Channel
@GhostlySolutions
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
54subscribers
-2 since we began measuring on 8 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1003187740838 |
|---|---|
| Type | Channel |
| Username | @GhostlySolutions |
| Description | 🛡️ Traffic Quality, Security & Conversion Platform ghostlysolutions.com |
| First recorded | 11 September 2026 |
| Last confirmed live | 11 September 2026 |
| Measurements held | 4 |
| On Telegram | t.me/GhostlySolutions |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 11 Sept 2026, 11:23 | 54 | no change |
| 9 Sept 2026, 09:16 | 54 | -1 |
| 17 Aug 2026, 17:08 | 55 | -1 |
| 8 Aug 2026, 22:46 | 56 | first reading |
20 posts held, back to 18 August 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.
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 11 September 2026 |
|---|---|
| Posts held | 20 (18 August 2026 – 11 September 2026) |
| Views total | 258 |
| Reactions total | 47 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 11 Sept 2026, 11:23 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.
Lifetime counters from Telegram’s own channel header, read 11 September 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
47 reactions across 19 posts, in 3 distinct kinds. The most used accounts for 78.7% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 37 | 78.7% | |
| ❤🔥 | 7 | 14.9% | |
| 💯 | 3 | 6.38% |
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 19 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 47 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 18 August 2026 to 11 September 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.
☣️ Hijacked Process Execution: Tracking DLL Side-Loading Adversaries execute covert payloads by placing malicious libraries alongside signed system utilities. When launched, the trusted binary automatically loads the malicious DLL, masking execution behind signed processes. Execution Risks: • EDR Evasion: Attackers execute under signed process signatures to bypass basic file reputation checks • Process Concealment:…
❤1
🔐 Identity Provider Exposure: OAuth Authorization Risks Threat actors exploit enterprise accounts by tricking users into authorizing malicious OAuth applications. Delegated tokens allow long-term access to corporate communications and cloud storage without triggering login alerts. Threat Dynamics: • Delegated Scope Persistence: Malicious apps maintain API access without ongoing password re-entry • Silent Workspace …
❤1
🤖 Unsanctioned Model Ingestion: Inspecting Outbound Enterprise Prompts Unsanctioned "Shadow AI" usage exposes proprietary code, API tokens, and customer data to external LLM providers. Employees submitting sensitive inputs to unvetted models create major compliance and data leakage risks. Risk Factors: • Unmonitored Payload Transmission: Outbound API calls transmit internal IP and credentials directly to third-part…
❤1❤🔥1
☁️ SaaS Portal Exposure: Visibility Across Public Web Interfaces Misconfigured role permissions in enterprise SaaS portals and low-code interfaces can expose backend records to unauthenticated callers. Threat actors scan public endpoints to enumerate data without triggering traditional network alerts. Exposure Factors: • Permissive Role Configurations: Misconfigured rules allow unauthorized queries to internal tabl…
❤🔥1
🌐 Perimeter Appliance Exposure: Post-Ingress Tracking Edge appliances like VPNs and firewalls are prime entry targets. When unpatched firmware exposes an edge device, adversaries gain network access without triggering endpoint agent checks. Post-Infiltration Dynamics: • Unmonitored Edge Firmware: Specialized hardware firmware runs outside standard endpoint agent coverage • Internal Reconnaissance: Compromised edge …
❤1❤🔥1
🔐 Passwordless Onboarding Exposure: Client Context Validation Passkey protocols eliminate password harvesting, but attackers target client-side onboarding and device trust workflows. Exploiting local application variables or using stolen session tokens from unverified browsers allows user impersonation. Session Exposure Factors: • Workspace Identity Drift: Stolen trust indicators from unverified client environments…
❤1❤🔥1
🤖 Enterprise AI Integration: Mitigating Indirect Prompt Injection Risks Integrating LLMs into enterprise workflows creates distinct security boundary risks. Threat actors use indirect prompt injection in external documents, support tickets, or emails to manipulate AI outputs. Key Risks: • External Input Handling: Embedded instructions in ingested files alter response logic • Context Boundaries: Untrusted data mixed…
❤2❤🔥1
🚀 Join the Ghostly Solutions Internship Program! Ready to launch your career in cybersecurity and software engineering? We are expanding our team and opening applications for our Internship Program! Open Tracks: • Cybersecurity & Threat Intelligence • Software Engineering • Technical Marketing & Content Strategy • Operations & Business Development What You Get: • Real-world experience with GhostSentinel and GhostC…
❤🔥2
🎙️ Synthetic Voice Threats: Risks in Approval Workflows High-fidelity voice cloning has elevated vishing and financial approval fraud. Threat actors simulate executive audio to request out-of-band credential resets or authorization overrides. Relying solely on voice recognition creates structural exposure. Vulnerability Factors: • High-Fidelity Voice Cloning: Generative models require minimal source samples • Out-o…
💯3❤2
☁️ Cloud Portal Governance: Telemetry Across Administrative Consoles As organizations scale cloud platforms, administrative web consoles become high-priority target vectors, often compromised via stale sessions or stolen credentials. Centralizing session telemetry supports visibility across cloud management interfaces. 📋 Operational Exposure in Administrative Portals: • Unmonitored Access Paths: Stale admin cons…
❤3
☣️ Living-off-the-Land: Tracking Native OS Tool Abuse Adversaries frequently use built-in utilities like PowerShell and WMI to blend into standard system workflows and evade signature-based detection. 📋 Detection Challenges: • Signature Evasion: Trusted OS binaries bypass traditional file alerts. • Operational Ambiguity: Malicious calls mirror valid admin commands. 🛡️ How GhostSentinel Detects LOLBins: • Execution…
❤3
🧩 Client-Side Application Integrity: Protecting Workspace Tokens Compromised third-party scripts loaded into single-page applications can inspect DOM structures to silently extract session tokens and storage state. 📋 Key Execution Risks: • Unverified Script Access: Rogue JS libraries target session storage and app state. • Silent Exfiltration: Extracted tokens are stolen without disrupting user experience. 🛡️ How …
❤2
Showing the 12 most recent of 20 posts we hold for @GhostlySolutions. 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.
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.
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 11 September 2026 — this entry's latest reading, not the date you are reading this.
“Ghostly Solutions” (@GhostlySolutions), 54 subscribers as measured 11 September 2026. Telegram Register, tgregister.com/channel/GhostlySolutions.
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.