Comcast is promoting WiFi-based motion detection as a part of its new Xfinity Shield home protection platform, allowing routers and wireless devices to detect people moving through a home without cameras or motion sensors. This feature was announced as part of a new Xfinity Shield product offering on Tuesday, a new application suite that combines cybersecurity, physical home monitoring, and family safety features through Xfinity WiFi and the Xfinity app. Part of this new offering is WiFi Shield, which is included at no additional cost for Xfinity Internet customers with compatible gateways. It combines Xfinity's CyberSecure network protection, Family Settings, and WiFi Motion, with Home Watch, Away Watch, and Dark Watch modes used to control when notifications are generated. Comcast is also launching Shield Select for $15 per month, which adds an indoor camera, door/window sensor, cloud video storage, and 24/7 urgent response functionality.
Researchers tested 31 million patterns to disrupt surveillance AI, with promising results but significant gaps between simulation and real-world use. The Kansas City-based cybersecurity researcher Bill Swearingen spent the past year doing something that sounds almost too simple to work: printing patterns, watching cameras fail to detect them, and repeating. TechCrunch reports that after roughly 31 million tests, he can now generate patterns on demand that block license plate readers and surveillance cameras from recognizing whatever the pattern covers, whether that’s a person or a vehicle. The project is called noRecognition, and the core idea isn’t stealth in the traditional sense. The camera still records everything just fine. What breaks is the detection layer sitting on top of the footage, the software that flags license plates, tracks faces, or spots “activity of interest” across thousands of hours of video. Swearingen’s patterns don’t hide you from the lens; they make the algorithm looking through that lens shrug and move on. Swearingen, co-founder of the SecKC meetup, said his project started for personal reasons. He became concerned about the growing number of surveillance cameras in his town and the possibility of being tracked while attending a protest. What started as a simple experiment later became a reinforcement learning system. He taught the model to create patterns, learn from failures and keep improving. Over time, it learned how to avoid detection by several camera systems. Every time a pattern failed and got detected, the system adjusted and tried again, eventually learning to defeat multiple detection algorithms simultaneously rather than just one at a time. The research dashboard behind the project, published at sandbox.norecognition.org, goes considerably deeper into the numbers than the headline claim suggests, and it’s refreshingly upfront about what’s proven versus what isn’t. The team states its overall objective plainly as “one pattern that defeats every detector,” and by their own account that goal remains only partially met. Their strongest validated result against a detector extracted directly from a real deployed surveillance camera sits at 61.7% non-detection across held-out test subjects, a solid number, but nowhere near total, and still a digital simulation rather than a real-world fabric test. That distinction matters more than it might seem. Most of the dashboard’s headline figures are explicitly labeled as digital, simulated results, meaning the pattern was tested against a virtual camera and printed ink model rather than an actual garment photographed by an actual camera in the field. The gap between “works in simulation” and “works when Donut Media wraps a real 2009 Toyota Yaris in it,” which is the physical test Swearingen ran live at DEF CON, is exactly the gap this kind of research has to close before anyone should treat it as a reliable, everyday privacy tool. “On Friday at the Def Con cybersecurity conference in Las Vegas, Swearingen ran his first real-world test. With help from Donut Media, the test involved covering a 2009 Toyota Yaris with one of Swearingen’s newest patterns to see if the car would be invisible to detection by a Flock camera.” reports TechCrunch. “We proved it was effective,” said Swearingen, though the wheels were a challenge. The video of the demo will be out in the next few weeks, said Donut Media.” That DEF CON demo is where things got concrete. Swearingen covered a car in one of his newest patterns and tested it against a Flock Safety camera, the kind widely deployed for automated license plate reading across the US. He said the test proved effective, though the vehicle’s wheels turned out to be a persistent weak point, curved surfaces apparently don’t cooperate with flat printed patterns the way a car door does. Source Tech Crunch – A photo of a 2009 Toyota Yaris at the Def Con conference in Las Vegas, covered in a pattern made by Bill Swearingen, as part of a test to see if it can defeat surveillance camera detection. Image Credits:Bill Swearingen / Donut Media Swearingen is not publishing his best patterns because he does not want camera makers to easily find and block them. Instead, he is using crowdfunding to develop and sell printed products such as T-shirts and hoodies, with vehicle wraps possibly coming later. It is still unclear whether the project will become a practical privacy tool for everyday users or remain mainly a DEF CON demonstration. Its real effectiveness will depend on how well the patterns work on real clothing, in different weather and camera conditions. