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Heap Exploitation

OrganizationPopular
yaklang
heap-exploitation

Heap exploitation playbook. Use when targeting ptmalloc2/glibc heap vulnerabilities including UAF, double free, overflow, off-by-one/null, and leveraging tcache/fastbin/unsortedbin attacks for arbitrary write or code execution.

Overview

Publisheryaklang
Repositoryhack-skills
Skill nameheap-exploitation
Stars
2.2K
Forks
292
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Heap Exploitation 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.
  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/yaklang/hack-skills.git /tmp/hack-skills
mkdir -p .claude/skills
cp -r /tmp/hack-skills/skills/heap-exploitation .claude/skills/heap-exploitation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Heap Exploitation 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 Heap Exploitation 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 Heap Exploitation 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.

SKILL: Heap Exploitation — Expert Attack Playbook

AI LOAD INSTRUCTION: Expert glibc heap exploitation techniques. Covers ptmalloc2 internals, bin structures, tcache mechanics, libc/heap leak methods, and attack selection by glibc version. Distilled from ctf-wiki heap sections, how2heap, and real-world exploitation. Base models often confuse glibc version constraints and miss safe-linking (PROTECT_PTR) introduced in 2.32.

0. RELATED ROUTING

Advanced References


1. PTMALLOC2 STRUCTURE QUICK REFERENCE

malloc_chunk Layout (64-bit)

         chunk pointer (returned by malloc - 0x10)
         ┌──────────────────────────┐
    0x00 │  prev_size (if prev free)│
    0x08 │  size        | A | M | P │  ← P=PREV_INUSE, M=IS_MMAPPED, A=NON_MAIN_ARENA
         ├──────────────────────────┤  ← user data starts here (returned pointer)
    0x10 │  fd (if free)            │  ← forward pointer to next free chunk
    0x18 │  bk (if free)            │  ← backward pointer to prev free chunk
    0x20 │  fd_nextsize (large only)│
    0x28 │  bk_nextsize (large only)│
         └──────────────────────────┘

Bin Types

BinSize Range (64-bit)StructureLIFO/FIFO
tcache (per-thread)≤ 0x410 (7 entries per size)Singly linked (next pointer)LIFO
fastbin≤ 0x80 (default)Singly linked (fd)LIFO
unsortedbinAny freed sizeDoubly linked circularFIFO
smallbin< 0x400Doubly linked circularFIFO
largebin≥ 0x400Doubly linked + size-sortedSorted

Key Global Structures

StructureLocationPurpose
main_arenalibc .data segmentContains bin heads, top chunk, system_mem
mp_libc .datamalloc parameters (tcache settings, mmap threshold)
tcache_perthread_structHeap (first allocation)Per-thread tcache bins and counts

2. LEAK METHODS

Libc Base Leak

MethodPreconditionTechnique
Unsortedbin fd/bkFree a chunk > tcache range (or fill tcache)fd/bk → main_arena + 0x60 (or +0x70 depending on version) → libc base
Smallbin fd/bkChunk moved from unsortedbin to smallbinSame as unsortedbin leak
stdout FILE leakWrite to _IO_2_1_stdout_Corrupt _IO_write_base to leak libc data (see IO_FILE)

Heap Base Leak

MethodPreconditionTechnique
Tcache fd pointerFree two tcache chunks, read first's fdfd → heap address (XOR'd in ≥ 2.32)
Fastbin fdFree two fastbin chunksfd → heap address
UAF readUse-after-free on freed chunkRead fd/bk directly

Safe-Linking Decode (glibc ≥ 2.32)

python
# PROTECT_PTR: fd_stored = (chunk_addr >> 12) ^ real_fd
# To decode: real_fd = fd_stored ^ (chunk_addr >> 12)
# To encode: fd_stored = (chunk_addr >> 12) ^ target_addr

def deobfuscate(stored_fd, chunk_addr):
    return stored_fd ^ (chunk_addr >> 12)

def obfuscate(target, chunk_addr):
    return (chunk_addr >> 12) ^ target

3. ATTACK CATEGORIES BY GLIBC VERSION

glibc < 2.26 (No tcache)

AttackPrimitive NeededResult
Fastbin dupDouble freeArbitrary allocation
Unsortedbin attackCorrupt unsortedbin bkWrite main_arena addr to target (used for __malloc_hook nearby overwrite)
Unlink attackHeap overflow into prev_size + fd/bkArbitrary write (with known heap pointer)
House of ForceTop chunk size overwriteArbitrary allocation
House of SpiritWrite fake chunk headerFastbin allocation at fake chunk
Off-by-one nullNull byte overflow into next chunk sizeOverlapping chunks

glibc 2.26–2.28 (tcache, no key)

AttackNotes
Tcache poisoningOverwrite tcache fd → arbitrary allocation, no size check
Tcache dupDouble free into tcache (no double-free detection yet)
All previous attacksStill work, but chunks go to tcache first

glibc 2.29–2.31 (tcache key introduced)

