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Learn how to conduct secure source code reviews of legacy systems using Git, AI tools, taint analysis, and smart auditing techniques.Legacy codebases present a unique set of challenges, often hard to navigate and understand, housing a lot of deprecated practices that make code unreadable. However, with the right tools and strategies, you can effectively navigate and audit these systems. Here’s how you can tackle legacy C and C++ codebases.
“6 hours to chop down a tree, first 4 to sharpen the axe.”
Your development environment is your axe. The sharper the setup is, the more efficiently you'll be navigating large codebases.
(Also don't chop trees, they're cool and good for us.
Anyways, tools like VSCode offer a wide range of features, including built-in grep searches and syntax highlighting, which can significantly enhance your productivity.
Your ideal tool will be one that can be easily customized and can help you navigate at super speeds through the code base. cscope and ctags allow you to quickly locate function definitions and jump between different parts of the codebase. GNU Global can help in indexing references to quickly jump across them when analyzing taint.
Ability to customize your environment with extensions and plugins ensures that you can tailor it to your specific needs.
Git history is a powerful tool for understanding how the codebase has evolved over time. Tools like git blame and git log provide insights into who made changes, when they were made, and why.
Use git blame to identify who last modified a line of code, which can be invaluable for tracking down domain knowledge.
git log helps you understand the rationale behind changes, making it easier to understand how a component changed and why.
A Language Server Protocol (LSP) is a protocol that enables rich code-editing features like auto-completion, go-to-definition, and real-time diagnostics across different editors and IDEs. It standardizes communication between an editor and a language-specific server.
The nifty auto-complete and in-line error squiggles you see on your IDE is due to the LSP and the language server constantly statically analyzing and indexing your code.
To fully utilize a LSP like clangd, you need a compile_commands.json file.
This file provides essential compilation details, enabling features such as precise symbol resolution, quick navigation to function definitions, and accurate code completions.
Build systems like CMake have in-built support for generating this file but if you’re dealing with a legacy code base, it’s most probably just Makefiles.
In that case tools like Bear and Compiledb come really handy. These tools can generate the necessary compile commands even for legacy codebases that use Makefiles instead of CMake.
When working with large or legacy codebases, documentation isn’t just helpful, it’s essential. Without clear and structured documentation, it can be a harsh struggle to understand intricate dependencies, function flows, or the intent behind specific implementations.
Documentation is essential for understanding complex codebases. Tools like Doxygen can generate detailed documentation from comments in your code.
Doxygen can generate graphs showing class relationships or function calls, which can help visualize the architecture. It also supports searching through documentation to find specific functions or classes.
Although end of the day, it is up to the developers to write good documentation. So, pray or manifest that they do.
Test cases are more than just validation tools; they provide insights into the developer's intentions and design decisions. They reveal how developers expect the code to behave, shedding light on potential edge cases, implicit trust assumptions, and areas where security might have been an afterthought.
Test cases provide a roadmap of intended functionality, helping you spot discrepancies between expected and actual behavior. If a function is tested only with benign inputs, it might indicate a lack of handling for malformed, oversized, or unexpected data.
If test cases assume specific input constraints without validating them, the corresponding code might be vulnerable to injection attacks, buffer overflows, or privilege escalation.
Security-focused test cases (if they exist) can guide your research toward input validation routines, authentication mechanisms, and access control logic
Taint analysis is a technique used in static code analysis to track the flow of untrusted data through a program.
The goal is to determine whether user-controlled input can reach sensitive operations (sinks) without proper validation or sanitization. If untrusted data can influence security-critical code, it can lead to severe vulnerabilities
Manually tracking tainted data in a large codebase can be quite a pain, which is where Joern and CodeQL come in.
CodeQL is a powerful static analysis tool that enables security researchers and developers to model taint flows at scale using a database-driven approach. It works by converting source code into a queryable database, allowing you to write complex queries to detect security flaws.
Joern is an open-source Code Property Graph (CPG) tool designed for deep analysis of C and C++ codebases. It constructs a graph representation of the source code by combining the Abstract Syntax Tree (AST), which captures the code structure, the Control Flow Graph (CFG) for execution flow, and the Program Dependency Graph (PDG) for tracking data dependencies.
Sometimes, compiling the code and reverse engineering it can be more readable than reading the source code itself.
It can give you clarity when dealing with complex casts and custom types. This also eliminates dead stores and branches.
Before diving into the code, understand its context. If the code implements an RFC or a security-critical standard (e.g., TLS, WebAuthn, or WASM sandboxing), read up on it first. Knowing the intended behavior helps you spot deviations that might introduce vulnerabilities.
Comparing your target implementation with others can also help clarify things. For example, if analyzing a cryptographic library, checking how OpenSSL or BoringSSL handles similar logic can reveal weaknesses in lesser-known implementations.
If you don’t have a high-level understanding of:
…then you risk missing critical vulnerabilities or misinterpreting normal behavior as a bug.
Finally, take detailed notes as you navigate the codebase. Use pen and paper or digital tools to document your findings, including data flows and specific insights.
Writing down your thoughts helps solidify your understanding of complex systems. Notes also can serve as a valuable resource when revisiting the codebase later.
In recent years, AI tools like GitHub Copilot have emerged as powerful allies in navigating legacy code. Copilot can offer code completions, suggest explanations for unfamiliar code snippets, and even generate tests and documentation for poorly documented code.
AI is not a replacement for human judgment, it can certainly help streamline the process of understanding and maintaining legacy systems. You can think of AI as a multiplier to your workflow and not a replacement of you.
Very recently the emergence of MCPs (Model Context Protocol) seems to have a significant impact on the reverse engineering workflow with a lot of heavy duty and tasks being delegated to AI.
Legacy codebases might seem daunting, but with the right approach, they can be transformed into maintainable and efficient systems. By combining traditional techniques with modern tools and AI, you can not only navigate and audit these codebases but also improve them over time.
Whether you're dealing with C, C++, or any other language, the principles remain the same: find sources and sinks, build hypotheses, analyze taint and deliver verdict.
With AppSecEngineer you can learn to supercharge your secure code review skills by practising on realistic, hands-on labs!
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