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Anti Debugging Techniques

Community
ptn1411
anti-debugging-techniques

Anti-debug detection and bypass for protected binaries. Activate before patching commercial apps that use ptrace/PEB/timing/exception checks on Linux or Windows.

Overview

Publisherptn1411
Repositoryskill
Skill nameanti-debugging-techniques
Stars
275
Forks
133
Bundled files
3
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ptn1411 on GitHub. Read the source before you install it.

Installation

Install the Anti Debugging Techniques AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ptn1411/skill/tree/main/anti-debugging-techniques
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ptn1411/skill.git /tmp/skill
mkdir -p .claude/skills
cp -r /tmp/skill/anti-debugging-techniques .claude/skills/anti-debugging-techniques
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Anti Debugging Techniques in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Anti Debugging Techniques on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Anti Debugging Techniques is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Anti-Debugging Techniques

Upstream reference: yaklang/hack-skills/anti-debugging-techniques — distilled from ctf-wiki + hacktricks. Use that page for full prose. Language rule: instructions in English; final user-facing summary in Vietnamese.

0. Authorization & routing

Operates under MASTER_POLICY.md §1-§2 (owner-attested target, granted technical authority).

Run this skill before binary-patcher on any commercial-grade binary — patches misfire when anti-debug watchdogs are still live.

Sibling skillWhen
binary-identifierFirst — fingerprint protector (VMProtect/Themida ship known anti-debug stacks)
binary-protection-bypassAfter — ASLR/PIE/canary still blocks patch even if debugger is hidden
vm-and-bytecode-reverseWhen the check is implemented inside a custom VM dispatcher
symbolic-execution-toolsSkip checks symbolically with angr instead of patching

1. Detection-class → first-line bypass

ClassLinux primaryWindows primary
API callLD_PRELOAD shim returning 0Hook IsDebuggerPresent / NtQueryInformationProcess
Process flagFilter TracerPid in /proc/self/statusZero PEB BeingDebugged / NtGlobalFlag / heap flags
TimingFrida hook on clock_gettimeFrida hook on rdtsc / QueryPerformanceCounter
Signal / exceptionGDB handle SIGTRAP nostop passVEH handler or ScyllaHide
HW breakpointn/aHook GetThreadContext to clear DR0–DR3
Multi-process watchdogGDB set follow-fork-mode childAttach to both processes

2. Linux check signatures (grep-able)

text
ptrace(           # PTRACE_TRACEME self-attach
fopen("/proc/self/status"      # TracerPid scan
fopen("/proc/self/maps"        # Frida/LD_PRELOAD scan
rdtsc                          # timing measurement
signal(SIGTRAP                 # signal-swallowed test
fork()                         # potential watchdog parent/child

3. Windows PEB + Nt* offsets (x64)

FieldLocationDebuggedClean
BeingDebuggedPEB+0x0210
NtGlobalFlagPEB+0xBC0x700
ProcessHeap.FlagsHeap+0x400x400000620x00000002
ProcessHeap.ForceFlagsHeap+0x440x400000600

NtQueryInformationProcess info-class IDs: ProcessDebugPort=0x07, ProcessDebugObjectHandle=0x1E, ProcessDebugFlags=0x1F (returns 0 when debugged — inverted).

4. Tooling matrix

ToolPlatformUse
ScyllaHideWindows (x64dbg/IDA plugin)One-click PEB/Nt*/timing patch
TitanHideWindows kernel driverKernel-level hide for DR/heap checks
FridaCross-platformScript hooks, timing spoof
LD_PRELOADLinuxptrace/getenv/fopen replacement
GDBLinuxcatch syscall, conditional BP, register fixup
QilingCross-platformFull emulation — bypasses HW-level checks

5. Functional bypass snippets

Linux ptrace shim

bash
# bash / WSL
cat > /tmp/ap.c <<'EOF'
long ptrace(int r, int p, void *a, void *d) { return 0; }
EOF
gcc -shared -fPIC -o /tmp/ap.so /tmp/ap.c
LD_PRELOAD=/tmp/ap.so ./target

GDB catch-and-clear

text
(gdb) catch syscall ptrace
(gdb) commands
> set $rax = 0
> continue
> end

Frida cross-platform

javascript
// Linux ptrace
Interceptor.replace(
  Module.getExportByName(null, 'ptrace'),
  new NativeCallback(() => 0, 'long', ['int', 'int', 'pointer', 'pointer'])
);
// Windows IsDebuggerPresent
Interceptor.replace(
  Module.getExportByName('kernel32.dll', 'IsDebuggerPresent'),
  new NativeCallback(() => 0, 'int', [])
);

x64dbg + ScyllaHide (PowerShell launch)

powershell
& 'C:\Tools\x64dbg\x64\x64dbg.exe' 'C:\Program Files\MyApp\target.exe'
# In GUI: Plugins → ScyllaHide → Options →
#   PEB BeingDebugged, NtGlobalFlag, HeapFlags
#   NtQueryInformationProcess (all classes)
#   NtSetInformationThread (HideFromDebugger)
#   GetTickCount, QueryPerformanceCounter
# → Apply → restart session

6. Bypass methodology

  1. Static scan — grep the strings/imports above; map every check site.
  2. Classify each hit into the table in §1.
  3. Apply layered bypass — start with ScyllaHide (Win) or LD_PRELOAD shim (Linux); they cover ~80% of common checks.
  4. Handle TLS callbacks before main runs (x64dbg "Break on TLS" / WinDbg sxe ld).
  5. Verify — set BP on ExitProcess/exit/_exit; if hit unexpectedly, a check was missed → trace back.

7. Real-world protector defaults

ProtectorStackRecommended kit
VMProtectPEB + timing + driverTitanHide + ScyllaHide
ThemidaPEB + SEH + timingScyllaHide + manual NOP
EnigmaIsDebuggerPresent + CRCx64dbg + ScyllaHide
UPX (custom)Usually noneStandard unpack

8. Final report (Vietnamese)

🛡️ Bypass Anti-Debug hoàn tất:
  🔍 Phát hiện     : <DETECTION_TYPES>
  🛠️ Bypass        : <METHODS>
  🧰 Công cụ        : <ScyllaHide | Frida | LD_PRELOAD | GDB>
  ✅ Verify        : chạy không exit sớm dưới debugger

▶  Tiếp:
   binary-protection-bypass (nếu ASLR/PIE chặn patch)
   → binary-patcher (vá license check)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Anti Debugging Techniques AI skill do?

Anti-debug detection and bypass for protected binaries. Activate before patching commercial apps that use ptrace/PEB/timing/exception checks on Linux or Windows.

Why use Anti Debugging Techniques on TypingMind?

Because you install it once and use it with any model. Anti Debugging Techniques is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Anti Debugging Techniques in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ptn1411/skill/tree/main/anti-debugging-techniques. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Anti Debugging Techniques?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Anti Debugging Techniques?

As many as you like. As long as a model supports skills, you can use Anti Debugging Techniques with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Anti Debugging Techniques AI skill free?

It is published on GitHub by ptn1411. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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