zeroforge
Enables adaptive binary 0day research with persistent knowledge graph and adaptive planning, automating vulnerability analysis and exploit development through HexStrike and local tools via MCP.
README
ZeroForge
An adaptive binary-0day research brain that sits on top of HexStrike AI.
HexStrike is an excellent executor: 150+ security tools wrapped behind an HTTP API and MCP. But it is stateless — every tool returns raw text to the LLM and the target model is forgotten the moment the call returns, and its "decision engine" is a hard-coded effectiveness table that never adapts.
ZeroForge adds the part that was missing: memory and reasoning.
┌── LLM (Claude via MCP) ──┐
│ reasons & decides │
└────────────┬──────────────┘
│ small stateful verbs (zf_ingest / zf_plan / zf_assess …)
┌───────▼─────────┐
│ ZeroForge │ persistent knowledge graph (SQLite)
│ • model │ adaptive planner (state → ranked actions)
│ • planner │ action log (never repeat work)
└───────┬─────────┘
│ executes via …
┌──────────▼───────────┐ ┌───────────────┐
│ HexStrike /api/tools │ or → │ local tools │ (fallback if HS down)
└──────────────────────┘ └───────────────┘
Why it's more powerful than raw HexStrike
| HexStrike | ZeroForge | |
|---|---|---|
| Target model | none — raw tool text per call | persistent knowledge graph that accumulates across runs |
| Tool selection | static effectiveness table | adaptive planner reacting to discovered facts |
| Repeated work | re-runs freely | action log dedup — never repeats a completed step |
| Reasoning | in the LLM only, each turn cold | LLM + a growing state it can query |
| Backend | its own wrappers | reuses HexStrike, with local fallback |
Install
python3 -m pip install requests # only hard dep for the core
# MCP mode additionally needs fastmcp (already in the HexStrike venv)
Use — CLI
python3 -m zeroforge.cli ingest ./target # characterize + enumerate + gadgets
python3 -m zeroforge.cli assess # narrative state
python3 -m zeroforge.cli plan # ranked next actions + rationale
python3 -m zeroforge.cli exploit ./target # autonomous ret2win -> real shell
python3 -m zeroforge.cli show sink # dump graph nodes
python3 -m zeroforge.cli log # what has already run
Autonomous exploitation (proven end-to-end)
Against a no-canary stack overflow with a win()/system("/bin/sh") function,
zeroforge exploit runs the whole chain by itself — no offsets hard-coded:
[*] Attempting autonomous ret2win on ./vuln_test
detail: ret2win -> win@0x401166 offset=72 align_ret=0x401016
[+] EXPLOIT SUCCESSFUL — code execution proven:
| ZEROFORGE_PWNED_1000
| uid=1000(benny) gid=1000(benny) groups=1000(benny),...
[+] Reproducible exploit written to: ./vuln_test.zeroforge_exploit.py
It (1) detects the input vector from the discovered sinks, (2) computes the
saved-RIP offset by crashing the target under GDB with a cyclic pattern,
(3) locates a win/system function, (4) builds a stack-aligned payload and
(5) runs the binary to obtain a shell — then records GOAL ACHIEVED: shell
into the knowledge graph and emits a standalone pwntools script.
tests/test_exploit.py compiles a fresh target and asserts the shell.
Example against a scanf/strcpy/system("/bin/sh") binary with no canary:
[ 98] Trace reachability to system()/popen() (exploit)
↳ system()/popen() present and a /bin/sh string exists — likely a one-shot command-injection path.
[ 93] Dynamically confirm stack overflow (offset) (exploit)
↳ Unbounded copy sink present. No stack canary → saved-return overwrite is viable.
[ 82] Probe format-string primitive (exploit)
↳ Non-constant format sink present → test for arbitrary read/write & leaks.
Use — MCP (drive it from Claude)
Point your MCP client at zeroforge.mcp_server. Verbs: zf_ingest,
zf_plan, zf_assess, zf_show, zf_note, zf_run_hexstrike.
{
"mcpServers": {
"zeroforge": {
"command": "/home/you/hexstrike-ai/hexstrike-env/bin/python",
"args": ["-m", "zeroforge.mcp_server"],
"env": { "ZEROFORGE_DB": "engagement.db", "HEXSTRIKE_URL": "http://localhost:8888" }
}
}
}
Status
v0.2 — working core: knowledge graph, triage capabilities (file/checksec/sinks/
strings/ROP gadgets), adaptive planner, CLI, MCP server (7 verbs incl.
zf_exploit), and autonomous ret2win exploitation proven end-to-end.
Roadmap: ret2libc / full ROP chaining, format-string primitive, taint-based
reachability (angr), fuzzing orchestration + crash triage, agentic run-loop.
Legal
For authorized security research, CTFs and pentesting engagements only. You are responsible for having permission to test any target.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.