GitHub Trends
Project Radar
- Blue team toolsublime-security/sublime-rulesYAML · 368 stars
- Dual-use project0xDanielLopez/TweetFeedRepo · 671 stars
- Dual-use projectphishdestroy/destroylistHTML · 1,662 stars
Research DeskLatest update July 19, 202659 research entries
Independent Research Desk
Phishing Tradecraft · Infrastructure · Detection Engineering
PhishPond researches how modern phishing operations are built, run, and detected — campaign evolution, adversary infrastructure, phishing kits, OAuth and device-code abuse, AiTM frameworks, and the detection and validation workflows that catch them.
Recurring Intel
Attack-Side Tradecraft
Campaign tradecraft, lure mechanics, adversary infrastructure, identity pressure, and operator workflows worth modeling.
12 attack-side readsDetection Engineering
Detection engineering, telemetry analysis, reporting workflows, and validation that security teams can operationalize.
32 detection readsAPT Tradecraft
Emerging procedures, tooling, initial-access patterns, and cross-team tradecraft from real-world actor reporting.
15 tradecraft readsGitHub Trends
New Today
Lead Research
The authentication failures look fragmented by app, but the campaign becomes visible when defenders pivot on missing app names, error semantics, source behavior, and tenant-wide volume.
Two large Entra ID campaigns used hundreds of thousands to millions of fictional OAuth client IDs to spread account enumeration across apparent applications.
Live Collection
Fake verification steps train users into running attacker-provided instructions.
Read more:The Hacker News
A successful click delivers persistent remote access, not just credentials.
Read more:The Hacker News
Trusted suppliers and developer channels can carry phishing risk past normal filters.
Read more:BleepingComputer
Fake verification steps train users into running attacker-provided instructions.
Read more:BleepingComputer
Fake verification steps train users into running attacker-provided instructions.
Read more:The Hacker News
Sublime rules for email attack detection, prevention, and threat hunting. Primary language: YAML. 368 stars.
Open project:GitHub
#email-security#phishing#threat-hunting
TweetFeed collects Indicators of Compromise (IOCs) shared by the infosec community at Twitter. Here you will find malicious URLs, domains, IPs, and SHA256/MD5 hashes. 671 stars.
Open project:GitHub
#blueteam#malware#malware-detection#malware-research
Real-time phishing & scam domain blocklist — 200k+ curated threats, 888K+ community, free API, multiple formats Primary language: HTML. 1,662 stars.
Open project:GitHub
#anti-phishing#blacklist#blocklist#crypto-scam
🐟 PhishTank Blocklist for Pi-hole Primary language: Shell. 12 stars.
Open project:GitHub
#blocklist#hosts#phishing#pihole
Aggregation of lists of malicious domains (phishing) that can be integrated into FortiGate firewalls and other products. Primary language: DIGITAL Command Language. 110 stars.
Open project:GitHub
#blocklist#blocklists#domains-blacklist#domains-list
Coverage Map
Specific campaigns, actor activity, and the lures behind them.
How techniques work end-to-end — walkthroughs and operator workflows.
Adversary infrastructure: kits, AiTM, redirectors, and sending abuse.
Detection engineering, telemetry, validation, and response.
Longer research notes, measurement, and periodic briefs.
Search Tool
Search titles, authors, tags, and body text across the PhishPond research archive.
Showing 1 matching entry.Clear search
Field Analysis
Scammers abusing a real Microsoft account-alert sender are part of a wider pattern: attackers are turning legitimate SaaS notification workflows into authenticated phishing infrastructure.
Read more:TechCrunchAbnormal AI