macOS Red Teaming. • Gathering System Information Using IOPlatformExpertDevice; • Targeting Browser and Diagnostic Logs; • Manipulating the TCC Database Using PackageKit; • Leveraging Application Bundles and User-Specific Data; • Taking Over Electron App TCC Permissions with electroniz3r; • Exploiting Keychain Access; • Signing Your Payload; • Exploiting Installer Packages; • Exploiting DMG Files for Distri…

Channel
Cybersecurity and Digital Forensics
@DigitalForensicAndCyberSecurity
On this record: Topic · Growth · Engagement · Reactions · Posts · Telegram's recommendations · Cite this entry
912subscribers
+6 since we began measuring on 1 September 2026
Risers and fallers across the register · movement among entries of Under 1,000.
Register entry
| Telegram ID | -1001365546890 |
|---|---|
| Type | Channel |
| Username | @DigitalForensicAndCyberSecurity |
| Created | Between 1 April 2018 and 31 August 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 1 September 2026 |
| Last confirmed live | 18 September 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 18 September 2026 |
| On Telegram | t.me/DigitalForensicAndCyberSecurity |
Topic
Hacking & security — 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 19 September 2026 and assigned it the closest of 31 fixed categories, at 93% 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 why it has no browse page of its own yet.
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 18 Sept 2026, 19:59 | 912 | +7 |
| 11 Sept 2026, 00:57 | 905 | -1 |
| 1 Sept 2026, 18:05 | 906 | no change |
| 1 Sept 2026, 12:30 | 906 | first reading |
Engagement
20 posts held, back to 20 December 2024 — 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.
- ERR · 30 days
- 7.24%
- avg views ÷ 912 subscribers
- Avg views / post
- 66.0
- 1 post measured
- Reaction rate
- —
- this channel exposes no reaction counts
- Posts in window
- 1
- of 20 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.
| Window | Rolling 30 days · latest post in window 30 August 2026 |
|---|---|
| Posts held | 20 (20 December 2024 – 30 August 2026) |
| Views total | 66 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 1 Sept 2026, 12:30 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.
Reaction mix
20 reactions across 11 posts, in 3 distinct kinds. The most used accounts for 65.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 13 | 65.0% | |
| 👍 | 5 | 25.0% | |
| 👎 | 2 | 10.0% |
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 11 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 20 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 20 December 2024 to 30 August 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.
Recent posts
Pentration Testing, Beginners To Expert! • Phase 1 – History; • Phase 2 – Web and Server Technology; • Phase 3 – Setting up the lab with BurpSuite and bWAPP; • Phase 4 – Mapping the application and attack surface; • Phase 5 – Understanding and exploiting OWASP top 10 vulnerabilities; • Phase 6 – Session management testing; • Phase 7 – Bypassing client-side controls; • Phase 8 – Attacking authentication/login…
❤3
🖥 X-OSINT This is an OSINT tool designed to gather useful, credible, and valid information about phone numbers, email addresses, IP addresses, and more features to come in future updates. Features of this tool include: 1. Host Lookup: Identifies and retrieves information about a host (server) on the network. 2. Port Scan: Detects open ports on a host and the services associated with them. 3. Subdomain Enumerat…
❤1👍1
🚨 New Malware Tactics Bypass Traditional Defenses If your SOC can’t afford to miss a single threat, this session is for you. 📍Cases & Detection Tips for SOCs In real time, ANY.RUN experts will: — Share insights into strengthening SOC expertise — Demonstrate and break down examples of evasion techniques — Offer detection strategies for ClickFix, phishing kits, and Living-Off-the-Land (LotL) attacks 👥 Who should…
❤2
macOS Red Teaming. • Gathering System Information Using IOPlatformExpertDevice; • Targeting Browser and Diagnostic Logs; • Manipulating the TCC Database Using PackageKit; • Leveraging Application Bundles and User-Specific Data; • Taking Over Electron App TCC Permissions with electroniz3r; • Exploiting Keychain Access; • Signing Your Payload; • Exploiting Installer Packages; • Exploiting DMG Files for Distri…
❤1
CVE-2024-22116: RCE in Zabbix, 9.9 rating 🔥 Lack of escaping for script parameters allows an attacker to execute arbitrary code. Dork: http.favicon.hash_sha256:22b06a141c425c92951056805f46691c4cd8e7547ed90b8836a282950d4b4be2 Vendor's advisory: https://support.zabbix.com/browse/ZBX-25016 #zabbix
❤2
https://www.researchgate.net/profile/Subodh-Tiwari-4/stats/report/weekly/2025-04-20
🔍 Linux Persistence Detection Engineering – Part 1 The first part of the Linux Persistence Detection Engineering series explores fundamental persistence techniques used by attackers to maintain unauthorized access to Linux systems. Understanding these mechanisms is crucial for developing effective detection and threat-hunting strategies. 🛠️ Key Persistence Techniques: 1️⃣ Cron Job & Systemd Service Abuse – Attackers …
👍1
🔥 Malware Datasets for Research & Analysis 🔥 📌 If you're looking for malware datasets for digital forensics, cybersecurity research, and machine learning, check out these resources: 1️⃣ BODMAS: Blue Hexagon Open Dataset 🔗 BODMAS Dataset 2️⃣ Malware Detection Dataset (Kaggle) 🔗 Malware Detection | Kaggle 3️⃣ Malware Memory Analysis Dataset (CIC) 🔗 Malware Memory Analysis Dataset 4️⃣ DikeDataset: Labeled Malicious & Be…
😈 Reverse Engineer's Toolkit A collection of tools for those interested in reverse engineering and malware analysis on x86 & x64 Windows systems. 🔗 GitHub: Reverse Engineer's Toolkit 📢 Join the Team: https://t.me/DigitalForensicAndCyberSecurity #Reverse #Malware #CyberSecurity
❤1
👁 Uncle Spfus – Spoof MAC Address & Hostname A tool that automates MAC address and hostname spoofing. It uses macchanger but also verifies whether the MAC address remains spoofed after connecting to a network. This is crucial because some network configurations might reset the MAC to its original state. 🔗 GitHub: Uncle Spfus #CyberSecurity #MACSpoofing #Privacy
🛡 Ultimate Recon & Threat Intelligence Toolkit 🛡 🔍 Enhance your Threat Intelligence, OSINT, and Pentesting with these powerful tools! 🚨 Threat Intelligence: 🔹 BinaryEdge | GreyNoise | FOFA 🔹 Zoomeye | LeakIX | URLScan 🔹 SOCRadar | Pulsedive 💻 Code Search: 🔹 Grep.app | SearchCode | PublicWWW 🌍 Server Search: 🔹 Shodan | Onyphe | Censys | IVRE 🔎 Attack Surface Discovery: 🔹 Netlas | FullHunt | BinaryEdge 🕵️ OSINT & Recon…
Showing the 12 most recent of 20 posts we hold for @DigitalForensicAndCyberSecurity. 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.
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.
@pycode_hubb · 36,933
Telegram ranks this channel #28 of 58 here — alongside 57 others — read 1 September 2026
@Jobs_Internship_Campus_Placement · 31,748
Telegram ranks this channel #73 of 81 here — alongside 80 others — read 5 September 2026
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.
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 18 September 2026 — this entry's latest reading, not the date you are reading this.
“Cybersecurity and Digital Forensics” (@DigitalForensicAndCyberSecurity), 912 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/DigitalForensicAndCyberSecurity.
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.