Python remains a powerhouse for building custom toolkits, automating workflows, and chaining together analysis pipelines. This article presents a curated 2026 edition of the “Python Arsenal”: essential libraries, frameworks, and workflows you should know if you work in reverse engineering or security analysis.
Why a “Python Arsenal”?
Python’s readability, ecosystem maturity, and extensibility make it a go-to choice for security practitioners. A consolidated toolkit of scripts and modules that empower reverse engineering will help you automate tedious tasks, dissect binaries, analyze network traffic, and exploit vulnerabilities with precision.
Building your own updated Python Arsenal helps you:
- Avoid black-box tools you don’t understand
- Can integrate automation, CI/CD, and custom logic easily
- Stay nimble in response to new malware, architectures, or obfuscation techniques
What You Should Pay Attention to When Building a Python Arsenal for 2026
- Modern architectures and instruction sets: Support for ARM64, RISC-V, WASM, etc.
- Better Python bindings and bridges: Tools that expose APIs more cleanly to Python (e.g., PyGhidra)
- AI + automation synergy: Copilot-augmented scripts, auto-generation of debugging fragments
- Stricter sandboxing and safety in security tools: Less “run whatever script”
- Greater emphasis on pipeline maturity: As well as reproducibility, packaging, CI/CD
Essential Python RE Tools & Libraries for 2026
Below is a suggested minimal but powerful set of tools. You can pick & choose based on your focus (binary reverse engineering, embedded firmware, malware analysis, etc.):
| Category | Tool / Library | Why It’s Valuable | Notes |
|---|---|---|---|
| Core Disassembly / Decompilation | IDA Pro | The industry gold standard. Provides deep analysis and scripting via IDAPython | Commercial; license is costly, but essential for many environments |
| Ghidra | Open-source alternative supporting scripting via Jython and CPython bridges (PyGhidra) | Great as part of automation pipelines | |
| Binary Ninja | Clean UI, built-in Python scripting APIs | Good trade-off between capability and usability | |
| Radare2 (via r2pipe / python bindings) | Flexible open-source RE framework, scriptable via Python, supports patching, debugging, and emulation | Steep learning curve; UI is less polished; performance and documentation can be challenging | |
| Dynamic Instrumentation | Frida | Scriptable in Python; works across mobile, desktop, embedded | Useful for injecting into running processes |
| Symbol / Format Parsing | LIEF, Capstone, Keystone | For parsing, assembling, and emulating instructions | These underpin many custom RE scripts |
| Unicorn | Efficient CPU emulation for small code fragments; useful in safe sandboxed execution | Best for limited code ranges rather than full system emulation | |
| Fuzzing / Input Generation | Atheris, BOOFuzz wrappers | To fuzz binary interfaces or scripts | Especially useful for proprietary protocols |
| Glue / Helper Libraries | pwntools, python-elfutils, angr | Useful for exploits, symbolic execution, and parsing | Integrate these as utilities in your toolkit |
| Androguard | Strong for Android reverse engineering: decompiling APKs, analyzing DEX, building call graphs | Focused on Android; doesn’t replace full binary RE for native code | |
| Automation / Workflow | GitHub Copilot for GDB, CI scripts | Speed up writing boilerplate and tractable pipelines | Use with caution and review the output |
| Miasm | Python-based binary analysis, lifting, disassembly, and control-flow capabilities in RE research | Less mature and feature-complete than major tools; good for prototyping | |
| Volatility | Memory forensics and runtime analysis; helpful when malware or runtime state is involved | More for analysis of memory dumps than static reverse engineering |
IDA Pro (via IDAPython)
IDA Pro includes the IDAPython plugin, which embeds a Python interpreter into IDA and gives full access to the IDA API, letting you write scripts, automate workflows, or build custom plugins.
With IDAPython, you can rename symbols, annotate disassembly, implement custom analyses, batch-process many binaries, or even hook UI elements. Compared to IDC (IDA’s legacy scripting), Python adds modern library access (e.g., requests, networkx) and more maintainable code. However, some performance-critical tasks may still prefer C++ plugins. (You can write IDAPython plugins that act nearly like native ones.)
Ghidra (via PyGhidra / Jython)
Ghidra supports Python scripting primarily via Jython (Java-based Python) and also via PyGhidra, a CPython bridge that uses JPype to expose Ghidra APIs natively in Python.
PyGhidra enables you to run Ghidra scripts using modern Python 3, interact with Ghidra’s GUI or headless mode, and integrate with other Python tooling. Its advantages include leveraging Python’s ecosystem (e.g., pandas, numpy) while still tapping into Ghidra’s reverse engineering engine. The tradeoff is managing the Java-Python boundary and ensuring compatibility across versions.
Binary Ninja (via its Python API)
Binary Ninja offers a first-class Python API that lets you script or extend virtually every part of the tool (analysis, UI, intermediate representations) in Python.
You can write plugins or automation scripts that open a binary as a BinaryView, iterate functions, inspect low/medium/high IL, and patch code via Python.
This approach allows you to bring Python’s ecosystem (data libraries, custom logic) into a RE workflow, but you still depend on the Binary Ninja core for heavy lifting (e.g., disassembly, graph building).
Radare2 (via r2pipe / Python bindings)
You can control Radare2 from Python using r2pipe, which acts as a thin bridge: it spawns or connects to an r2 instance and sends commands, returning their results (often in JSON).
This allows full scripting of radare2 workflows (disassembly, function listing, patches) within Python, automating repetitive analysis tasks.
One limitation is that you must manage the state between Python and r2 (e.g., when to re-analyze or re-load). But r2pipe is widely used in toolchains for RE automation.
