#tool

a inject ( #mould / #Mold ) #tool is over 50 times more expensive then #3dprinting and a 3dprinter can make anything you can dream up of within #enginering limit s but tooling like molds can only do one shape #savemoney

Etwas, für das ich ein #Abo brauche, ist kein #Tool, sondern eine #Melkmaschine.

---------------- 🛠️ Tool =================== VulHunt Community Edition is an open-source vulnerability hunting framework developed by Binarly's Research team. It is designed to help security researchers identify vulnerabilities in software binaries and UEFI firmware. Built on top of Binarly's Binary Analysis and Inspection System (BIAS), the tool provides a flexible environment for analyzing binaries. It integrates with the Binarly Transparency Platform (BTP) for large-scale vulnerability management, hunting, and triage capabilities. Key Features • Open Source Engine: The Community Edition provides the core VulHunt engine for free, facilitating community-developed rulepacks and integrations. • Multiple Loaders: Supports scanning single binary files (component), BA2 archives (ba2), and Binary Ninja databases (bndb). • MCP Server Mode: Can run as a Model Context Protocol (MCP) server for integration with AI assistants. By default, it starts a streaming HTTP server with SSE transport at http://127.0.0.1:8080 • Output Formats: Supports standard JSON output, human-readable formatting (--pretty), streaming JSONL messages (--stream), and Zstandard compression (--compress). Technical Implementation The framework is built in Rust and can be compiled using cargo-make. It requires a patched version of LuaJIT for static building. On Windows, it uses msvcbuild.bat to compile LuaJIT. The tool accepts directories containing auxiliary data, rules, and modules via command line arguments or environment variables (BIAS_DATA, BIAS_VULHUNT_RULES, BIAS_VULHUNT_MODULES). Use Cases • Automated scanning of firmware images and software binaries for known and unknown vulnerabilities using custom rulepacks. • Integrating binary analysis capabilities directly into AI assistant workflows via the MCP server interface. • Large-scale vulnerability triage when combined with the Binarly Transparency Platform. Limitations Building the tool without cargo-make requires manual setup of a patched LuaJIT, which might introduce friction for some environments. Binary Ninja database scanning requires enabling the bndb feature at build time. 🔹 vulhunt #binarly #uefi #firmware #tool 🔗 Source: https://github.com/vulhunt-re/vulhunt

---------------- 🛠️ Tool =================== Conversation Stenography is a proof of concept tool that hides secret messages inside normal-looking chat text using local AI models. 🔹 Key Features • Generates innocent cover text for encrypted messages • Works across any messaging platform like WhatsApp or Signal • Uses local GPT-2 model for generation and decoding 🔹 Technical Implementation The tool is written in Go. It clones from GitHub, builds a binary, and downloads a recommended local AI model on first run. It encrypts the message and then uses the local model to generate a cover text that looks like a normal conversation. A local simulation mode allows testing with two users on one device. 🔹 Use Cases • Educational demonstration of LLM-based steganography • Covert communication research against message scanning 🔹 Limitations The author explicitly states this is a proof of concept with multiple issues. Techniques are already being developed to detect hidden content in text. The author notes they are 18 and not the first to explore this, acknowledging LLM-based steganography has existed for years and may already operate at scale. 🔹 steganography #llm #privacy #tool #gpt2 🔗 Source: https://github.com/nethical6/conversation-steganography

---------------- 🛠️ Tool =================== A security researcher detailed an autonomous vulnerability hunting system built around Claude Code and the Model Context Protocol (MCP). The motivation stems from the overhead of context switching and tool wrangling during manual vulnerability research. Architecture Overview The system wraps standard research tools as callable MCP servers, allowing Claude Code to execute terminal commands with typed inputs and outputs natively. The setup consists of 8 distinct MCP servers distributed across 5 VMs, exposing over 300 tools. Server Breakdown • Lab Controller: Manages SSH/WinRM sessions and Proxmox VMs. Handles basic reverse engineering. • Hunter: Dedicated to patch diffing, attack surface enumeration, fuzzing across 10 domains, and crash triage. • RE Tools: Integrates Ghidra, radare2, and Frida for static and dynamic analysis. • Exploit Dev: Automates shellcode generation, heap sprays, CFG bypasses, and PoC assembly. • Debugger: Maintains persistent WinDbg/GDB sessions that survive across tool calls. • RAG: Provides semantic search over campaign data and prior research. • Infra: Provisions and scales fuzzing VMs on Proxmox. • Reporting: Automates disclosure reports and CVE requests. Technical Implementation All 8 servers run as separate Python processes registered in a single .mcp.json file. When Claude needs to interact with a Windows target, it calls tool_surface_kernel_drivers. For decompilation, it uses tool_re_ghidra_decompile. This structured approach eliminates the need to copy-paste terminal output or switch contexts manually. Analysis By delegating tool execution to the AI, the researcher maintains focus on critical thinking and disclosure writing. While automated fuzzing is not new, integrating it directly with an LLM via MCP provides a structured pipeline from initial mapping to CVE submission. 🔹 mcp #vulnerability_hunting #claude_code #fuzzing #tool 🔗 Source: https://blog.zsec.uk/bullyingllms/

