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Free dashboard that follows recently published, significant CVEs with some context on real-world impact. No account needed. Sharing in case it helps others here.

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submitted 2 weeks ago by london443@lemmy.world to c/netsec@lemmy.world

thekeys looks for exposed secrets such as AWS, Stripe, GitHub and OpenAI keys in public GitHub repositories. You can scan a whole repo or search by keyword.

Repo scans need no GitHub token. Only keyword search does.

It is strictly defensive. It never tests whether a key is valid and findings are masked. Single Python file. Feedback welcome.

[-] london443@lemmy.world 1 points 2 weeks ago

Since your records are DNS-only, these requests hit your origin directly, so you can deal with them there no matter who is behind the Worker. A Lemmy host has no WordPress, so anything asking for /wp-login.php, /wp-content/ or /xmlrpc.php is junk. Two cheap fixes: have your reverse proxy reject those paths outright, or use CrowdSec with its WordPress/http-probing scenarios, which bans the source after a few hits.

[-] london443@lemmy.world 2 points 2 weeks ago

Adding the security basics, since nobody listed them yet:

  1. A firewall with default deny, then open only what you need. A simple one such as ufw is fine.
  2. Install CrowdSec or fail2ban early. Within a day you will see how much junk hits any public server.
  3. Reach SSH and admin pages over a private network (Tailscale or WireGuard) instead of exposing them.
  4. Read your reverse proxy access log for a week. It teaches you more about how the internet really behaves than most courses.

None of it has to be perfect on day one, but it is much easier to build in from the start.

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Directive4 is a public feed where two AI agents continuously debate real vulnerabilities: Agent Red analyzes attack vectors from CVEs pulled from the CISA KEV catalog, Agent Blue proposes defenses. New threads open automatically, 8 turns each (about 30% are general cybersecurity topics instead of a specific CVE, to keep it varied).

Content is AI-generated for educational purposes and may contain analysis errors, but it's grounded in real public CVE data, not invented details. Free to read, no account needed, available in French and English.

[-] london443@lemmy.world 2 points 3 weeks ago

if this ends in an actual kessler event please leave starleak's uptime out of the incident report

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I built TIMING as a transparent way to look at public attention trends. For any Wikipedia topic, it pulls the last 90 days of daily pageviews from the Wikimedia Analytics API, works on log(1+views), computes a 7-day rolling average and a linear regression, and flags rupture points using a z-score at 2σ — after subtracting the expected false-positive rate from pure noise (~4.6% at that threshold). It outputs two normalized scenarios (continuity vs rupture), and every sentence in the output is a template filled from the actual computed numbers — no LLM involved.

Could be useful for OSINT/monitoring work tracking public attention around a topic, though worth being clear: this models attention, not real-world events. Free, no account: https://timing.london.li/

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submitted 1 month ago by london443@lemmy.world to c/space@lemmy.ml

Hi, I made StarLeak because I wanted to know which Starlink satellites are actually overhead where I live. It pulls orbital data from CelesTrak and computes positions client-side in the browser (elevation/azimuth/range), estimates Doppler shift, shows a map with sunlit/eclipsed status, has a naked-eye visibility filter with optional notifications, and can export the next visible pass as an .ics calendar file. Free, no account needed: https://starleak.london.li/

Known limitation: accuracy depends on how fresh the orbital elements are (shown in the page header), so treat predictions as approximate, especially for objects re-entering or maneuvering.

london443

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