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A vulnerability in the Linux kernel's netfilter nft_set_pipapo component could cause out-of-bounds writes due to improper handling of memory allocation failures during cloning of nft_pipapo_match structures. This flaw allows a bad clone to persist into future transactions, potentially leading to memory corruption. The issue has been fixed by tracking the state of clones and preventing operations on those in an error state.

A vulnerability in the Linux kernel's KVM subsystem for arm64 architecture was resolved. The issue involves KVM unconditionally using cacheable memory attributes when constructing an L1 VNCR mapping, even if the underlying page frame number (PFN) is not normal memory. This can cause system errors such as SError on writeback if the endpoint does not support cacheable memory attributes. The fix rejects non-memory mappings to prevent this problem. The vulnerability affects multiple specific Linux kernel versions and has a high CVSS score of 9.3. A patch is available and should be applied to mitigate the risk.

CVE-2026-72099 is a high-severity vulnerability in the Linux kernel's dm-integrity module where the hash_offset variable is incorrectly incremented twice. This flaw was fixed in specific commits after version 5.7. The issue could lead to integrity verification problems due to improper hash offset handling.

CVE-2026-72019 is a high-severity vulnerability in the Linux kernel's macsec_encrypt() function where an unset MAC header leads to a 12-byte heap out-of-bounds read. This occurs on the AF_PACKET SOCK_RAW + PACKET_QDISC_BYPASS transmit path, causing the function to read memory beyond the intended buffer and emit incorrect MAC addresses on the network wire. The issue is fixed by using skb_eth_hdr() to safely access the L2 header when the MAC header is unset.

Metacat is data repository software that helps researchers preserve, share, and discover data. Metacat versions 2.0.0 through 3.4.0 contain an unauthenticated SQL injection vulnerability in the `/cn/v1/object` and `/cn/v2/object` REST API endpoints due to unsanitized user input that can be passed through to the backend SQL database. The `nodeId` parameter can be modified to inject SQL commands, and the results are returned in error messages. Metacat appends the user-supplied data into the sql query without sanitization or parameterization. This allows extraction of arbitrary data from the underlying PostgresQL database, fully exposing protected information to the attacker. This is accomplished by leveraging the error reporting mechanisms in Metacat, where SQL error responses are mirrored back to the caller in the XML error message returned by Metacat. One approach, for example, is to use the PostgreSQL `CAST` function to generate an error with the results of an arbitrary subquery, which is then injected into the XML error message returned by Metacat. Attackers do not need to be authenticated to execute the attack. In addition, arbitrary SQL statements that insert, update, and delete data in the Metacat database can be executed, resulting in full compromise of all data in the database. Full proof of concept attacks have been developed and verified for these vulnerabilities. The impact of this vulnerability is critical for Metacat deployments in the DataONE network where information from the database can be exfiltrated, added, changed, or deleted. This includes management information about the data catalog, access log information about who accessed data, identifying information about individuals including their ORCID identifier and client IP address, access control information about who should be able to access and modify data, and other critical internals of the data system. This sql injection vulnerability was remediated fully in Metacat version 3.4.1. If upgrading to Metacat 3.4.1 isn't immediately possible, most deployments can mitigate the issue by disabling the `/cn` REST endpoints in the webapp deployment. This API is not needed or used by member repositories in the DataONE network, as it is only used by the DataONE Coordinating Node deployments. Consequently, this API can be disabled without reduction of functionality for most deployments. To disable the vulnerable endpoints, simply remove the servlet and servlet-mapping for the `/cn` endpoints in the servlet engine associated with the two servlets, `edu.ucsb.nceas.metacat.restservice.v1.CNRestServlet` and `edu.ucsb.nceas.metacat.restservice.v2.CNRestServlet`. For example, in Tomcat, remove the relevant `servlet-mapping` elements from the application web.xml file in Metacat.