---------------- 🛠️ Tool =================== π RuView is a WiFi sensing platform that converts radio signals into spatial intelligence using Channel State Information (CSI) from ESP32 sensors. The system enables presence detection through walls, vital sign monitoring, activity recognition, and environmental mapping without cameras or cloud dependency. How It Works Every WiFi router fills a space with radio waves. When people move, breathe, or sit still, they disturb those waves in measurable ways. RuView captures these disturbances using CSI data from low-cost ESP32 sensors and converts them into actionable spatial intelligence: who is present, what they are doing, and whether they are okay. Key Capabilities The platform senses five primary categories: • Presence and occupancy: detecting people through walls, counting them, tracking entries and exits • Vital signs: breathing rate and heart rate measured contactlessly during sleep or sitting • Activity recognition: walking, sitting, gestures, and falls derived from temporal CSI patterns • Environment mapping: RF fingerprinting to identify rooms, detect moved furniture, and spot new objects • Sleep quality: overnight monitoring with sleep stage classification and apnea screening Technical Architecture The system is built on RuVector and Cognitum Seed. It runs entirely on edge hardware: an ESP32 mesh (approximately $9 per node) paired with a Cognitum Seed for persistent memory, cryptographic attestation, and AI integration. No cloud, no cameras, no internet required. Spiking neural networks learn each environment locally and adapt in under 30 seconds. Multi-frequency mesh scanning operates across 6 WiFi channels, using neighboring routers as free radar illuminators. Each node ships 21 entities: 11 raw signals plus 10 inferred semantic states including someone-sleeping, possible-distress, room-active, elderly-inactivity-anomaly, meeting-in-progress, bathroom-occupied, fall-risk-elevated, bed-exit, no-movement, and multi-room-transition. Smart Home Integration The platform integrates natively with four major ecosystems: Home Assistant via HA-DISCO MQTT publisher (single --mqtt flag), Apple Home and HomePod as a discoverable HAP-1.1 bridge, Google Home and Amazon Alexa via the same Home Assistant bridge or a Matter endpoint. Siri, Google Assistant, and Alexa can voice-report presence and vitals by room with zero custom skills. Three starter Home Assistant Blueprints are included. Considerations The edge-only architecture preserves privacy but limits remote access without additional infrastructure. The CSI approach for spatial sensing is well-established in research, and the $9 per node cost makes broad deployment feasible. Performance in dense urban RF environments with many overlapping networks is not well documented. The technique of using neighbor routers as radar illuminators depends on local RF conditions that vary between deployments. Haven't tested personally. 🔹 wifisensing #esp32 #smarthome #tool #csi 🔗 Source: https://github.com/ruvnet/ruview