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Surveillance camera)
U.S. Cybersecurity and Infrastructure Security Agency (CISA) adds a Ray-Project Ray vulnerability to its Known Exploited Vulnerabilities catalog. The U.S. Cybersecurity and Infrastructure Security Agency (CISA) added a Progress LoadMaster vulnerability, tracked as CVE-2025-62593 (CVSS score of 9.4), to its Known Exploited Vulnerabilities (KEV) catalog. CVE-2025-62593 is a critical remote code execution (RCE) vulnerability in Ray, an AI compute engine. Versions before 2.52.0 insufficiently protected the Ray dashboard/API against browser-based attacks. Its defense relied on checking whether the HTTP User-Agent header started with “Mozilla”, but browsers can modify this header. By combining this weakness with DNS rebinding, an attacker could potentially execute arbitrary code on a developer’s machine simply by getting them to visit a malicious website or view a malicious advertisement while running Ray. The vulnerability affects Firefox and Safari. Ray 2.52.0 fixes the issue. “This vulnerability is due to an insufficient guard against browser-based attacks, as the current defense uses the User-Agent header starting with the string “Mozilla” as a defense mechanism. This defense is insufficient as the fetch specification allows the User-Agent header to be modified.” reads the advisory. “Combined with a DNS rebinding attack against the browser, and this vulnerability is exploitable against a developer running Ray who inadvertently visits a malicious website, or is served a malicious advertisement (malvertising).” “An attacker exploited a code injection vulnerability in Ray AI Compute Engine via a DNS rebinding attack, leading to remote code execution. This allowed the attacker to escalate privileges within the system, move laterally across the network, establish command and control channels, exfiltrate sensitive data, and ultimately disrupt operations.” reads the analysis published by Aviatrix. 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 vulnerability by the end of this week, on August 20, 2026. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, CISA)
Evooo1Bot is a Mirai-based Linux botnet that hijacks routers and IoT devices for DDoS attacks, credential theft and criminal proxy services. Fortinet’s FortiGuard Labs disclosed Evooo1Bot in mid-August, a previously undocumented Linux botnet that’s been active since July 2026. The bot borrows Mirai‘s DDoS engine but adds encrypted command-and-control communications, an SSH brute-force scanner, a credential sniffer, and a SOCKS5 proxy module on top. “FortiGuard Labs has been tracking a previously undocumented Linux botnet family, which we have named Evooo1Bot. The name derives from the hardcoded string “evooo1” found in every binary.” reads the report published by Fortinet. “While the malware reuses the DDoS engine from the publicly leaked Mirai source code, it extends the original framework with numerous capabilities, including encrypted C2 communications, an SSH brute-force scanner, a SOCKS relay module, a credential sniffer, and an integrated exploit arsenal targeting multiple known vulnerabilities.” The botnet targets 18 known CVEs, some of them dating back to 2007, including: CVE-2007-3010: Alcatel OmniPCX Enterprise Remote Code Execution Vulnerability CVE-2016-6277: NETGEAR Multiple Routers Remote Code Execution Vulnerability CVE-2018-14558: Tenda AC7, AC9, and AC10 Routers Command Injection Vulnerability CVE-2019-14931: Mitsubishi Electric Europe B.V. ME-RTU devices and INEA ME-RTU devices remote Command Injection vulnerability CVE-2020-10987: Tenda AC1900 Router AC15 Model Remote Code Execution Vulnerability CVE-2021-46422: Telesquare SDT-CW3B1 Command Injection vulnerability CVE-2022-37055: D-Link Routers Buffer Overflow Vulnerability CVE-2024-29269, Telesquare TLR-2005KSH Command Injection Vulnerability CVE-2025-10123, D-Link DIR-823X Command Injection Vulnerability CVE-2025-55583: D-Link DIR-868L B1 router Command Injection Vulnerability The bot communicates exclusively over port 443, which is intentional: the traffic blends into expected HTTPS flows at the network perimeter. After gaining initial access