It can be daunting to determine who’s responsible for showing ads on the websites we visit, or who’s harvesting data from the mobile apps we use every day. That information is already semi-public, but it is not easily parsed and traditionally much of it has remained walled away in the hands of large advertising platforms. Not anymore: A powerful and free new service called DecryptAds scrapes and correlates this adtech data and makes it simple to quickly learn a great deal about the entities that are tracking you. A Decryptads summary of the advertising partnerships declared by espn.com. The newly launched decryptads.com says it is constantly scraping the files that websites and apps make publicly available to disclose the companies that are permitted to run ads or collect user data. These files include: – ads.txt : all of the adtech companies and data brokers that may run ads or harvest data from the site; – app-ads.txt : entities that can harvest data from or display ads on mobile and smart TV apps; – buyers.json/sellers.json : the entities buying, selling or reselling ad inventory for a given site or app. Zach Edwards is chief research officer for DecryptAds and a threat researcher at the security company Infoblox . Edwards said he and two other founders decided the service was needed because the adtech data in these files is generally only useful when it can be cross-referenced to build a more complete picture of the advertising ecosystem for each website or app. “It’s an adtech tool but we’re trying to approach adtech from a security perspective,” Edwards said. “It’s really built for a lot of privacy and security use cases that have been dramatically underserved.” Those use cases, he said, include tracking down the source of malicious ads that try to foist malware on targeted users, identifying ad networks located in adversarial nations, and detecting the fast growing swarms of AI-generated slop websites and apps. And as decryptads.com demonstrates, these potential security and privacy threats are near impossible to detect just by viewing a single apps.txt or app-ads.txt file. “Supply-chain integrity issues rarely live in a single file,” the site explains . “They show up as broken cross-references between ads.txt, app-ads.txt, and sellers.json files; as cloned declaration sets across unrelated domains; as seller removals that only make sense when viewed across exchanges; and even as supply paths in bid logs that never actually appear in any given publisher’s authorized-seller list.” A search in DecryptAds for the hugely popular sports network espn.com reveals 143 ad partners and 19 registered data broker domains are listed within its ads.txt and app-ads.txt files. That data broker information is gradually becoming available because four states — California, Oregon, Texas and Vermont — have recently passed laws requiring data brokers to register if they buy or sell data on consumers from those states. DecryptAds reports that almost half of those data brokers are collecting geolocation data from espn.com visitors who aren’t blocking ads, while another three disclose that they collect device fingerprints and sensitive personal information. A visual representation of the complex ad supply chain declared by espn.com. Image: decryptads.com. HIGH-RISK AD PARTNERS DecryptAds also makes it easy to learn the beneficiaries and national origins of the advertising firms lurking in apps and websites, displaying a conspicuous warning when adtech partners of an app or website are based in “geo-risk” areas like China and Russia, or in countries with strong financial and political ties to both — such as Cyprus and the United Arab Emirates (UAE). According to DecryptAds, espn.com works with four different advertising entities that are based in either Russia, China or the UAE, including the adtech firm Between Digital , which lists a New York address. However, the dossier on Between Digital flags them as a Russian firm, showing that their pub…

node-tar is a tar archive manipulation library for Node.js. Prior to 7.5.21, node-tar's filesFilter in src/list.ts uses the recursive mapHas helper to walk an archive entry path upward with path.dirname() and no segment cap when tar.t(...) or tar.x(...) receives a non-empty member-selection list. A crafted GNU L or PAX x long-path header with thousands of slash-separated segments reaches this.filter(entry.path, entry) in Parser[CONSUMEHEADER] in src/parse.ts before Unpack[CHECKPATH] applies maxDepth, causing an uncatchable RangeError stack overflow that terminates asynchronous and streaming Node.js consumers. This issue is fixed in version 7.5.21.

