Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 67% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and the other topics.
Growth
60 measurements spanning 92 days, net +502. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 19,150–20,141 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 60
Measured (UTC)
Subscribers
Change
6 Nov 2026, 21:56
20,027
+9
5 Nov 2026, 20:49
20,018
+13
4 Nov 2026, 23:26
20,005
+9
2 Nov 2026, 21:57
19,996
+8
2 Nov 2026, 00:34
19,988
+2
1 Nov 2026, 02:41
19,986
+21
30 Oct 2026, 05:40
19,965
+7
29 Oct 2026, 02:11
19,958
+3
28 Oct 2026, 02:28
19,955
-2
27 Oct 2026, 05:19
19,957
+20
26 Oct 2026, 03:29
19,937
+8
25 Oct 2026, 06:18
19,929
+2
24 Oct 2026, 09:17
19,927
+5
23 Oct 2026, 07:04
19,922
+10
22 Oct 2026, 07:15
19,912
+60
21 Oct 2026, 02:31
19,852
+44
20 Oct 2026, 02:38
19,808
+32
19 Oct 2026, 05:39
19,776
+31
18 Oct 2026, 04:57
19,745
+31
17 Oct 2026, 08:28
19,714
first reading
Engagement
92 posts held, back to 30 July 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 71 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
10.2%
avg views ÷ 20,027 subscribers
Avg views / post
2,040
20 posts measured
Reaction rate
0.531%
reactions ÷ views · ER floor
Posts in window
20
of 92 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
What these figures were computed from
Window
Rolling 30 days · latest post in window 6 November 2026
Posts held
92 (30 July 2026 – 6 November 2026)
Views total
40,704
Reactions total
216
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Nov 2026, 04:24 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Photos
≈1,510
Videos
≈243
Links
≈6,630
Lifetime counters from Telegram’s own channel header, read 7 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Video runtime
18m 04s
Average length
1m 48s
Measured directly from 10 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
706 reactions across 92 posts, in 8 distinct kinds. The most used accounts for 70.0% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
494
70.0%
👍
78
11.0%
🔥
55
7.79%
😁
39
5.52%
🤯
18
2.55%
🤔
12
1.70%
🌚
7
0.992%
💯
3
0.425%
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 92 of the 92 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 706 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 92 most recent posts we hold, published 30 July 2026 to 6 November 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.
Telegram Stars
Stars received
3
across the posts below
Posts paid on
1
of 92 we hold a reading for · 1%
Most on one post
3
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @cybersecinform. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 92 most recent posts we hold for this entry, published 30 July 2026 to 6 November 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
Recent posts
Telegram’s own ‘edited’ marker: 0 of 20 posts held from the last 30 days (0%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
Введение в Harness Engineering для исследования уязвимостей.
Опубликована статья, посвящённая применению Harness Engineering в исследованиях безопасности и поиске уязвимостей.
https://grahamhelton.github.io/harness-engineering-workshop-book/introduction.html
👉 @cybersecinform
iPhone 18 Pro удалось взломать через AirDrop, причём даже включённый Lockdown Mode не помог.
В опубликованной демонстрации показали, как отправка одного PDF-файла через AirDrop позволяет скомпрометировать iPhone 18 Pro под управлением iOS версии ниже 27.2.
Причём вредоносное ПО сохраняется в системе даже после обновления до последней версии iOS. То есть простая установка обновления уже заражённое устройство не очищ…
В открытый доступ выложили SAPPER — инструмент, позволяющий запускать целые команды ИИ-агентов для автономного пентестинга.
Работает всё по принципу небольшой хакерской команды. Один ИИ-агент выступает командиром, планирует операцию и распределяет задачи между остальными агентами, которые самостоятельно выполняют проверки безопасности.
Для работы используются настоящие инструменты вроде Nmap и Nuclei. У агентов ест…
С помощью OpenAI Codex в Linux устранили 149 уязвимостей безопасности.
Исправления для этих уязвимостей уже существовали в новых версиях ядра Linux, однако в более старых версиях они по-прежнему отсутствовали.
Специалисты по кибербезопасности из Trail of Bits использовали Codex, чтобы находить такие пробелы, адаптировать исправления для старых версий ядра и проводить тестирование.
К 29 октября благодаря этой работ…
DeepSeek-V4.1-Flash-1017 без цензуры теперь можно запускать локально на видеокарте с 12 ГБ видеопамяти.
Модель работает на обычном пользовательском оборудовании и, как утверждается, не отказывает в выполнении запросов.
https://huggingface.co/AtomicChat/DeepSeek-V4.1-Flash-1017-Uncensored-GSQ-RCO-GGUF
👉 @cybersecinform
Инструменты для реверс-инжиниринга под Windows теперь можно напрямую подключать к ИИ-агентам.