---------------- 🛠️ Tool =================== open·kritt is an open-source, self-hosted security research platform that orchestrates AI agents to find vulnerabilities in code. Rather than pointing a model at an entire repository and hoping for results, it breaks research into focused, well-defined tasks, runs them in parallel across AI agents, and combines output into validated, prioritized findings. How it works The core approach is decomposition. Full-repository scans with a single LLM prompt tend to produce noisy, unfocused results. open·kritt chains focused prompts into reusable security research playbooks (workflows). Each workflow defines a sequence of targeted analysis steps. Agents run these steps in parallel, and results are merged with automatic de-duplication and custom severity ranking. Key capabilities • Workflow builder: Chain focused prompts into reusable security research playbooks • Scan execution: Analyze remote or local repositories and dependencies using Codex or Claude Code • Finding validation: Post-scripts verify issues, build proofs of concept, produce reports • Result prioritization: Custom severity rankers, consistent finding schema, automatic de-duplication • Model flexibility: Bring your own model access via Codex, OpenAI, Anthropic, or OpenRouter Technical details The stack runs on Docker with Docker Compose, requiring Node.js 20 or newer. The CLI is repository-local with no separate install step. Default ports bind to 127.0.0.1, and the backend ships without application authentication. The documentation explicitly advises keeping the stack private. Tool-enabled agents run as root inside disposable job containers, with writable repository copies and direct internet access. This allows agents to install tools, compile targets, run tests, and build proofs of concept. The threat model documentation recommends running open·kritt on a dedicated Docker host or VM, especially when scanning untrusted code. Background The Kritt team built this from real security research. Under the researcher name Blockian, they earned over $1,500,000 in bug-bounty payouts across platforms including Immunefi and HackenProof. open·kritt is the open-source version of the internal tool behind that work. Limitations No application-level authentication by default. Agents run as root in containers with internet access, requiring isolation awareness. The tool has not been independently verified for this writeup. 🔹 openkritt #tool #AI #vulnerability #bugbounty 🔗 Source: https://github.com/Kritt-ai/open-kritt

Offline Translator An Android translator app that performs text, PDF/ODT documents and image translation completely offline using on-device models. Supports automatic language detection and transliteration for non-latin scripts. There's also a built-in word dictionary. 💬 https://github.com/DavidVentura/offline-translator 📱 https://f-droid.org/packages/dev.davidv.translator #android #fdroid #translater #offline #tool #language #pdf #odt #text #opensource

Ooh, look! I made a thing! A handy little clamp/vice tool thing to help get #PTFE tubing out of couplers. #3DPrinting #tool https://www.printables.com/model/1783120-ptfe-decoupler-tool

---------------- 🛠️ Tool: garak - LLM Vulnerability Scanner =================== garak (Generative AI Red-teaming & Assessment Kit) is an open-source tool developed under NVIDIA's GitHub organization that systematically probes LLMs for security weaknesses. If you know nmap or Metasploit Framework, garak operates on a similar concept but targets language models instead of network services or software vulnerabilities. What garak does The tool probes LLMs and dialog systems for failure modes that security teams care about: hallucination, data leakage, prompt injection, misinformation generation, toxicity output, and jailbreaks. It combines three probing strategies. Static probes use fixed test cases. Dynamic probes generate test cases based on model responses. Adaptive probes adjust their strategy based on intermediate results, which is potentially more effective at uncovering weaknesses that fixed test suites miss because the probing strategy evolves as it learns about the model's behavior. Supported backends • Hugging Face Hub generative models • Replicate text models • OpenAI API (chat and continuation models) • AWS Bedrock foundation models • LiteLLM • REST-accessible endpoints • GGUF models via llama.cpp (version >= 1046) This range means you can run the same probe suite across different providers and compare results directly. That comparative angle is useful for organizations evaluating which model to deploy. Installation Standard install via pip: python -m pip install -U garak Development version from GitHub: python -m pip install -U git+https://github.com/NVIDIA/garak.git@main Recommended Conda environment setup with Python >=3.10, <=3.12. The tool runs as a command-line utility with the general syntax garak . Technical context The project has active CI pipelines for Linux, Windows, and macOS. Code formatting follows Black. An arXiv paper (2406.11036) documents the methodology. DEF CON presentation slides are available. The Discord community is active for discussion. Practical considerations The tool is free under Apache 2.0. It focuses on making LLMs fail in ways we don't want, which is a different posture than typical benchmarking. The adaptive probe mechanism is conceptually interesting. I'm not sure how it performs in practice against commercially deployed models with layered safety filters. Haven't tested personally, so can't speak to performance at scale or coverage completeness against specific model families. Documentation at docs.garak.ai. 🔹 garak #LLM #red_teaming #NVIDIA #tool 🔗 Source: https://github.com/NVIDIA/garak

Earliest #Evidence of #Ivory #Tool Production Uncovered in #Ukrainian Site : Medium #Neutrino’s #Nursery found: the ‘#Shadow #Blaster’ : Nature #Bacteria share #Proteins to survive #Antibiotics : Science Latest #KnowledgeLinks https://knowledgezone.co.in/resources/bookmarks

AskTodo: la #Suite di scrittura AI con oltre 35 #Tool gratuiti per la tua #produttivita #intelligenzaArtificiale @tecnologia https://webappsmagazine.blogspot.com/2026/07/asktodo-la-suite-di-scrittura-ai-con.html