Welcome to this week’s edition of the Threat Source newsletter. “Experiment is the mother of knowledge.” ― Madeleine L'Engle, A Wrinkle in Time “Don't slide down the rabbit hole. The way down is a breeze, but climbing back's a battle.” ― Kate Morton, The Clockmaker's Daughter Hacker Summer Camp has come and gone, which means it’s time for you to start planning next year’s trip. I’m surely going to recap Camp Season, right? Nope. One of the things that I’ve really enjoyed lately is a segment on the Beers with Talos podcast that we call “Make Hazel a Hacker.” If you haven’t listened to it, this is a perfect time to start. Each episode we take a few minutes and pose a security question, term, or concept to Hazel and force her to come up with an idea or explanation on the spot. There are no parameters, so she’s faced with the entirety of information security — past, present, and future. I know, it’s insane. The craziest part is that (I think) Hazel came up with this idea and still volunteered to put herself in the line of fire. As we put Hazel’s feet to the fire, one of my favorite things happens: The rest of us listen in and offer our thoughts during her brainstorming process. Invariably, we’ve got three very different answers, ideas, hints, or directions for her. It’s surely maddening for Hazel, but to me, the best part of the discussion that inevitably follows is that although they’re all different, they’re all correct. For example, this past episode I asked her about a behavioral indicator (regarding “wallpaper.bmp”) that seems benign on its own, but can be interesting to use as a pivot for a threat hunt. We had various interesting angles to consider, backed by years of knowledge and experience. It gave us a good conversation, and that was a .bmp! One of the most nebulous things to learn in this field is that multiple things can be both different and correct. When you are making your decisions this week — whether it’s deciding on a new pivot in your hunting, what devices to prioritize in your patching and updating, or which books or online training to focus on — take a quick second and get a second, third, and fourth opinion. Then try something that’s outside of your normal wheelhouse but sounds good when it’s proposed. None of this is a solo sport. It’s a team game and the best plays come from a mix of perspectives, experiences, and mistakes. The “right” answer can wear many faces, and your ability to hold different truths will lead you to undiscovered territory, the rabbit hole where anomaly lives and breathes. So... welcome back from Vegas. Now go down a rabbit hole on a path you wouldn’t normally take because one of your friends (Joe) or your mortal enemy (Dave) told you that it would work. “She'd been to Narnia, Wonderland, Hogwarts, Dictionopolis. She had tessered, fallen through the rabbit hole, crossed the ice bridge into the unknown world beyond.” ― Anne Ursu, Breadcrumbs The one big thing Cisco Talos recently discovered "JWR," a previously undocumented, real-time phishing framework and likely variant of "The Outsider" phishing-as-a-service platform. JWR uses an open WebSocket connection that allows attackers to monitor keystrokes live and dynamically steer victims through fake checkout and login flows. Currently deployed via SMS lures impersonating regional toll and postal authorities, JWR enables operators to steal payment data, 2FA codes, identity documents, and device fingerprints. Why do I care? Because JWR is operator-driven in real time, attackers can actively bypass multi-factor authentication (MFA) by prompting victims for 2FA codes exactly when needed. The sheer volume of collected data gives threat actors a comprehensive identity profile primed for extensive follow-on fraud and network compromise. Furthermore, JWR's seamless integration with legitimate e-commerce platforms like Shopify makes these lures incredibly convincing to the untrained eye. So now what? Prioritize user education around SMS-based phish…

A vulnerability in Dulwich's porcelain.format_patch function allows commit subjects to be used unsafely as patch filenames. This can lead to directory traversal, causing patch files to be written outside the intended output directory. The issue also causes failures on Windows due to illegal filename characters and can produce excessively long filenames. The flaw is fixed in Dulwich version 1.2.5.

A vulnerability in urllib3's handling of the Proxy-Authorization header during cross-origin HTTP redirects can lead to unintended exposure of authentication credentials. This occurs when the Proxy-Authorization header is manually set without using urllib3's proxy support, and automatic redirects are enabled. The issue is mitigated in urllib3 version 2.8.2_3 and later.