AttackBypass for tcache key
Tcache dupCorrupt key field (at chunk+0x18) before second free
House of BotcakeDouble free: one in unsortedbin, one in tcache → overlapping
Tcache stashing unlinkAbuse smallbin→tcache refill to get arbitrary chunk

glibc 2.32–2.33 (safe-linking / PROTECT_PTR)

AttackAdaptation
Tcache poisoningEncode target with (chunk_addr >> 12) ^ target
Heap leak requiredNeed heap addr to decode/encode safe-linked pointers
Fastbin dupSame encoding required

glibc ≥ 2.34 (hooks removed)

ChangeImpact
__malloc_hook removedCannot overwrite hook for one_gadget
__free_hook removedCannot overwrite hook
__realloc_hook removedCannot use realloc trick for one_gadget constraints

Post-2.34 targets: see arbitrary-write-to-rce for _IO_FILE, exit_funcs, TLS_dtor_list, _dl_fini.


4. COMMON VULNERABILITY PATTERNS

VulnerabilityDescriptionExploitation Path
UAF (Use-After-Free)Access chunk after freeRead: leak fd/bk; Write: corrupt fd for tcache poisoning
Double Freefree() same chunk twiceTcache dup (bypass key) or fastbin dup
Heap OverflowWrite past chunk boundaryCorrupt next chunk's metadata (size, fd, bk)
Off-by-oneOne byte overflowNull byte → shrink next chunk size → overlapping chunks
Off-by-nullSpecifically \x00 overflowClear PREV_INUSE → trigger backward consolidation
Uninitialized readRead heap memory without clearingLeak fd/bk from recycled chunk

5. TOOLS

bash
# pwndbg heap inspection
pwndbg> heap                      # display all chunks
pwndbg> bins                      # show all bin contents
pwndbg> tcachebins                # tcache status
pwndbg> fastbins                  # fastbin status
pwndbg> unsortedbin               # unsortedbin content
pwndbg> vis_heap_chunks           # visual heap layout
pwndbg> find_fake_fast &__malloc_hook  # find nearby fake fastbin chunks

# how2heap — reference implementations
git clone https://github.com/shellphish/how2heap

# heapinspect
pip install heapinspect
heapinspect <pid>

# pwntools helpers
from pwn import *
libc = ELF('./libc.so.6')
print(hex(libc.symbols['__malloc_hook']))
print(hex(libc.symbols['__free_hook']))

6. DECISION TREE

Heap vulnerability identified
├── What is the primitive?
│   ├── UAF (read + write)
│   │   ├── Can read freed chunk? → Leak libc (unsortedbin) or heap (tcache fd)
│   │   └── Can write freed chunk? → Tcache poisoning / fastbin dup
│   ├── Double free
│   │   ├── glibc < 2.29 → direct tcache dup
│   │   ├── glibc 2.29-2.31 → corrupt tcache key first, or House of Botcake
│   │   └── glibc ≥ 2.32 → need heap leak for safe-linking encode
│   ├── Heap overflow (controlled size)
│   │   ├── Overwrite next chunk size → overlapping chunks → UAF
│   │   └── Overwrite fd directly → arbitrary allocation
│   ├── Off-by-one / off-by-null
│   │   ├── Null byte into size → House of Einherjar (backward consolidation)
│   │   └── One byte into size → shrink chunk, create overlap
│   └── Arbitrary write (from overlap or poisoned allocation)
│       ├── glibc < 2.34 → __malloc_hook / __free_hook → one_gadget
│       ├── glibc ≥ 2.34 → _IO_FILE vtable, exit_funcs, TLS_dtor_list
│       └── Partial RELRO → GOT overwrite
├── Need libc leak?
│   ├── Free chunk into unsortedbin (size > 0x410 or fill 7 tcache)
│   ├── Read fd/bk → main_arena offset → libc base
│   └── Alternative: stdout FILE partial overwrite for leak
└── Need heap leak? (glibc ≥ 2.32)
    ├── Read tcache fd from freed chunk
    └── Decode: real_addr = stored_fd ^ (chunk_addr >> 12)

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 Heap Exploitation AI skill do?

Heap exploitation playbook. Use when targeting ptmalloc2/glibc heap vulnerabilities including UAF, double free, overflow, off-by-one/null, and leveraging tcache/fastbin/unsortedbin attacks for arbitrary write or code execution.

Why use Heap Exploitation on TypingMind?

Because you install it once and use it with any model. Heap Exploitation 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 Heap Exploitation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yaklang/hack-skills/tree/main/skills/heap-exploitation. 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 Heap Exploitation?

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 Heap Exploitation?

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

Is the Heap Exploitation AI skill free?

Yes. It is published on GitHub by yaklang under the MIT license. 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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