I'm praying for rain I'm praying for tidal waves I wanna see the ground give way I wanna watch it all go down Mom, please flush it all away I wanna see it go right in and down I wanna watch it go right in Watch you flush it all away Yeah, time to bring it down again Yeah, don't just call a pessimist Try and read between the lines And I can't imagine why you wouldn't Welcome any change, my friend https://youtu.be/CehYA3omb5o #Tool #Adapt #Fitness #Change

---------------- 🛠️ Clankerusecase — Threat-led Detection Library =================== Clankerusecase is a threat-led detection library providing detection rules across four platforms: Microsoft Defender KQL, Azure Sentinel KQL, Sigma, and Splunk SPL. The core value is reducing latency between threat intelligence publication and deployable detection content. 🔹 Two Content Tiers Generic use cases are rule files stored in use_cases/*.yml that activate when an article mentions a known trigger keyword. For example, an article referencing psexec fires the rule UC_LATERAL_PSXEC. These are broadly applicable but lack specificity to individual campaigns. They provide baseline coverage for well-known techniques and tools. AI-badged use cases represent a higher-fidelity tier. The pipeline feeds the source article to Claude, which generates bespoke detection logic targeting the exact campaign, threat actor, or malware family described. These rules are pinned to the specific IOCs and TTPs mentioned in the article. AI-badged use cases sort to the top of article cards and the matrix drawer to surface the highest-quality content first. 🔹 Cross-Verification Process AI-generated detections undergo a cross-verification step via web search against authoritative vendor advisories: Microsoft Threat Intelligence, Mandiant, CrowdStrike, MITRE ATT&CK, and abuse.ch. Each AI-badged rule includes "Cross-checked against:" references linking back to these verification sources. This adds a validation layer that pure LLM-generated detection rules typically lack. 🔹 Platform Coverage and Filtering Detection rules target four platforms, each with its own query language. The interface provides filter groups organized by Source, Content, Platform, Target, and Splunk category. On mobile viewports, the filter toolbar collapses behind a "Filters ▾" toggle to keep article cards above the fold. 🔹 Practical Considerations For detection engineers, the AI-badged use cases offer campaign-specific hunting logic without starting from scratch. The cross-check against vendor advisories provides some confidence, though this does not replace manual validation in production. The generic rules provide baseline coverage for known patterns, while AI rules address the gap for novel or recently reported threats. The quality of AI-generated rules depends on Claude's ability to accurately extract IOCs and TTPs from source articles. 🔹 detection_engineering #KQL #sigma #splunk #tool 🔗 Source: https://clankerusecase.com/

---------------- 🛠️ Tool =================== zsazsa CTI is a cyber threat intelligence program management and production platform built around MISP. It links collection, triage, analyst workflows, requirement management, publishing, and stakeholder delivery in a single integrated workflow. The platform targets teams that treat threat intelligence as an operational capability rather than a collection of loose documents and disconnected scripts. Analysts move from source events to validated intelligence products, align output to PIR and GIR priorities, distribute to stakeholders, and feed response back into program maturity signals. Key functional areas: Dashboard provides a live snapshot: active PIRs and GIRs, stakeholder counts, analyser freshness, the last 24 hours of processing, and scraper events awaiting triage. Stakeholders records who receives output, with role, organisation, TLP clearance, product subscriptions, and notification channels. Includes a power and interest matrix for engagement planning. Requirements (PIR and GIR) hold the intelligence questions driving collection, with scope, ownership, and distribution. Adding scope to a requirement highlights matching events in the data collection view. RFIs handle one-off requests from intake to closure, with SLA, owner, linked PIR or GIR, response confidence, attachments, notes, and feedback. Data collection is the cached view of everything arriving from the scraper MISP, other MISP servers, and manual or newsletter sources. Analysts browse and triage events, enrich them with scope from MISP galaxies, generate AI summaries, and start a product straight from a source event. Products form a searchable catalogue: Flash Intel Alerts, Vulnerability Advisories, Daily Threat Briefings, Threat Landscape Reports, Indicator Feeds, and Threat Actor Profiles. Statistics cover pipeline and program metrics, RFI and feedback figures, and a scope coverage view. A CTI-CMM maturity panel maps the program against levels CTI0 to CTI3. MISP integration All operational data resides in MISP using events, object templates, attributes, and event reports. This preserves auditability and allows teams to inspect raw records directly in MISP. The MISP event history serves as an audit trail for every change to a product, stakeholder, or requirement. Product content and supporting context sit together, so analysts move from collection evidence to published output without losing traceability. The built-in reference panel helps teams apply common intelligence concepts consistently, including the Admiralty Scale, TLP, and CTI evaluation criteria. Considerations The platform assumes you are already running or willing to adopt MISP as the backbone of your CTI stack. Teams without an existing MISP instance face additional deployment overhead. The AI summary feature in data collection is mentioned but the underlying model or service is not specified. Not independently verified. For CTI teams struggling with fragmented workflows, zsazsa provides a structured path from collection to delivery with built-in maturity measurement. The MISP-native design eliminates data silos between stages. 🔹 CTI #MISP #threat_intelligence #tool #zsazsa 🔗 Source: https://github.com/zsazsa-project/zsazsa