A recent article in The Atlantic ran under the blunt headline, The End of Reading Is Here . According to the piece, research from the National Endowment for the Arts (NEA) found that fewer than 50% of American adults read even a single book in 2022, and only 38% read a novel or short story. At the same time, the U.S. book market keeps breaking its own publishing records year after year: in 2025, for instance, the number of new titles topped four million — up a third from 2024. A huge chunk of that, 3.5 million titles, came from self-publishing, and one of its most powerful engines is Amazon’s Kindle Direct Publishing (KDP). My theory on this mismatch in trends: nobody’s reading books anymore because everyone’s too busy writing them. Joking aside, if there’s still a reader left out there, they’ve probably already guessed where I’m going with this: the all-powerful AI. Not long ago, a New York Times tech journalist was startled to find her own biography on Amazon — and one quick look confirmed it had been written by AI. Digging deeper into the world of AI book publishing, she stumbled onto the work of an extraordinarily prolific retired cybersecurity consultant. What happened next — and where to find reliable information in the age of AI — is what this post is about. Backstory: how a journalist chased the White Rabbit and fell down the hole Let’s start from the beginning. In the summer of 2026, New York Times tech journalist, Kashmir Hill, learned from an acquaintance that a biography about her had been published on Amazon a year earlier. It was titled The Biography of Kashmir Hill: The New York Times Technology Journalist Who Exposed Clearview AI, Challenged Big Tech and Redefined Privacy in the Digital Age . And she was more than stunned since nobody had ever reached out to her, or to anyone she knew, to research the book. “Alice started to her feet, for it flashed across her mind that she had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it…” The author of record was one John Crane Miller, and the photo on his Amazon page turned out to be a generic stock image of a white man: the kind that shows up all over the internet. Hill couldn’t find a shred of personal detail or contact information for him. But digging deeper into his Amazon profile turned up something else: in a single week in the summer of 2025, he had published biographies not just of Hill, but of nine other American journalists. Even the most trusting reader would find it hard to believe a literary genius could produce this kind of staggering output without a little robotic help. The AI-generated biography of journalist Kashmir Hill, which she stumbled across by accident on Amazon. Source And yet the hardcover of Kashmir Hill’s biography cost a full US$26.99 — despite running to just 90 pages. After reading it, Hill learned a whole slew of “facts” about herself she’d never known — including a detailed description of her coffee-making ritual (which, as the journalist notes, is actually her husband’s duty). Mr. Miller even claimed in the text to have read Kashmir Hill’s childhood diary. “…burning with curiosity, she ran across the field after it, and fortunately was just in time to see it pop down a large rabbit-hole under the hedge.” Hill had no luck tracking down the author of her biography. She left a comment on the book’s Amazon page asking him to get in touch. Nobody wrote back — and not long after, every book under John Crane Miller’s profile vanished. But that gave her a lead on several other AI authors on Amazon. In particular, she came across a certain Bill Johns, whose author page listed a staggering 445 books . Not expecting much to come of it, she asked Johns if he’d be willing to do an interview — and was more than a little surprised when he agreed. It was probably right around then that Kashmir Hill’s investigation turned into a genuine trip down the AI rabbit hole… “In another moment down went Alice after it, neve…

Untrusted data inclusion in PostgreSQL psql COPY may allow a server administrator to elicit execution of data lines as psql commands, via error injection. If the "COPY FROM STDIN" or "\copy FROM STDIN" command fails before the server indicates that it awaits input rows, psql processes the in-line data rows as psql commands. "COPY FROM" with a filename is unaffected. The server administrator has no inherent control over the data rows, so a complete attack requires the attacker to separately acquire control of both the server and the data rows. Alternatively, an attacker controlling data rows alone might complete an attack through a coincidental error that they don't control. Versions before PostgreSQL 18.6, 17.11, 16.15, 15.19, and 14.24 are affected.

