angr is a platform-agnostic binary analysis framework developed by the Shellphish research group at UC Santa Barbara. It provides a comprehensive suite of program analysis capabilities including disassembly and IR lifting (via VEX), control-flow graph construction (CFG fast/emulated), symbolic execution, data-dependency analysis, value-set analysis, and a full decompiler pipeline that produces C-like pseudocode. The framework supports multiple architectures (x86, ARM, MIPS, PowerPC, etc.) and operating systems (Linux, Windows, CGC, FreeBSD, Java VM, UEFI) through a modular SimOS layer and extensive library procedure models (libc, POSIX, Linux kernel, Windows API, C++ stdlib, Java/JNI). The architecture is built around a mixin-based engine composition pattern, where execution engines (VEX, AIL, Soot, Pcode, unicorn) are combined into an UberEngine. Analyses are registered via an AnalysesHub and instantiated as project.analyses.Name. A knowledge-base plugin system stores functions, variables, types, cross-references, and other analysis results. The decompiler is particularly sophisticated, with dozens of optimization passes, peephole optimizations, region simplifiers, and multiple structuring algorithms (SAILR, Phoenix, Dream). Native Rust components (clarirs-core, clarirs-vsa, clarirs-z3) provide high-performance symbolic reasoning and solver integration. Key entry points include angr.Project for loading binaries, project.factory for block/engine creation, project.analyses for running analyses, and simulation_manager for symbolic execution. The framework also includes an MCP server for LLM integration, a distributed analysis framework, and database persistence via AngrDB. External dependencies include claripy (solver abstraction), cle (binary loader), pyvex (VEX IR), capstone/keystone (disassembly/assembly), unicorn (concrete execution), and Z3 (SMT solver).
DeepZero is an automated vulnerability research pipeline engine, primarily targeting Windows kernel driver (BYOVD) analysis. It provides a highly concurrent, resumable orchestration framework where pipelines are defined declaratively in YAML. The engine supports stages: ingest, map (parallel analysis), reduce (aggregation), and bulk_map, with built-in fault tolerance, atomic state persistence, and live progress dashboards. It integrates external tools like Ghidra (headless decompilation), Semgrep (static analysis), LIEF (PE parsing), and LLM backends (LiteLLM, Claude Code) for exploitability assessment. The repository includes a full reference pipeline (loldrivers) and multiple language documentation sites. While primarily an application framework, it has strong data research characteristics due to its focus on binary corpus analysis and heuristic/AI assessment.
This repository is a curated list of resources for embedded systems vulnerability research, not a software application or data pipeline. It aggregates books, blog posts, conference talks, papers, tools, and other references organized by category (e.g., Books, YouTube channels, Blogs, Papers, Tools). The primary content is a single README.md file containing over 200 links and brief descriptions. Additionally, the papers/ directory holds a few PDF files of research papers and a secondary README listing paper titles with links. The repo serves as a knowledge base for anyone interested in IoT/embedded security, providing a starting point for learning and staying current. There is no executable code, no data transformation logic, and no analytical models. The value lies in the curation and organization of external resources, not in any original software or data processing.
LLEf (LLDB Enhanced) is a Python-based LLDB plugin for low-level reverse engineering, vulnerability research, and exploit development. It provides enhanced debugger context (registers, stack, code, threads, trace) via an automatic stop hook and adds specialized commands: context, hexdump, pattern (cyclic pattern generation/search), checksec (ELF security mitigations), xinfo (memory mappings), dereference (pointer chain dereferencing), and scan (heap/memory scanning). It supports multiple architectures (x86_64, ARM, AArch64, i386, PowerPC) and includes experimental Go binary analysis: parsing the PCLNTAB and ModuleData structures to recover function names, types, and stack frames from stripped Go executables (versions 1.7–1.24). The plugin persists configuration via JSON files in the user's home directory. The codebase is structured with clear base classes for commands, settings, and architecture definitions, though it lacks automated tests and relies heavily on the LLDB Python API.
This repository is a comprehensive collection of vulnerability proof-of-concept (PoC) exploits and detailed write-ups, primarily targeting Chinese enterprise software (OA, ERP, CMS, network devices, etc.). It contains over 1000 vulnerability entries organized by year and CVE identifier, each with structured risk assessments, HTTP request/response PoCs, FOFA search queries for asset discovery, and custom Snort/Suricata detection rules. The repository also includes executable Python scripts and YAML nuclei templates for automated exploitation and detection. While the majority of content is documentation, the scripts provide reusable scanning patterns with threading, proxy support, and output saving. The code quality is functional but basic, with many single-file scripts. The repository is actively maintained and serves as a practical reference for penetration testers and security researchers, though some CVE numbers appear speculative and PoCs may rely on future-dated vulnerabilities.