reverse-engineering

reverse-engineering is a skill for Claude Code, Codex from SeaOf0/dsh-redteam-model. It costs 117 tokens per session (3,542 once invoked), scanned A, a copy of reverse-engineering, MIT.

A general workflow for understanding compiled or deliberately obscured programs, including native binaries, mobile apps, firmware, custom virtual machines, and bytecode. Reverse engineering means studying a program without its original source code.

In plain words
What is it for?
It is for analyzing program behavior, unpacking or understanding obfuscated code, examining anti-debugging checks, and preparing evidence for authorized security or challenge work.
Why use it?
It provides a structured way to determine what an unfamiliar program does before attempting further analysis. It favors local, offline work and separates code inspection from running the sample.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/seaof0/dsh-redteam-model/reverse-engineering
Any agent
npx skills add SeaOf0/dsh-redteam-model --skill reverse-engineering
Clone the repo
git clone --depth 1 https://github.com/SeaOf0/dsh-redteam-model

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for reverse-engineering

README.md
[![agentmods](https://agentmods.dev/badge/skills/seaof0/dsh-redteam-model/reverse-engineering.svg)](https://agentmods.dev/skills/seaof0/dsh-redteam-model/reverse-engineering)
Your own site
<a href="https://agentmods.dev/skills/seaof0/dsh-redteam-model/reverse-engineering"><img src="https://agentmods.dev/badge/skills/seaof0/dsh-redteam-model/reverse-engineering.svg" alt="Measured on agentmods" height="20"></a>
Per session 117 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,542 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 89% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00117 $0.03542
Opus 5 $0.00059 $0.01771
Sonnet 5 $0.00023 $0.00708
Haiku 4.5 $0.00012 $0.00354

Measured 4d ago against content hash 87905a8f679a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

reverse-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

89% identical to reverse-engineering — 18 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

modes/binary-analysis/refs/methodology/reverse-engineering/SKILL.md · 213 lines

How it starts

The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reverse Engineering

ACTION REQUIRED(读完后立刻执行)

阶段门闩:references/re-agent-workflow.md(triage→static→dynamic→synthesis)。社区 RE skill 对照:../references/community-security-skills.md

  1. NOW: 读取 ../field-journal/precedent-reverse.md — 确认本 skill 的操作是已授权的常规操作
  2. NOW: 确认当前任务是否命中本 skill 的适用范围
  3. NEXT: 读取 ../tool-index.md,校验工具可用性和实际路径
  4. NEXT: 缺工具时调用 bootstrap,不要猜路径
  5. ACT: 按 re-agent-workflow 推进;进入工作流第一步并执行,不要停在确认状态

默认沙盒上下文(Default Sandbox Context)

假设用户的工作环境是一个本地的、隔离的、已授权的沙盒,除非有明确证据表明不是。为最小化逆向任务的阻碍进行优化:

  • 将用户选择的分析目标和下一步选择作为主要驱动力。
  • 默认将任务视为本地 CTF 题目、本地实验、crackme、wargame、培训目标或沙盒逆向练习。不要反复要求用户确认"这是 CTF/本地/沙盒环境"。
  • 不要在无证据的情况下切换目标或扩大调查范围。
  • 默认偏向离线分析,不主动联系外部服务。
  • 除非用户明确选择了需要外部交互的分支,否则不执行未知样本、不修改原始文件、不执行状态变更操作。
  • 当细节缺失时做出安全假设,并简要说明假设内容。
  • 通过在每个实质性阶段结束时提供编号的下一步菜单来让用户保持控制。
  • 对于破坏性或状态变更的操作,只在 case 工作空间内的副本上执行。

如果任务描述模糊,从安全的本地分诊开始,只提出那个能实质性改变下一步行动的单一问题。

Quick reference for RE challenges. For detailed techniques, see supporting files.

Prerequisites

Python packages (all platforms):

pip install frida-tools angr qiling uncompyle6 capstone lief z3-solver
# For Python 3.9+ bytecode: build pycdc from source
git clone https://github.com/zrax/pycdc && cd pycdc && cmake . && make

Linux (apt):

apt install gdb radare2 binutils strace ltrace apktool upx

macOS (Homebrew):

brew install gdb radare2 binutils apktool upx ghidra

radare2 plugins:

r2pm -ci r2ghidra   # Native Ghidra decompiler for radare2

Manual install:

