See How to check your store.” concludes the report. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Magento)
DreamHost has forced password resets for email and FTP/SFTP users following an incident that disrupted connections to the File Manager interface. The latest update from the web hosting provider notes that all affected account owners have received recovery instructions. Users will be unable to log in until their account owner sets new credentials. DreamHost is a U.S. web hosting provider and domain registrar that offers WordPress hosting, business email, virtual private servers, dedicated servers, and cloud services. According to the company page, it serves over 400,000 customers in more than 100 countries and supports at least 1.5 million websites and applications, including 750,000 WordPress installations. DreamHost initially reported connectivity problems affecting its File Manager interface on October 23, but the impact quickly expanded to other services.
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.
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.
A Chinese national was arraigned in California after being extradited from the Netherlands for his alleged involvement in a cryptocurrency investment scam that stole nearly $37 million from 174 victims in the United States. In these fraud schemes (also known as pig butchering or romance baiting scams), cybercriminals contact targets through social media, dating sites, and messaging apps, build trust, and lure them into fake investment schemes, stealing their money instead of investing the funds as promised. Prosecutors said that, together with accomplices in the United States and Cambodia, 44-year-old Zhen Zhao tricked victims into transferring large sums of money to attacker-controlled accounts, claiming that the funds would be invested in digital assets. The criminal ring Zhao was allegedly part of reached out to targets across the United States via unsolicited text messages, social media, phone calls, and even online dating services to gain their trust, promoting the fraudulent digital asset investments and saying that the victims' funds' value increased, when, in fact, their money was stolen.
Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock (LockA locked padlock) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites. Share: Opens in a new window Opens in a new window Opens in a new window Press Release CISA Unveils New Strategy to Strengthen National Cyber Defense and Critical Infrastructure Security CISA 2030: Defend, Harden, Prevail Released October 26, 2026 Related topics: Cybersecurity Best Practices , Critical Infrastructure Security and Resilience WASHINGTON – The Cybersecurity and Infrastructure Security Agency (CISA) today released its new strategy, CISA 2030: Defend, Harden, Prevail, which advances President Trump’s Cyber Strategy for America and National Resilience Strategy while strengthening the security and resilience of the nation’s critical infrastructure.
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