Cisco Talos recently identified an undocumented phishing framework, internally branded "JWR" by its developer, built to convincingly impersonate checkout and login pages across major payment and shopping platforms. The client engine of the JWR phishing framework is a real-time, operator-driven system that, rather than merely logging form submissions like a static credential-stealing page, keeps an AES-CTR encrypted WebSocket open to the threat actor so they can steer each victim's session live. The victim data targeted by the actor using JWR extends well beyond payment data, encompassing identity documents, Social Security numbers, passport and driver's license images, website and PayPal credentials, 2FA codes, and full device fingerprints, all committed to the actor's server once a session ends. Talos assesses with medium confidence that the JWR phishing framework is a variant of "The Outsider," a phishing-as-a-service (PhaaS) platform, based on several similarities in the client engine scripts and functionalities of the two PhaaS platforms. Talos observed a real-world campaign delivering the JWR client via SMS lures impersonating toll authorities, and postal and courier services of several countries in Southeast Asia and the Middle East. JWR phishing framework, a likely variant of the Outsider JWR is a phishing framework capable of harvesting complete payment card data, login credentials, and personally identifiable information (PII) documents and images in real time. The client-side engine of the framework impersonates login, and checkout flows of several payment gateways, including Shopify, PayPal, Apple, Klarna, and banks, while allowing the operator to stealthily control the victim session through an AES-CTR encrypted WebSocket channel. The client engine architecture is divided into a Host Bridge module that relays commands into a phishing inline frame (iframe) and a Vue.js victim application that renders across 44 phishing pages, streams the victim's keystrokes to the actor as they are typed, and carries out more than 40 distinct instructions issued from the command-and-control (C2) console. The data exfiltration schema is a cvvform object that includes fields such as credit card number, CVV, PIN, expiry date, Social Security Number (SSN), passport or ID images, two-factor authentication (2FA) codes, website logins, PayPal credentials, and device fingerprint. Talos discovered that the JWR client engine shares significant code and functional similarities with the client of The Outsider PhaaS platform operated by the Chinese-speaking actor “Outsider Enterprise,” which was reported by external researchers . JWR client architecture and workflow Figure 1. JWR phishing framework’s client engine architecture and execution flow. The execution starts when the parent phishing webpage loads and executes the client's engine. It checks a single global flag, window.__HOST_MODE , which is set by the parent phishing page, and selects one of two execution modes. If the flag is set, the script enters Host Mode, and control passes to the Host Bridge module, an immediately invoked function expression (IIFE) that operates within the parent page, typically a replica of a legitimate checkout or account login page, relaying received details into a child iframe that contains the actual phishing form. It establishes a persistent WebSocket connection to the actor’s C2 server. If the flag is not set, the page enters Content Mode, and control passes to the Vue.js Application, an interactive front end that renders the phishing pages, collects victim input, manages the flow across 44 HTML files, and handles the actor’s instructions from the C2 server, ultimately redirecting to a custom error page after sending the data to the C2. The Content Mode of execution has three communication modes: standalone, pluginIframe, and hostIframe. In standalone mode, the application fully owns its WebSocket connection. In pluginIframe mode, it has no direct link to t…

NortheBridge/luminalshine is a Sunshine-compatible game stream host for Moonlight. Prior to version 26.05.0-rc4, a latent gap exists on a default install, the file at `src/platform/windows/misc.cpp` lives at `C:\ProgramData\LuminalShine\config\apps.json` and is created by the `SYSTEM` service. Under Windows' default `C:\ProgramData` inheritance, that gives `BUILTIN\Users` only Read+Execute — not writable — so the canonical EoP doesn't actually trigger on a vanilla install. Version 26.05.0-rc4 contains a patch for the issue. As a workaround, use default condition DACLs for `ProgramData`.

The npm package @years19/n8n-nodes-utils-helper-t version 1.0.0 contains malicious code that executes automatically during installation and import. It writes and runs a Python UDP/TCP flood script targeting a hardcoded IP address, effectively turning the host into a participant in a DDoS attack. Concurrently, it collects system identity and load information, encodes it, and exfiltrates the data to a remote server with disabled TLS verification.

The npm package @years18/n8n-nodes-utils-helper-t version 1.0.0 contains malicious code that executes during installation and runtime. It collects system information and sends it to a remote server, disables TLS verification, and establishes a command channel allowing remote command execution. Additionally, it installs and runs a Python-based DDoS toolkit on the host. The package name is designed to mimic a legitimate n8n community node to trick users into installing it.

CVE-2026-68442 is a high-severity use-after-free vulnerability in the Linux kernel's btrfs filesystem. It occurs due to improper handling of the EXTENT_FLAG_LOGGING flag when splitting extent maps, causing the flag to be incorrectly propagated and not cleared. This leads to a use-after-free condition when the affected extent map is freed while still linked in the inode's modified_extents list. The issue was introduced by a commit that changed how flags are cleared, and has been fixed by restoring the correct flag clearing behavior.