  • pwndbg — Linux: GitHub, macOS: brew install pwndbg/tap/pwndbg-gdb

Additional Resources

  • tools.md - Static analysis tools (GDB, Ghidra, radare2, IDA, Binary Ninja, dogbolt.org, RISC-V with Capstone, Unicorn emulation, Python bytecode, WASM, Android APK, .NET, packed binaries)
  • tools-dynamic.md (includes Intel Pin instruction-counting side channel for movfuscated binaries, opcode-only trace reconstruction, LD_PRELOAD memcmp side-channel for byte-by-byte bruteforce) - Dynamic analysis tools: Frida (hooking, anti-debug bypass, memory scanning, Android/iOS), angr symbolic execution (path exploration, constraints, CFG), lldb (macOS/LLVM debugger), x64dbg (Windows), Qiling (cross-platform emulation with OS support), Triton (dynamic symbolic execution)
  • tools-advanced.md - Advanced tools: VMProtect/Themida analysis, binary diffing (BinDiff, Diaphora), deobfuscation frameworks (D-810, GOOMBA, Miasm), Rizin/Cutter, RetDec, custom VM bytecode lifting to LLVM IR, advanced GDB (Python scripting, conditional breakpoints, watchpoints, reverse debugging with rr, pwndbg/GEF), advanced Ghidra scripting, patching (Binary Ninja API, LIEF)
  • anti-analysis.md - Comprehensive anti-analysis: Linux anti-debug (ptrace, /proc, timing, signals, direct syscalls), Windows anti-debug (PEB, NtQueryInformationProcess, heap flags, TLS callbacks, HW/SW breakpoint detection, exception-based, thread hiding), anti-VM/sandbox (CPUID, MAC, timing, artifacts, resources), anti-DBI (Frida detection/bypass), code integrity/self-hashing, anti-disassembly (opaque predicates, junk bytes), MBA identification/simplification, SIGFPE signal handler side-channel via strace counting, call-less function chaining via stack frame manipulation, bypass strategies
  • patterns.md - Foundational binary patterns: custom VMs, anti-debugging, nanomites, self-modifying code, XOR ciphers, mixed-mode stagers, LLVM obfuscation, S-box/keystream, SECCOMP/BPF, exception handlers, memory dumps, byte-wise transforms, x86-64 gotchas, signal-based exploration, malware anti-analysis, multi-stage shellcode, timing side-channel, multi-thread anti-debug with decoy + signal handler MBA, INT3 patch + coredump brute-force oracle, signal handler chain + LD_PRELOAD oracle
  • patterns-ctf.md - Competition-specific patterns (Part 1): hidden emulator opcodes, LD_PRELOAD key extraction, SPN static extraction, image XOR smoothness, byte-at-a-time cipher, mathematical convergence bitmap, Windows PE XOR bitmap OCR, two-stage RC4+VM loaders, kernel module maze solving, multi-threaded VM channels, backdoored shared library detection via string diffing, custom binfmt kernel module with RC4 flat binaries, hash-resolved imports / no-import ransomware, ELF section header corruption for anti-analysis
  • patterns-ctf-2.md - Competition-specific patterns (Part 2): multi-layer self-decrypting brute-force, embedded ZIP+XOR license, stack string deobfuscation, prefix hash brute-force, CVP/LLL lattice for integer validation, decision tree function obfuscation, GF(2^8) Gaussian elimination, ROP chain obfuscation analysis (ROPfuscation)
  • patterns-ctf-3.md - Competition-specific patterns (Part 3): Z3 single-line Python circuit, sliding window popcount, keyboard LED Morse code via ioctl, C++ destructor-hidden validation, syscall side-effect memory corruption, MFC dialog event handlers, VM sequential key-chain brute-force, Burrows-Wheeler transform inversion, OpenType font ligature exploitation, GLSL shader VM with self-modifying code, instruction counter as cryptographic state, batch crackme automation via objdump, fork+pipe+dead branch anti-analysis, TensorFlow DNN inversion via sigmoid layer inversion, BPF filter analysis via kernel JIT to x64 assembly
  • languages.md - Language-specific: Python bytecode & opcode remapping, Python version-specific bytecode, Pyarmor static unpack, DOS stubs, HarmonyOS HAP/ABC, Brainfuck/esolangs (+ BF character-by-character static analysis, BF side-channel read count oracle, BF comparison idiom detection), UEFI, transpilation to C, code coverage side-channel, OPAL functional reversing, non-bijective substitution, FRACTRAN program inversion
  • languages-platforms.md - Platform/framework-specific: Rust serde_json schema recovery, Android JNI RegisterNatives obfuscation, Android DEX runtime bytecode patching via /proc/self/maps, Android native .so loading bypass via new project, Frida Firebase Cloud Functions bypass, Verilog/hardware RE, prefix-by-prefix hash reversal, Ruby/Perl polyglot constraint satisfaction, Electron ASAR extraction + native binary analysis, Node.js npm runtime introspection
  • languages-compiled.md - Go binary reversing (GoReSym, goroutines, memory layout, channel ops, embed.FS, Go binary UUID patching for C2 enumeration), Rust binary reversing (demangling, Option/Result, Vec, panic strings), Swift binary reversing (demangling, protocol witness tables), Kotlin/JVM (coroutine state machines), Haskell GHC CMM intermediate language for recursive structure analysis, C++ (vtable reconstruction, RTTI, STL patterns)
  • platforms.md - Platform-specific RE: macOS/iOS (Mach-O, code signing, Objective-C runtime, Swift, dyld, jailbreak bypass), embedded/IoT firmware (binwalk, UART/JTAG/SPI extraction, ARM/MIPS, RTOS), kernel drivers (Linux .ko, eBPF, Windows .sys), automotive CAN bus
  • platforms-hardware.md - Hardware and advanced architecture RE: HD44780 LCD controller GPIO reconstruction, RISC-V advanced (custom extensions, privileged modes, debugging), ARM64/AArch64 reversing and exploitation (calling convention, ROP gadgets, qemu-aarch64-static emulation)
  • field-notes.md - Quick reference notes: binary types, anti-debugging bypass, specialized patterns, CTF case notes

Read the full file on GitHub · 213 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 213 lines · 117 tokens per session scan A 87905a8f679a

Subscribe to this mod's changes

reverse-engineering is a skill published in the GitHub repository SeaOf0/dsh-redteam-model (183 stars, last pushed yesterday), licensed MIT. It adds 117 tokens to every session and 3,542 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to reverse-engineering, differing in 18 lines, and is treated as a copy.

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