through one of its exploit modules or via brute-forced SSH credentials, the bot runs a loader script that clears Bash history to erase evidence of the intrusion before pulling the architecture-appropriate binary from an external server. The breadth suggests the operators are scanning opportunistically for anything unpatched rather than targeting specific organizations. “This capability significantly increases the value of an infected host to attackers. The victim’s IP address can be used to disguise malicious traffic, bypass geographic restrictions, or provide access to internal networks through an already compromised machine.” continues the report. “In larger botnets, the same functionality could also be used to build a distributed proxy infrastructure, enabling anonymous traffic forwarding or monetization through residential and enterprise proxy services.” Evooo1Bot stands out because of its proxy module. A network of compromised routers, cameras, and firewalls acting as SOCKS5 relays is a valuable commodity; operators can use it themselves to obscure attack traffic, or sell access to other criminals looking for residential or enterprise IP addresses that don’t trigger geographic blocks. “Unlike typical botnet commands that focus on downloading payloads or launching attacks, the !socks module turns an infected host into a SOCKS5 proxy that the operator can use as a network relay. It supports two operating modes. In direct mode, it opens a SOCKS5 listener on the infected host on the default TCP port 1080 and waits for incoming client connections. The implementation first attempts to create a dual-stack IPv6 listener and falls back to IPv4 if that fails. Each accepted client is then passed to the session handler for proxying.” continues the report. “The botnet also implements a reverse relay mode. Instead of exposing a listening port, the bot establishes an outbound encrypted connection to an operator-specified relay server. This persistent control channel listens for commands such as RELAY_NEW:<session_id>, which indicate that a new proxy session should be created.” After establishing C2 contact, the bot accepts commands covering the full post-compromise toolkit: file upload and download, interactive shell access, persistence installation, binary updates, HTTP Basic Auth and Cookie header interception, DDoS over DNS, TCP, and UDP, and the HTTP exploit dispatcher. The credential sniffer intercepts authentication headers in transit, so any HTTP Basic Auth credentials passing through an infected device can be captured without any additional effort from the operator. If you’re still running devices with unpatched firmware from the CVE list above, or if any of your edge hardware is using default SSH credentials, Evooo1Bot is already scanning for you. “Beyond traditional botnet functionality, it features encrypted C2 communications, multiple layers of string obfuscation using AES-256-CTR, ChaCha20, and XOR-based key derivation, as well as a 28-command remote administration interface.” concludes the report. “These capabilities place Evooo1Bot well beyond the technical baseline of conventional Mirai-derived malware.” Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Evooo1Bot botnet)
Two critical vulnerabilities impacting MLflow, an open-source artificial intelligence (AI) platform, and FUXA, an open-source, web-based SCADA / HMI software built for operational technology (OT) and industrial automation, are witnessing malicious scanning and exploitation efforts. According to independent reports from watchTowr and VulnCheck, the vulnerabilities in question are as follows -
A single piece of infrastructure has been pulling records out of Salesforce and ServiceNow customer portals across multiple industries for more than a year, according to research published this week by agent security platform Reco. The activity, which Reco has named the City Forum campaign after a domain tied to the attacker's IP address, traces back to one server: 158.220.87.79, hosted on a
Pokémon Center is notifying customers in the United Kingdom and Germany that it suffered a third-party data breach after hackers stole customer personal and order information from third-party logistics provider CEVA Logistics. While CEVA's systems were compromised in the cyberattack, the exposed records belonged to Pokémon Center customers who submitted orders on the site. The company then shared this information with the logistics provider to fulfill and ship PokemonCenter.com orders. CEVA Logistics is a subsidiary of the CMA CGM Group, the world's third-largest shipping company. The logistics provider operates 1,000 warehouses, handled 15 million shipments last year, and reported $18.3 billion in revenue in 2025. The company recently suffered a cyberattack in which attackers breached its servers between July 29 and August 1, affecting multiple retailers in Europe.
