Binalyzer is an open-source Python tool that parses ELF and PE binary files, and its newly completed Phase 3 adds section-listing capabilities that read, parse, and display detailed information about each section inside an executable. For builders in 2026, this project is a case study in how writing your own tools teaches reverse engineering faster than reading about it.
Why should beginners care about binary analysis?
Binary analysis sounds intimidating. It conjures images of hex editors, assembly language, and late nights staring at memory addresses. But at its core, binary analysis is just reading structured files. Every program on your computer, whether it runs on Linux or Windows, is stored in a binary format that follows a documented specification. Learning to read that format is no different from learning to read JSON or CSV. The structure is more compact, but the idea is the same: headers describe the data, and sections contain it.
The reason beginners should care is that binary analysis sits at the foundation of cybersecurity, malware research, and systems programming. If you want to understand how software actually works once it is compiled, you need to understand binaries. And the best way to understand them is to build a tool that reads them. That is exactly what Binalyzer does, one phase at a time, with 45 commits and 5 releases tracking the entire learning journey in public.
What is Binalyzer and what did Phase 3 add?
Binalyzer is a command-line tool designed to analyze binary files in both ELF and PE formats. The project is built by a developer learning reverse engineering in public, and each phase adds a new layer of parsing capability. Phase 1 handled magic bytes (the first few bytes of a file that identify its format). Phase 2 tackled header parsing. Phase 3 adds section-listing functionality for both PE and ELF files, reading, parsing, and displaying detailed information about each section within binary files.
Sections are where the interesting data lives. In an ELF binary, you will find sections like .text (executable code), .data (initialized variables), and .rodata (read-only data). In a PE file, you will see .text, .rdata, .data, and sometimes .rsrc for resources. Understanding what each section contains and how the operating system uses it is fundamental knowledge for anyone working in security or systems development. Phase 3 makes all of this visible through a single command.
How do ELF and PE formats actually work?
Every executable file starts with a header. The header tells the operating system what kind of file it is, what architecture it targets, and where to find the rest of the file's structure. After the header comes a table of sections (or segments, depending on the context), and then the sections themselves.
For ELF files, the format is defined in the Linux man-pages under elf(5). The specification describes how the header file <elf.h> defines the format of ELF executable binary files, including normal executables, relocatable object files, core files, and shared objects. The ELF header contains fields like e_shoff (section header table offset), e_shnum (number of section headers), and e_shstrndx (index of the section name string table). Parsing these fields correctly is what Binalyzer Phase 3 accomplishes on the Linux side.
For PE files, Microsoft's official specification describes the structure of executable image files and object files under Windows. The PE format includes a DOS header (for backwards compatibility), a PE signature, a COFF header, optional headers, and section headers. Each section header contains the section's name, virtual size, virtual address, raw data size, and characteristics flags that control permissions like read, write, and execute.
When you build a parser for both formats, you start to see the similarities and differences clearly. Both use headers pointing to section tables. Both store code and data in named sections. But the byte layouts, field sizes, and conventions differ. Writing code to handle both is one of the most efficient ways to internalize these concepts.
Why is building your own tool better than using an existing framework?
There are excellent binary analysis frameworks already available. Projects like BARF (Binary Analysis and Reverse engineering Framework) offer mature Python-based tools for binary analysis. Libraries like LIEF provide cross-platform binary parsing with Python bindings. These tools are powerful, but they are also complex. For a beginner, opening a framework with thousands of lines of code and dozens of modules can feel overwhelming.
Building your own tool from scratch, the way Binalyzer does it, forces you to confront each concept one at a time. In Phase 1, you learn what magic bytes are by writing the code that reads them. In Phase 2, you learn header structures by writing struct.unpack() calls that extract each field. In Phase 3, you learn section layouts by iterating over section header tables and formatting the output. Each phase is small enough to finish in a few sessions but substantial enough to teach something real.
This is not a new idea. It is how most experienced reverse engineers learned. They built small tools, broke them, fixed them, and gradually understood the formats. The difference in 2026 is that you can accelerate this process with AI assistance while still retaining the understanding that comes from writing the code yourself.
How can AI coding tools accelerate this kind of project?
If you are exploring the best AI coding agents in 2026, you already know that tools like Claude, Cursor, and GitHub Copilot can generate boilerplate code quickly. For a binary analysis project, an AI agent can help you write the initial struct definitions, generate test cases, and explain format specifications in plain language.
But there is a balance to strike. The learning value of a project like Binalyzer comes from wrestling with the format specification yourself. If you ask an AI to write your entire ELF parser, you will have a working tool but no understanding of why e_shentsize matters or what happens when a section's SHF_EXECINSTR flag is set. The better approach is to write each parsing function yourself, then use an AI agent to review your logic, catch off-by-one errors in offset calculations, and explain fields you do not recognize.
This is different from vibe coding, the rapid approach to going from idea to running app. Vibe coding optimizes for speed and output. Building a binary analyzer optimizes for understanding. Both approaches have their place, and knowing when to use which one is a skill worth developing. For learning projects, slow and deliberate wins. For shipping products, speed matters more.