---------------- 🛠️ Tool =================== Orochi is an open-source framework for collaborative forensic memory dump analysis, developed by LDO-CERT and built on Volatility 3, Django, and Dask. Key Features The core value proposition is multi-analyst collaboration on memory forensics. Multiple analysts can upload, analyze, and correlate memory dumps simultaneously through a web interface, eliminating the need for local Volatility installations or manual result sharing. The architecture distributes Volatility 3 plugin execution across Dask workers, enabling parallel processing of forensic artifacts. The stack includes: • Volatility 3: Core memory forensics framework for extracting digital artifacts • Dask: Parallel computing library distributing plugin execution across workers • PostgreSQL: Stores user and analysis metadata • Redis: Message broker and cache for asynchronous communications between components • Django WSGI/ASGI: Web backend with real-time WebSocket updates for result delivery • Nginx: Reverse proxy providing HTTPS termination • Mailpit: Local SMTP service for user registration emails • Docker Compose: Orchestrates the full stack for x64 and arm64 platforms The real-time WebSocket updates via Django ASGI mean analysts see results as they complete rather than polling or refreshing. Technical Implementation When an analyst triggers a Volatility plugin against a memory dump, the task is queued through Redis and distributed to available Dask workers. Results are persisted to PostgreSQL and pushed to connected clients via WebSocket. Symbol files and Volatility plugins are managed through the admin interface or management commands. Use Cases • Incident response teams correlating memory analysis across multiple compromised endpoints • SOC workflows where analysts share findings without transferring large dump files • Multi-host forensic correlation to identify common artifacts across breached machines • Training environments for memory forensics education with shared datasets Considerations The tool requires Docker infrastructure and sufficient storage for potentially large memory dump files. The Dask architecture allows scaling workers based on analysis demand, but resource planning is needed for production deployments. Default credentials (admin/admin) should be changed before any non-lab deployment. The initial setup requires downloading Volatility plugins and symbol files. Note: haven't tested personally. 🔹 orochi #memoryforensics #volatility3 #dfir #tool 🔗 Source: https://github.com/LDO-CERT/orochi

---------------- 🛠️ Tool =================== T3MP3ST is a multi-agent offensive security framework designed to transform AI coding agents into zero-day hunters. It does not introduce its own model or require separate infrastructure. Instead, it wraps around whichever AI coding agent is already running on the host machine (Claude Code, Codex, Hermes) and orchestrates a full kill chain: recon, exploit, report. Interaction happens through a web-based War Room interface or the command line. Architecture The framework uses an 8-operator swarm architecture. Each operator covers a different phase or capability in the offensive pipeline. The recon engine is described as live and tool-backed, meaning it already integrates with external tooling for discovery and enumeration. The exploit loop has been benchmarked against a formal challenge suite. The system is self-hosted and keyless. It uses whatever agent credentials you already authenticate with. No additional API keys, no separate billing, no cloud dependency. Benchmark Results On XBOW's XBEN benchmark (104 challenges), T3MP3ST achieved 90.1% pass@1. Every solve was graded against a committed flag oracle. The npm run verify-claims command recomputes all performance numbers from committed data. The current score is 24/24 green, meaning every claim in the README can be independently verified from the repository's own data. This reproducibility mechanism is worth emphasizing. In a space where AI security tools routinely ship with unverified or selectively reported numbers, a committed verification pipeline that any user can run is a meaningful design choice. It does not prove the tool works in all scenarios, but it does make the claims auditable. On a held-out test of 10 real CVEs disclosed in 2026, spanning 7 programming languages, a single agent pinned 8 out of 10 to the exact file, line, and CWE classification. The full operator pack surfaced all 10 CVEs. The authors explicitly note the small sample size (n=10) and describe the results as "directional" rather than definitive. They also state that both memorization and overfitting are off the table, since the CVEs were disclosed after the model's training cutoff. Design Principles Three stated principles. First, reproducible: every number recomputes from committed data. Claims that cannot be reproduced do not ship. Second, keyless: no additional API keys, no gatekeeper. Third, honest about scope: a status table marks exactly what is stable, experimental, or still on the roadmap. Practical Considerations Offensive tool under AGPL-3.0, authorized use only. Not all 8 operators are fully live. The small-n CVE results are promising but preliminary, as the authors acknowledge. 🔹 T3MP3ST #tool #offensive_security #AI_agent #zero_day 🔗 Source: https://github.com/elder-plinius/T3MP3ST