Новый MCP-сервер позволяет Claude программно устанавливать breakpoints, выгружать содержимое памяти, пошагово выполнять код и проводить полноценный реверс-инжиниринг.
Теперь ИИ может самостоятельно отлаживать и анализировать программы, выполняя эти операции через инструменты.
Проект будет особенно полезен специалистам по …
Серьёзно, почему Docker присваивает контейнерам настолько безумные имена? 😭
Запускаешь контейнер, не указывая ему имя, а Docker совершенно спокойно придумывает что-нибудь вроде suspicious_tesla, angry_morse или goofy_fermat.
КТО ВООБЩЕ ПРИДУМАЛ ЭТУ СИСТЕМУ ИМЕНОВАНИЯ??
👉 @cybersecinform
На GitHub опубликован полноценный курс по DevOps, охватывающий создание инфраструктуры для продакшена.
Курс состоит из 18 практических лабораторных работ, в ходе которых вы пройдёте весь путь развёртывания одного приложения: от Docker и CI/CD до Terraform, Kubernetes, Helm, Argo CD, Prometheus, Grafana и постепенного развёртывания обновлений.
https://github.com/inno-devops-labs/DevOps-Core-Course
👉 @cybersecinform
Создание лаборатории для разработки вредоносного ПО. Часть 1: Elastic EDR на базе собственного Elastic Stack.
Автор: bsdrip.
В первой части серии рассматривается развёртывание Elastic EDR с использованием самостоятельно размещённого Elastic Stack.
https://blog.bsdrip.tech/maldev-lab/01-elastic-edr-lab-setup/
👉 @cybersecinform
Серия материалов memN0ps по разработке эксплойтов для пользовательского режима Windows (Windows User Mode Exploit Development).
- Часть 1. Основы (Basics): https://t.co/EltfALmcNr
- Часть 2. Структурированная обработка исключений (SEH): https://t.co/74yeagnDTe
- Часть 3. Поиск полезной нагрузки в памяти (Egg Hunter): https://t.co/24TywGftZY
- Часть 4. Предотвращение выполнения данных (DEP): https://t.co/OT514lUXhp
…
❤3
Showing the 12 most recent of 92 posts we hold for @cybersecinform. 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.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Posts recorded as deleted after publishing
We recorded 7 posts on @cybersecinform as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.
A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.
First seen missing
10 August 2026
Most recent seen missing
6 November 2026
Median time before seen missing
3 days
Seen missing in the last 30 days
2
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
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.
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 2 October 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
Appears in Telegram’s recommendations for other channels
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.
Python Portal @PythonPortal · 50,972 Telegram ranks this channel #4 of 80 here — alongside 79 others — read 25 August 2026
IT Portal @IT_Portal · 118,869 Telegram ranks this channel #12 of 78 here — alongside 77 others — read 15 August 2026
ZeroDay | Кибербезопасность @cybersec_academy · 42,632 Telegram ranks this channel #21 of 70 here — alongside 69 others — read 29 August 2026
Montana.exe @dejavu041 · 49,540 Telegram ranks this channel #23 of 80 here — alongside 79 others — read 25 August 2026
CYBER HUNTER - Кибербезопасность @cyberbezopasno · 58,740 Telegram ranks this channel #24 of 50 here — alongside 49 others — read 26 August 2026
Белый хакер @bezopasno_soft · 29,631 Telegram ranks this channel #26 of 78 here — alongside 77 others — read 8 September 2026
Kali Linux @linuxkalii · 55,825 Telegram ranks this channel #26 of 89 here — alongside 88 others — read 23 August 2026
Бункер Хакера @book_academ · 21,306 Telegram ranks this channel #34 of 72 here — alongside 71 others — read 23 September 2026
Python Developer @python_tg · 21,210 Telegram ranks this channel #45 of 85 here — alongside 84 others — read 25 September 2026
Лаборатория хакера @lab_hack · 25,457 Telegram ranks this channel #46 of 71 here — alongside 70 others — read 14 September 2026
no system is safe // cybersec @nsis_cybersec · 21,198 Telegram ranks this channel #49 of 71 here — alongside 70 others — read 23 September 2026
Хауди Хо - IT юмор, жизнь программиста @howdyho_official · 77,552 Telegram ranks this channel #65 of 90 here — alongside 89 others — read 18 August 2026
Простой Python | Программирование @python_piton_javascript · 123,933 Telegram ranks this channel #67 of 84 here — alongside 83 others — read 13 August 2026
Frontend Portal @FrontendPortal · 36,280 Telegram ranks this channel #73 of 90 here — alongside 89 others — read 1 September 2026
This channel appears in 14 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
Cite this entry
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 6 November 2026 — this
entry's latest reading, not the date you are reading this.
“InfoSec Portal” (@cybersecinform), 20,027 subscribers as measured 6 November 2026. Telegram Register, tgregister.com/channel/cybersecinform.
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.