What does a section-listing output actually look like?
When you run Binalyzer Phase 3 against an ELF binary, the output shows each section with its name, type, offset, size, and flags. A typical output might list 20 to 30 sections for a compiled C program. You will see .text marked as executable and allocatable, .data marked as writable and allocatable, and .symtab marked with no allocation flag (because symbol tables are not loaded into memory at runtime).
For a PE binary, the output shows sections with their virtual and raw sizes, virtual addresses, and characteristic flags. A simple Windows executable might have five or six sections. The .text section will show IMAGE_SCN_MEM_EXECUTE and IMAGE_SCN_MEM_READ flags. The .rdata section will show only IMAGE_SCN_MEM_READ.
Reading this output teaches you something important: executables are not monolithic blobs of code. They are carefully organized structures where each section has a specific purpose and specific permissions. Understanding this organization is the first step toward tasks like malware analysis (where you check for suspicious section names or unusual permission combinations) and vulnerability research (where you examine which sections are writable and executable simultaneously, which is a common indicator of exploitable code).
How does this connect to security fundamentals?
Binary analysis is a core skill in application security. OWASP's guidance on static code analysis describes how static analysis is performed as part of code review during the implementation phase of a Security Development Lifecycle. While OWASP focuses on source code analysis, binary analysis extends the same principle to compiled code where source is not available.
When you can parse sections, you can start asking security-relevant questions. Does this binary have a writable and executable section? That might indicate a packing technique used by malware. Does the .text section's raw size differ significantly from its virtual size? That could mean the binary unpacks code at runtime. Are there sections with unusual names like .upx0 or .aspack? Those are signatures of known packers.
None of these observations require advanced skills. They require understanding what sections are and what their attributes mean. That is precisely what Binalyzer Phase 3 teaches. By building the parser yourself, you develop the intuition to spot anomalies. No framework or pre-built tool gives you that intuition. Only hands-on construction does.
What should you build after completing your own Phase 3?
If you follow Binalyzer's phased approach and build your own section parser, the natural next steps extend your tool in security-relevant directions. Phase 4 could add import and export table parsing, which shows you what external functions a binary calls (critical for malware analysis). Phase 5 could add disassembly of the .text section using a library like Capstone, turning raw bytes into readable assembly instructions.
Beyond the tool itself, consider these adjacent projects. Write a script that scans a directory of binaries and flags any with writable-executable sections. Build a diff tool that compares two versions of the same binary and highlights changed sections. Create a visualization that maps section layouts as colored blocks, making it easy to spot structural differences between samples.
Each of these projects builds on the foundation that section parsing provides. And each one produces something you can show to potential employers, open-source collaborators, or your own learning journal. The Generation AI community at AI Masterminds is full of builders working through exactly this kind of progression, from first project to portfolio piece.
Common pitfalls when building a binary parser
There are a few mistakes that trip up almost every beginner building their first binary parser. Knowing them in advance saves you hours of debugging.
Endianness confusion. ELF files can be little-endian or big-endian, and the header tells you which. If you hardcode little-endian unpacking and then feed your parser a big-endian MIPS binary, every field will be wrong. Always read the EI_DATA byte first and set your byte order accordingly.
Off-by-one errors in offset math. Section headers live at specific offsets from the start of the file. If your offset calculation is wrong by even one byte, the entire parse will produce garbage. Always verify your parsed values against a known-good tool like readelf or objdump before trusting your output.
Ignoring the string table. Section names in ELF are not stored in the section header itself. The header contains an index into a string table section. If you forget to parse the string table first, all your section names will be empty or garbage. This is the single most common bug in beginner ELF parsers.
Assuming PE optional header size. The PE optional header has different sizes for 32-bit and 64-bit binaries (PE32 vs PE32+). If you assume one size and encounter the other, your section header offsets will be wrong. Always read the Magic field of the optional header to determine which variant you are parsing.
Not handling malformed files. Real-world binaries are not always well-formed. Packed binaries, stripped binaries, and deliberately malformed files will break naive parsers. Add bounds checking early. Verify that offsets and sizes do not exceed the file's actual length before trying to read data.
How does this fit into a beginner's AI learning path?
If you are in your first 7 days with AI, a binary analysis project might seem like a strange starting point. But the connection is direct. Understanding how compiled code is structured helps you understand what AI coding tools are actually producing when they generate code for you. It grounds your understanding of software in something concrete and observable.
For beginners who want a broader foundation, start with what generative AI actually is and how it fits into the tools you will use every day. Then pick a builder project like Binalyzer that teaches you something tangible. The combination of AI literacy and hands-on technical skill is what separates people who use AI tools from people who understand what those tools are doing.
The ai-for-beginners pillar on this site is designed exactly for this progression. Start with the concepts, then build something real, then use AI tools to accelerate your building. Each step reinforces the previous one. Binary analysis is one path through this progression, but the principle applies to any domain: learn the fundamentals by building, then use AI to move faster once you understand what you are building.