Frida (via frida-python)
Frida offers a dynamic instrumentation framework, and its frida-python bindings allow you to script instrumentation from Python: injecting JavaScript snippets, hooking functions, intercepting API calls, and tracking runtime behavior.
You can attach to a running process, inject hooks, and communicate with the instrumented target via RPC, all within Python workflows. This makes Frida ideal for runtime RE, dynamic tracing, bypassing obfuscation, and instrumenting live systems. The limitation is that Frida is inherently runtime. Static binaries or cold analysis tasks require complementary tools.
LIEF, Capstone, Keystone
These libraries form the backbone of many Python-based RE toolchains. You often see these used together (assemble → patch → disassemble → emulate) in workflows. For example, patching ELF code inline via LIEF + Keystone + Capstone is common.
- LIEF (Library to Instrument Executable Formats) allows parsing, modifying, and rebuilding executable binaries (ELF, PE, Mach-O) in Python.
- Capstone provides disassembly of binary machine code into human-readable instructions, with Python bindings.
- Keystone is the inverse of Capstone: a lightweight assembler engine that lets you emit machine code from assembly strings, usable in Python.
Unicorn
Unicorn provides CPU and architecture emulation (x86, ARM, etc.), and its Python bindings allow you to embed emulation directly into your Python scripts.
You can allocate memory, set registers, run machine code with uc.emu_start(), and trap interrupts or exceptions. This is useful for testing small code snippets, validating patches, or analyzing shellcode paths without needing a full VM.
Because it emulates rather than runs on real hardware, you lose fidelity in certain low-level features such as I/O operations, memory-mapped hardware (MMIO), system calls, and timing behavior. As a result, Unicorn is often combined with instrumentation frameworks or live analysis to fill in those gaps
Atheris, BOOFuzz wrappers (Python fuzzing frontends)
Fuzzing frameworks and wrappers like Atheris (Google’s Python fuzzing engine) or Python wrappers around BOOFuzz let you integrate fuzzing into Python scripts.
You can write harnesses in Python, feed inputs to target functions (native or instrumented), mutate those inputs to explore different execution paths, drive coverage, and detect crashes. This fits neatly into RE workflows where you want to fuzz parsers, decode routines, or protocol handlers as part of reverse engineering pipelines.
pwntools, python-elfutils, angr
- pwntools is a Python library geared to exploit development and CTF-style interaction. It wraps process I/O, remote sockets, shellcode generation, packing/unpacking, and interfaces nicely with GDB.
- python-elfutils lets you inspect ELF metadata (sections, symbols, relocation) entirely in Python, useful for binary preprocessing.
- angr is a powerful Python framework for symbolic execution, control-flow graph analysis, path exploration, and vulnerability discovery. With angr, you can automatically reason about possible inputs or paths through the binary. It’s deeply integrated into RE workflows.
Androguard
Androguard is a pure-Python framework specializing in Android reverse engineering (APK / DEX / Dalvik) analysis.
It can parse APKs, disassemble DEX files, analyze bytecode control flow, tag classes and methods, detect malware patterns, and decompile into more readable forms. All of this is accessible through Python.
Because so much Android reverse engineering logic is already in Python, Androguard is a go-to library in mobile RE toolchains.
GitHub Copilot for GDB, CI script assistance
Although it is not a traditional binding, GitHub Copilot can be used in GDB and code editors to help generate or autocomplete Python and GDB script code during debugging and reverse-engineering automation building. It is especially helpful for speeding up script creation in continuous integration pipelines, automating GDB workflows, and generating scaffolding for tasks involving reverse engineering when contextual data, such as registers and memory patterns, is available.
Miasm
Miasm is a full Python reverse engineering framework that supports disassembly, intermediate representation (IR), symbolic and JIT-based execution, and transformation of binaries.
Because it is built in Python, you can embed Miasm modules directly in your RE scripts, convert binary code to its IR, simplify expressions, emulate parts of the binary, and chain transformations all without leaving Python.
Miasm is especially attractive in research and prototyping phases thanks to its flexibility and integration in the Python ecosystem.
Volatility
Volatility is a memory forensics and analysis framework written in Python.
It provides modules for parsing memory images, enumerating processes, detecting hooks, scanning kernel structures, and more. All in Python.
You can script advanced forensic or memory analysis tasks in Python, integrate with RE pipelines, or automate the extraction of indicators of compromise (IoCs) from memory snapshots.
Organizing Your Arsenal: Structure & Best Practices
To make your toolbox maintainable:
- Use a modular folder structure (e.g. core/, plugins/, fuzzers/, wrappers/)
- Maintain a requirements.txt or pyproject.toml with pinned versions
- Add automated tests, e.g., test sample binaries or checksum outputs
- Write documentation / “recipes” showing usage
- Version your arsenal in Git / store it centrally
Hosting & Distribution Recommendations
- Host the core arsenal in a private Git repo (or public if desired)
- Package as a Python package (wheel) or provide installation scripts
- Optionally containerize (Docker) to reduce “it works on my machine” problems
- Provide a minimal CLI wrapper (e.g., arsenal run fuzz, arsenal decompile)
Conclusion & Next Steps
No single tool does it all, but combining Capstone, LIEF, Unicorn, pwntools, Frida, and others gives you the flexibility to work across different domains.
Looking ahead, keep your bindings up to date, especially as new architectures like RISC-V and WebAssembly gain traction. Use AI and machine learning where they add value, such as in fuzzing or anomaly detection. Treat your toolkit as a living project: versioned, tested, and integrated into CI workflows.
If you are digging into firmware, analyzing malware, or picking apart custom binaries, having the right Python tools makes it easier to adjust and move fast. Think of these tools as modular pieces you can mix, match, and build on. The next big leap might come from automating something you used to do manually.