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🛠️ Tool: SigmaLineage MCP

Sigma hits without context are noise. SigmaLineage MCP is a FastMCP server that wraps three capabilities into a single AI-callable interface, designed to solve the false-positive problem that plagues detection engineering.

What it does

1. Sigma Hunt (run_sigma) — runs Chainsaw against an EVTX folder with the full SigmaHQ rule set.
2. Process Lineage Tracing (run_sigma_lineage) — for every Sigma hit, automatically traces the parent→child execution tree up to 5+ generations, building a full kill-chain view.
3. Rarity Baseline Engine (rare_events_baseline) — statistically surfaces anomalous process-to-port connections, suspicious user-log event combinations, and unusual URL lookups that don't fit the baseline.

The false positive problem

regsvr32.exe spawning a child process matches 40 Sigma rules and also matches every legitimate COM registration. wmic.exe executing a command could be lateral movement or your asset management tool. cmd.exe spawned by mmc.exe looks terrifying until you realize it is normal DCOM-based remote management. The alert alone tells you nothing. The parent chain tells you everything.

How lineage tracing works

SigmaLineage uses the Rust-backed evtx Python parser to build an in-memory process graph from Sysmon Event ID 1 (process creation) and Security Event ID 4688 in your EVTX corpus. It resolves ancestry using ProcessGuid strings for Sysmon events, and uses a PID + timestamp closest-fit algorithm for Security events that lack GUIDs. The result: for every Sigma hit, you get the full execution tree rendered in markdown.

Real example from EVTX-Attack-Samples — impacket wmiexec:

[WmiPrvSE.exe (PID: 836)] └─ [cmd.exe (PID: 2828)] (HIT) cmd.exe /Q /c whoami /all 1> \127.0.0.1\ADMIN$__1556656369.7 2>&1 └─ [whoami.exe (PID: 3328)] (HIT) whoami /all

One look and you know: cmd.exe spawned by WmiPrvSE.exe, writing output to the ADMIN$ share via a UNC path. Textbook WMI exec pattern. Not a false positive.

Compare to a surface-identical alert where lineage shows [services.exe] → [PSEXESVC.exe]. Same alert, different root cause (PsExec), instantly disambiguated.

The rarity engine solves anomaly discovery rather than false positive reduction. It statistically surfaces unusual process-to-port connections, suspicious user-log event combinations, and URL lookups without needing a predefined rule.

Plug into any MCP-compatible AI client (Cursor, Claude Desktop, Antigravity, OpenCode). Describe what you want to investigate in plain English, get structured analysis back.

Note: haven't tested personally.

🔹 SigmaLineage #tool #DetectionEngineering #Sigma #MCP

🔗 Source: https://mohitdabas.in/blog/sigmalineage-mcp-evtx-hunting-lineage-first-triage/

