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Reproducing the GhostMap APK crash loop on Android 16/17, plus an offline check for the ZIP parser mismatch
https://ift.tt/p8kvKCQ

Submitted November 7, 2026 at 02:04PM by ImL1s
via reddit https://ift.tt/en90IwY
I spent countless nights digging into PHP2Shell. Here's a deep dive into how it actually works, from memory to PHP internals to a real exploit.
https://ift.tt/EOmSJL4

Submitted November 8, 2026 at 06:30AM by djad04
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email encryption
https://ift.tt/pZ1Kh0G

Submitted November 8, 2026 at 09:30PM by fagnerbrack
via reddit https://ift.tt/iCqpf4T
Behavioral LOLBins detection engine with sequential correlation, PE masquerading checks, and Sysmon integration
I built a behavioral detection engine focused on Living-off-the-Land binaries on Windows and wanted to share it with the community for feedback.Problem it tries to solve Traditional signature/hash-based detection struggles with LOLBins because they are legitimate, signed Microsoft binaries. Attackers also rename them, obfuscate command lines, and chain multiple utilities across time. Single-event rules often miss the broader operational context.What the tool doesCollects process telemetry (prefers Sysmon Event ID 1, falls back to psutil)Single-event behavioral rules for common LOLBins (certutil, bitsadmin, mshta, regsvr32, rundll32, powershell, wmic, etc.)Sequential multi-stage correlation (download → execute, LOLBin → encoded PowerShell, process cascades)Binary masquerading detection via PE OriginalFileNamePPID spoofing anomaly checksCommand-line obfuscation heuristics (carets, backticks, Base64 blocks, environment variable slicing)False-positive suppression + de-duplicationTimeline reconstruction (±5–10 min window) + process lineageLocal web dashboard for triageAutomated Markdown incident reports + webhook support (Slack/Discord/SIEM)Architecture highlightsModular pipeline: collector → correlation state machine → detection engine → timeline reconstructor → dispatcherRules can be written in YAML (declarative) or pure PythonBuilt-in threat emulation test harness (11 scenarios currently)Current limitationspsutil mode has polling latency (can miss very short-lived processes)Still user-mode only (no kernel/ETW consumer yet)Needs more real-world tuning of suppression rulesGitHub: https://github.com/nimesh895/Behavioral-Living-off-the-Land-LOLBins-Detection-SystemWould love feedback on:Rule coverage / false positive rates you’ve seen with similar approachesMissing high-value sequential chainsWhether the dashboard and timeline reconstruction are useful in practiceAny other LOLBins or techniques I should prioritizeHappy to answer technical questions or discuss design decisions.

Submitted November 9, 2026 at 03:38PM by nimesh404
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Cracking Server 2022 NTLMv1-SSP Hashes with Rainbow Tables
https://ift.tt/3wrES59

Submitted November 11, 2026 at 01:23AM by elohhs
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