The SANDCLOCK LiteLLM supply-chain attack exposed credentials across 2,038 repositories, affecting technology, finance, healthcare, retail and more. Resecurity (USA) estimated the most affected sectors by the “SANDCLOCK” backdoor, which was planted as a result of the code repository compromise. According to cybersecurity experts, LiteLLM / TeamPCP Supply-Chain Attack will have long-lasting consequences. By compromising a well-known component in AI applications, adversaries will multiply the blast radius—some of the victim organizations are still unaware of the backdoor and its impact. LiteLLM is a popular open-soure AI gateway and utility library that unifies API calls for over 100 large language model providers, such as OpenAI, Anthropic, Google Gemini, and local Ollama models. Such incidents involve substantial MTTD (Mean Time to Detect) and MTTR (Mean Time to Respond). The threat actor group “TeamPCP” compromised maintainer credentials for LiteLLM and published malicious package versions 1.82.7 and 1.82.8 to PyPI around March 2026 – creating a window of exposure lasting at least a few months. Over 2,500+ organizations and hundreds of thousands of CI/CD environments suffered full-credential exposure, compromising cloud infrastructure keys, repository access tokens, SSH credentials, Kubernetes secrets, and AI provider API keys (such as OpenAI and Anthropic). Resecurity has acquired the 150GB archive attributed to the LiteLLM supply-chain attack conducted by TeamPCP using the “SANDCLOCK” credential-stealer. Per published incident reporting — accompanying victim manifests enumerate 898 compromised GitHub owners (organisations/accounts) across 2,038 repositories. The affected owners include major global enterprises — among them Microsoft, Azure, IBM, NVIDIA, PayPal (Zettle), Deloitte, Bosch, S&P Global, Elevance Health, 84.51° (Kroger), Adeo (Leroy Merlin), Kärcher, Dräger, ID.me and 1inch. Top 10 the most impacted sectors (by victim organization profile): Technology / Software Banking / Finance / Insurance Healthcare / Pharma / Medtech Retail / E-Commerce Media / Gaming / Adtech Manufacturing / Industrial Professional Services Cybersecurity Crypto Government Resecurity enumerated 2,146 records by key name (values never inspected beyond structural masking). The composition is overwhelmingly GitHub CI-CD identity material, with a long tail of high-value cloud and registry credentials. Victim manifests (owners.txt, repos.txt) enumerate 898 distinct compromised GitHub owners across 2,038 repositories. The distribution is long-tailed: 631 owners have a single affected repo, while the most-affected owner (Cencosud-Cencommerce) has 64. Critically, the owner list includes major global enterprises and regulated organisations. Every organization affected by the LiteLLM incident should revoke or rotate GitHub App private keys, PATs, AWS/GCP/Firebase credentials, ECR/JFrog tokens, SSH keys, and signing passwords, and invalidate sessions. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
The French Ministry of the Economy and Finance has disclosed a data breach after an attacker accessed the General Directorate of Public Finances (DGFiP) systems and stole data belonging to 678,000 individuals. This incident was discovered after a threat actor using the "ZeroBytes" handle claimed the attack and listed a stolen database for sale on August 12 on the PwnForums hacking forum. "The in-depth investigations conducted since August 12, 2026, have established that, prior to their interruption, these access points had been used to consult and extract data concerning a total of 678,000 individuals and professionals, including tax data such as reference tax income, family quotient, and withholding tax rate, and, for businesses, data such as their company name and SIREN number," the French Finance Ministry said.
GitLab has released security updates to address a critical vulnerability impacting its Community Edition (CE) and Enterprise Edition (EE) software that, under certain conditions, could allow an unauthenticated attacker to remotely modify or delete public projects and user data. The flaw, tracked as CVE-2026-19478, has been rated Critical by GitLab and assigned a CVSS score of 9.4. Released on
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