Channel
@cloud_sec
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
2,256subscribers
+4 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001246240066 |
|---|---|
| Type | Channel |
| Username | @cloud_sec |
| Created | 12 November 2019 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 12 August 2026 |
| On Telegram | t.me/cloud_sec |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Aug 2026, 16:55 | 2,256 | +4 |
| 6 Aug 2026, 18:56 | 2,252 | no change |
| 6 Aug 2026, 13:17 | 2,252 | first reading |
15 posts held, back to 20 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 2 pagesof 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 7 August 2026 |
|---|---|
| Posts held | 15 (20 July 2026 – 7 August 2026) |
| Views total | 3,963 |
| Reactions total | 51 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 16:45 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.
51 reactions across 15 posts, in 3 distinct kinds. The most used accounts for 35.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 18 | 35.3% | |
| 🔥 | 18 | 35.3% | |
| 👍 | 15 | 29.4% |
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 15 of the 15 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 51reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 15 most recent posts we hold, published 20 July 2026 to 7 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.
🤖 AI Worming through Word A coordinated disclosure with MSRC demonstrating a document-borne AI worm in Microsoft Copilot for Word: hidden XPIA prompts in source documents cause Copilot to alter generated content and self-propagate the malicious instructions into downstream documents. https://enklypesalt.com/posts/context-collapse-part3-ai-worming-through-word #AI
❤1👍1🔥1
Signed Артем Марков
🤖 A Security Analysis of Amazon S3 Vectors and Its Use in LLM Retrieval Pipelines An analysis of the security model of Amazon S3 Vectors and of the considerations that arise when it is used as the retrieval layer for LLM applications: access control scope, input validation, metadata integrity, and audit coverage. https://www.offensai.com/blog/amazon-s3-vectors-security-llm-rag-poisoning #AI
❤1👍1🔥1
Signed Артем Марков
🤖 Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident A companion technical writeup to HunggingFace's incident disclosure from last week. This post walks through how the intrusion actually worked: the two initial-access vectors, how the agent pivoted and moved laterally, representative examples of the commands that were run and how they investigated with GLM 5.2. https://huggin…
🔥2❤1👍1
Signed Артем Марков
🤖 Least privilege for AI agents: Identity, access, and tool binding AI agents acting as autonomous multi-system actors require dedicated managed identities, least-privilege task-scoped RBAC, explicit tool allowlists, JIT time-limited entitlements, downstream re-authorization per call, and end-to-end audit logs capturing identity, role, scope, and correlation IDs. https://www.microsoft.com/en-us/security/blog/2026/0…
❤1👍1🔥1
Signed Артем Марков
🤖 Inside the OpenClaw Ecosystem: What Happens When AI Agents Get Credentials to Everything Permiso researchers deployed an AI agent (Rufio) into the OpenClaw ecosystem and found active malware campaigns in its unvetted skill marketplace (ClawHub), credential-harvesting skills with 377+ downloads, C2 infrastructure, and prompt injection attacks targeting agents holding plaintext credentials to email, Slack, and file …
🔥2❤1👍1
Signed Артем Марков
🤖 New Study Identifies 53 Slopsquatting Targets Across 5 Frontier LLMs A study of ~200,000 LLM responses found 5 frontier models (Claude, GPT, Gemini, DeepSeek) hallucinate nonexistent package names at 4.62-6.10%, with 53 shared fictitious names on PyPI/npm still registrable and exploitable via slopsquatting attacks. https://socket.dev/blog/slopsquatting-targets-across-frontier-llms #AI
❤1👍1🔥1
Signed Артем Марков
🔶 Introducing the Amazon GuardDuty investigation agent: on-demand AI-powered threat assessment Amazon GuardDuty investigation agent (public preview) uses AI to auto-investigate GuardDuty security findings, reducing investigation time from hours to minutes. It returns risk levels, confidence scores, MITRE ATT&CK mappings, and remediation steps via console, CLI, API, or AWS MCP server. https://aws.amazon.com/ru/blogs…
❤1👍1🔥1
Signed Артем Марков
🤖 Delegated authority, running locally: Give an agent on your machine an identity you can trust A reference architecture for giving a locally-running AI agent a trustworthy, auditable identity, without long-lived credentials on disk, including a structural defense against prompt injection built into the protocol layer. https://1password.com/blog/ai-agent-identity-delegated-local #AI
🔥2❤1👍1
Signed Артем Марков
🤖 CISO's guide to agentic AI Anthropic's Deputy CISO shares a four-question framework for assessing agentic AI risk, and walks through controls that keep agent deployments bounded and auditable. https://claude.com/blog/ciso-guide-to-agentic-ai #AI
❤1👍1🔥1
Signed Артем Марков
🔶 Introducing Claude apps gateway for AWS Amazon announced the Claude apps gateway for AWS, a self-hosted control plane that gives organizations a single point of control over access, cost, and policy for Claude Code and Claude Desktop. https://aws.amazon.com/ru/blogs/machine-learning/introducing-claude-apps-gateway-for-aws #aws
❤1👍1🔥1
Signed Артем Марков
🔶🔷🔴 The Two Mitigations for the Service-Account Confused Deputy in the Cloud Two mitigations exist for cloud service-account confused deputy attacks: for customer-managed identities, an attachment gate (GCP actAs, AWS iam:PassRole, Azure assign/action) controls bind-time authorization; for provider-managed identities, the CSP enforces internal checks, with AWS uniquely exposing this via Forward Access Sessions and c…
❤1👍1🔥1
Signed Артем Марков
🔶 OIDC tokens can now restrict which AWS roles they assume AWS STS now supports a new OIDC claim that restricts which role ARNs a token can assume. Enforced before trust policy evaluation. The boolean condition key sts:RoleAuthorizedByIdp enables mandatory enforcement via trust policies or RCPs. https://awsteele.com/blog/2026/07/13/oidc-tokens-can-restrict-which-aws-roles-they-assume.html #aws
❤2👍1🔥1
Signed Артем Марков
Showing the 12 most recent of 15 posts we hold for @cloud_sec. 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.
Citation-graph rank — 797,181 of 1,169,250entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
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.
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.
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 12 August 2026 — this entry's latest reading, not the date you are reading this.
“CloudSec Wine” (@cloud_sec), 2,256 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/cloud_sec.
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.