How Python Decompilers Work (And How Nyami Breaks Them)

Python bytecode decompilers have become increasingly sophisticated. Tools like pylingual, pycdc, and uncompyle6 can reconstruct readable source code from compiled Python files with surprising accuracy, often recovering function names, control flow, and even string literals.

How decompilers reconstruct source

Python's compile() function produces code objects containing bytecode instructions, constants, variable names, and line number tables. Decompilers parse these structures and attempt to reverse the compilation process:

Bytecode disassembly: instructions are decoded into a stream of opcodes

2. Control flow recovery: jumps, loops, and conditionals are identified from the instruction stream

3. Structure reconstruction: the control flow graph is translated back into if/else blocks, loops, and try/except handlers

4. Expression rebuilding: individual operations are assembled into expressions

The key insight: decompilers depend on predictable bytecode patterns. If you can break those patterns while keeping the code functionally identical, you break the decompiler.

How Nyami breaks decompilers

Decompiler Breaker module: Nyami specifically targets the AST structures that decompilers rely on. It corrupts EXTENDED_ARG chains, which decompilers use to reconstruct multi-byte instructions. When pylingual encounters this corrupted structure, it either crashes or produces nonsensical output.

Control Flow Flattening: switches the entire function body into a state-dispatch loop. Instead of a clean if/else structure, the decompiler sees a flat sequence of state transitions. Most decompilers cannot recover the original branching logic from this representation.

Opaque Predicates: injects conditions that are always true or always false at runtime but appear variable to static analysis. Decompilers attempt to follow both branches, inflating their state space and often hitting recursion limits or producing broken code.

MBA Obfuscation: transforms simple expressions like x + y into algebraically equivalent forms using mixed Boolean-arithmetic identities. Decompilers that attempt to simplify these expressions produce code that looks wildly different from the original.

Why this matters

If you distribute Python applications, decompilation is the first tool an attacker reaches for. Stopping decompilers at the bytecode level (before they can produce usable source) raises the cost of reverse engineering significantly. Combined with anti-debug and anti-tamper protections, Nyami ensures that even if an attacker gets past the first layer, the next layers are waiting.