Channel
@CyberSecurity_Now
On this record: Growth · Engagement · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
748subscribers
+6 since we began measuring on 27 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001762620849 |
|---|---|
| Type | Channel |
| Username | @CyberSecurity_Now |
| Created | Between 1 December 2021 and 30 April 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 27 August 2026 |
| Last confirmed live | 4 September 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 4 September 2026 |
| On Telegram | t.me/CyberSecurity_Now |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 4 Sept 2026, 04:58 | 748 | +6 |
| 27 Aug 2026, 07:43 | 742 | no change |
| 27 Aug 2026, 00:01 | 742 | first reading |
20 posts held, back to 24 April 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 3 May 2026. An engagement rate over an empty window would be a number about nothing.
1 reaction across 1 post, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 1 | 100.0% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 1 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 1 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 24 April 2026 to 3 May 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
🧠 The most sophisticated mechanism currently observed involves direct manipulation of the Windows Kernel Callback Table (KCT) to achieve stealthy EDR evasion. By manually modifying the kernel's pointer to the Win32k.sys callback array, an adversary can intercept system-wide window messages without triggering standard API hooking alerts. ⚙️ Technically, this involves: Locating the _ETHREAD structure within kernel m…
Threat Actor: APT-41 (Double Dragon) targeting state-level critical infrastructure. The exploitation chain observed bypasses modern EDR solutions through a sophisticated "Bring Your Own Vulnerable Driver" (BYOVD) technique, specifically targeting outdated instances of the Capcom.sys driver. By leveraging the known CVE-2018-19320 vulnerability, the actor achieves kernel-level code execution, effectively blinding 🛡️ …
⚡️ The most insidious mechanism utilized by operatives within the Silk Typhoon ecosystem involves advanced Atom Bombing techniques to bypass modern EDR protections. By leveraging the Windows atom table to store malicious payloads, the attackers side-step standard memory injection signatures 🛡️. Specifically, the thread-context code injection mechanism forces a legitimate process to execute a malicious asynchronous pr…
Diving into the operational structure of Shanghai Powerock Network Co. Ltd., it's clear this wasn't just a random threat actor outfit; it was a textbook case of a shell corporation serving as a "front" for state-sponsored offensive cyber operations. From a backend perspective, these firms act as a 🛡️ proxy layer, obfuscating the source of command-and-control (C2) traffic while managing large-scale exfiltration pipeli…
The extradition of Xu Zewei represents a pivotal shift in how we track Silk Typhoon (Hafnium) operations, specifically regarding how state-sponsored actors manage their post-exploitation persistence. Unlike the opportunistic spraying of the ProxyLogon CVE-2021-26855 vulnerability that defined their 2021 public posture, the 2020 research-targeting campaigns operated via a more surgical, bespoke toolkit designed to byp…
🧠 The most critical, yet overlooked, vulnerability in forensic facial recognition pipelines is the exploitation of "adversarial noise" within latent feature embeddings. When law enforcement tools ingest low-resolution imagery, the underlying neural network maps pixels into a high-dimensional vector space 📉. By injecting carefully calibrated perturbations into the input pixels—essentially "hidden" noise—an attacker ca…
💻 ⚖️ The legal landscape has shifted from "vendor negligence" to a brutal reality of strict enterprise liability. From a backend perspective, if you aren't implementing rigorous output validation or "human-in-the-loop" sanity checks for every inferential trigger, you’re just accumulating toxic technical debt 📉. When an AI agent triggers a defamatory email or a faulty legal action based on its weights, the court doesn…
🧠 The most sophisticated threat vector currently lurking in forensic software involves Adversarial Perturbation Injection within latent print matching pipelines. Unlike standard noise, these crafted patterns exploit the underlying convolutional neural network (CNN) architectures by introducing sub-pixel intensity shifts 📉. By targeting the feature extraction layer of the matching algorithm, an attacker can force a lo…
💻 🧬 The challenge here isn't just about matching alleles; it’s a high-dimensional search problem in Investigative Genetic Genealogy (IGG). When we hit a 96% Ashkenazi Jewish demographic, we’re essentially dealing with a population bottleneck that creates massive amounts of "Identity by Descent" (IBD) noise. Because of the limited ancestral gene pool, standard algorithms often return false-positive matches that look s…
The emergence of "Black-Box Forensic Algorithms" in federal criminal investigations creates a critical blind spot in the chain of custody, specifically regarding Adversarial Machine Learning (AML) vulnerabilities. While these tools promise objective output, they are susceptible to Data Poisoning and Model Inversion attacks that defenders—and prosecutors—are currently ill-equipped to audit. If an actor injects adversa…
🧠 The most insidious vector here isn't the SNMP flaw, but the exploitation of the "Direct Memory Access" (DMA) bridge within the underlying System-on-Chip architecture. By crafting malicious packets that trigger a buffer overflow in the specific serial-to-IP parsing routine, an attacker can overwrite the DMA controller descriptor tables 🧬. This allows the payload to bypass standard OS memory protections entirely. ⚡…
Threat actors are weaponizing the legacy SNMPv1/v2c implementations within these Lantronix and Silex modules to facilitate a "Man-in-the-Middle" pivot point, effectively turning industrial serial-to-IP gateways into persistent persistence nodes 🕸️. The critical vulnerability here isn't just the RCE—it’s the total lack of integrity checking in the bootloader environment, which allows for a stealthy firmware-level root…
Showing the 12 most recent of 20 posts we hold for @CyberSecurity_Now. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 2 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 4 September 2026 — this entry's latest reading, not the date you are reading this.
“CyberSecurity Now” (@CyberSecurity_Now), 748 subscribers as measured 4 September 2026. Telegram Register, tgregister.com/channel/CyberSecurity_Now.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.