Real-world example: from parser to malware triage
Here is a concrete scenario that shows why section parsing matters outside of a learning project. Imagine you are a junior analyst at a small security firm. A client sends you a suspicious Windows executable. You do not have an expensive sandbox or a commercial disassembler. You do have Python and the knowledge you gained from building your own section parser.
You run your parser against the file. The output shows six sections, five with normal names and one called .0day with both writable and executable flags set. That is unusual. Legitimate compilers do not produce sections with names like that, and writable-executable sections are a red flag for self-modifying code.
You check the .text section. Its raw size on disk is 512 bytes, but its virtual size is 45,000 bytes. That means the section expands dramatically when loaded into memory, a strong indicator that the binary unpacks itself at runtime. You also notice the import table references VirtualAlloc and VirtualProtect, two Windows API functions commonly used by malware to allocate executable memory.
With three observations, all derived from section-level analysis, you have enough to escalate the sample to a senior analyst with specific findings rather than just "it looks suspicious." That is the practical value of understanding binary sections. You did not need Ghidra or IDA Pro. You needed a Python script and the knowledge of what to look for.
This is the kind of practical, grounded skill that compounds over time. Every binary you analyze adds to your pattern recognition. Every anomaly you spot reinforces your understanding of what "normal" looks like. And it all started with writing a section parser.
Getting started today
If you want to follow Binalyzer's approach, here is a minimal starting path.
First, pick your language. Python is the natural choice because of struct.unpack() and the wealth of existing libraries, but Rust and Go are also popular for binary tooling in 2026. Binalyzer uses Python, and so does most of the beginner-focused content available.
Second, bookmark the specifications. The ELF man page and the Microsoft PE format documentation are your two primary references. You will return to these constantly.
Third, start with magic bytes. Open a compiled binary in your hex editor. Read the first 4 bytes for ELF (\x7fELF) or 2 bytes for PE's DOS header (MZ). Write a Python function that reads those bytes and identifies the format. That is your Phase 1.
Fourth, parse headers. Read the ELF header (52 bytes for 32-bit, 64 bytes for 64-bit) or the PE headers (DOS header, PE signature, COFF header, optional header). Extract each field using struct.unpack(). Print them in a human-readable format. That is your Phase 2.
Fifth, list sections. Use the header information to locate the section header table. Iterate over each entry, parse its fields, and display the results. Handle the string table for ELF. Handle PE32 vs PE32+ variants. That is your Phase 3, and it is where the real learning accelerates.
Each phase builds directly on the previous one. You cannot parse sections without first parsing headers, and you cannot parse headers without first identifying the file format. This natural dependency chain makes the project self-structuring. You always know what to build next.
If you want to connect with other builders working through projects like this, join AI Masterminds. The community includes people at every stage, from their first Python script to production security tools. No pitch, no pressure. Just builders learning by building.
FAQ
What is Binalyzer and what does it do?
Binalyzer is an open-source, command-line Python tool that analyzes binary files in ELF and PE formats. It reads, parses, and displays structured information about executable files, including headers, magic bytes, and (as of Phase 3) detailed section data. The project is built in public across multiple phases, making it a useful learning resource for anyone interested in reverse engineering fundamentals. The source code is available on GitHub under the MIT License.
Do I need to know reverse engineering before building a binary analyzer?
No. That is the entire point of a phased build approach. You start with the basics (reading a file's magic bytes in Phase 1), then move to header parsing, and eventually reach section analysis. Each phase teaches you one layer of how executables work. You learn the concepts by writing code that interacts with them directly. A beginner with basic Python knowledge can follow along and pick up reverse engineering vocabulary naturally.
What is the difference between ELF and PE binary formats?
ELF (Executable and Linkable Format) is the standard binary format for Linux, BSD, and most Unix-like systems. PE (Portable Executable) is the format used by Windows for .exe and .dll files. Both formats contain headers, sections, and metadata, but they organize that information differently. Learning to parse both formats, as Binalyzer does, gives you cross-platform reverse engineering skills that apply whether you are analyzing Linux malware or Windows applications.
Can AI coding tools help me build a binary analyzer?
Yes, but with a caveat. AI coding agents can help you write boilerplate, debug struct-unpacking errors, and explain format specifications. However, the learning value comes from understanding why each byte matters. Use AI as a tutor that explains what you are reading, not as a shortcut that writes the parser for you. The best approach is to write each function yourself, then ask an AI agent to review your logic against the official format specification.
Where can I find the official specifications for ELF and PE formats?
The PE format specification is maintained by Microsoft on their Learn documentation site under the Win32 debugging section. The ELF format is documented in the Linux man-pages, specifically the elf(5) manual page hosted at man7.org. Both documents are freely available online and serve as the authoritative references when building any binary analysis tool. Bookmarking these two pages is the first step for any beginner project in this space.
Sources
- Binalyzer: Phase 3 is now complete! · DEV Community
- PE Format – Win32 apps · Microsoft Learn
- elf(5) - Linux manual page · man7.org
- AngBan2x/binalyzer - GitHub · GitHub
- Static Code Analysis – OWASP · OWASP Foundation