---------------- 🛠️ Tool: Awesome Incident Response =================== The Awesome Incident Response repository is a curated collection of DFIR tools and resources organized into over 20 categories, aimed at security analysts and incident response teams. 🔹 Adversary Emulation Tools The repository lists several adversary emulation frameworks that allow blue teams to test their detection capabilities: • APTSimulator: A Windows batch script that uses a set of tools and output files to make a system appear as if it was compromised by an advanced threat actor • Atomic Red Team (ART): Red Canary's collection of small, portable detection tests mapped directly to the MITRE ATT&CK framework • Caldera: MITRE's automated adversary emulation system that performs post-compromise adversarial behavior within Windows enterprise networks, generating plans using a planning system and pre-configured adversary models based on ATT&CK • RTA (Red Team Automation): Endgame's framework of scripts designed to allow blue teams to test detection capabilities against malicious tradecraft modeled after MITRE ATT&CK • Network Flight Simulator: A lightweight utility from AlphaSOC that generates malicious network traffic to evaluate security controls and network visibility • DumpsterFire: A modular, menu-driven, cross-platform tool for building repeatable, time-delayed, distributed security events for Blue Team drills and Red Team decoy operations 🔹 Evidence Collection by Platform The repository separates evidence collection into platform-specific categories: Windows, Linux, and OSX. This separation is operationally practical because IR procedures and available tooling differ significantly between operating systems. During time-sensitive investigations, analysts can go directly to the relevant platform section without filtering through unrelated tools. 🔹 Memory Forensics Two distinct categories address memory work: • Memory Imaging Tools: For acquiring volatile memory from live systems during initial response • Memory Analysis Tools: For examining acquired memory images to extract processes, network connections, loaded modules, and other artifacts This separation mirrors the actual IR workflow where acquisition and analysis are performed at different stages, often by different team members. 🔹 Timeline and Log Analysis Timeline reconstruction is critical for understanding attack progression and scope. The repository catalogs dedicated timeline tools alongside log analysis utilities, covering both artifact correlation and raw log parsing capabilities. 🔹 Additional Resources Beyond tools, the repository catalogs books, communities, knowledge bases, purpose-built Linux distributions (including RedHunt-OS for adversary emulation and threat hunting), playbooks for structured IR procedures, and training videos. 🔹 Considerations The repository includes an automated URL check workflow, indicating some level of ongoing maintenance. However, individual tool relevance, compatibility with current operating system versions, and maintenance status should be verified before operational deployment. The "Awesome" list format does not include tool version tracking, compatibility matrices, or maturity assessments. 🔹 DFIR #incident_response #forensics #bookmark #tool 🔗 Source: https://github.com/meirwah/awesome-incident-response

---------------- 🛠️ Tool =================== arsenal-ng is a modern penetration testing command launcher written in Go, inspired by the original arsenal project from Orange-Cyberdefense but rewritten from scratch with a focus on simplicity and speed. It ships with 238 tools and 2830 commands preloaded, searchable through a terminal user interface. Purpose and Architecture Pentesters regularly work with dozens of tools, each with hundreds of flag combinations. Remembering exact syntax for nmap scan types, ffuf rate limits, or sqlmap injection parameters is impractical across engagements. The original arsenal project addressed this with searchable command cheatsheets. arsenal-ng rebuilds the concept as a single Go binary with no runtime dependencies, launching in milliseconds. The TUI accepts multi-word fuzzy search across tool names, titles, tags, descriptions, and command text. Typing nmap scan or ffuf narrows results in real time. Each tag gets a consistent, distinct color based on its hash value for quick visual identification. Key Technical Features The argument system supports {{arg}} placeholders with optional defaults ({{arg|default}}) and auto-completion. Commands become templatized for recurring workflows. Global variables extend this further. Setting set target=10.10.10.10 once propagates that value across every command using the corresponding placeholder, removing repetitive manual input. Commands write directly to the terminal input buffer rather than the clipboard. You select a command, it appears in your prompt, and you edit before executing. No window switching, no clipboard management. The YAML cheatsheet format enables straightforward extension. Teams can create methodology-specific command sets, version them with engagement documentation, and share across members. Syntax highlighting color-codes command text for readability. The built-in tools view presents all 238 tools with command counts in a paginated table. An interactive help screen accessible via ? displays all shortcuts. Platform Support Linux requires kernel 6.2+ for terminal prefill. Older distributions on kernel 5.x lose the prefill feature, though the tool otherwise functions. macOS works natively. Windows is supported only through WSL. Native CMD and PowerShell are not supported. Go 1.24.0 or higher is required. Practical Assessment For pentesters working across large toolsets, arsenal-ng provides a functional workflow improvement over shell aliases and text notes. The YAML format supports team-specific customizations. Terminal prefill avoids clipboard dependency. The kernel 6.2+ requirement for Linux prefill is a constraint in older environments. Haven't tested personally. 🔹 arsenalng #pentesting #commandlauncher #tool #redteam 🔗 Source: https://github.com/halilkirazkaya/arsenal-ng