Security-first MCP server for reverse engineering, malware analysis, forensics, and SAST.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
AI-Powered Reverse Engineering & Security Analysis via Model Context Protocol
An MCP server that gives AI assistants like Claude and Cursor the ability to perform reverse engineering, malware analysis, vulnerability research, digital forensics, and source code auditing through natural language.
Reversecore MCP is a Model Context Protocol server that wraps 120 analysis tools into a single interface that AI assistants can call through natural language.
Instead of learning the command-line syntax for a dozen different tools, you describe what you want:
The AI assistant breaks this into tool calls:
Each tool returns a structured ToolResult (either ToolSuccess or ToolError) with typed data that the AI can reason about, chain into follow-up queries, or render for the user.
| Domain | What you can do |
|---|---|
| Static analysis | Disassembly, decompilation (r2ghidra), binary parsing (LIEF), packer detection (DIE), capability detection (CAPA), string extraction, firmware scanning (binwalk) |
| Dynamic & symbolic | ESIL emulation, angr symbolic execution, taint analysis, fuzzing harness generation |
| Malware analysis | IOC extraction, YARA scanning, dormant backdoor detection, adaptive vaccine generation, autonomous vulnerability hunting |
| Vulnerability research | Dangerous API detection, ROP gadget discovery, heap exploit analysis, crash triage, PoC generation |
| Digital forensics | Memory forensics (Volatility3), PCAP analysis (Scapy), disk forensics (Sleuth Kit), artifact correlation |
| Source code audit | Python AST scanning, C/C++ regex pattern scanning |
| Reporting | Session-based reports with MITRE ATT&CK mapping, SIGMA rule generation, VEX reports, email delivery |
The reversecore_mcp/core/ directory contains the shared infrastructure that all tools build on:
| Module | Purpose |
|---|---|
config.py | Pydantic BaseSettings with 34+ environment variables |
security.py | Input sanitization, command argument validation |
validators.py | File and binary path validation with TOCTOU mitigation, symlink resolution |
r2_pool.py | Thread-safe Radare2 connection pool with configurable size |
r2_helpers.py | Structured Radare2 output parsing |
metrics.py | Per-tool execution times, call counts, error rates, cache statistics |
memory.py | Async SQLite-backed AI memory store for persisting analysis findings across sessions |
mitre_mapper.py | MITRE ATT&CK technique ID mapping engine |
evidence.py | Evidence classification system: OBSERVED, INFERRED, POSSIBLE |
resilience.py | Retry, circuit-breaker, and timeout decorator patterns |
task_queue.py | Background task queue via Redis + arq |
extension_registry.py | Plugin registration and lifecycle management |
arch_registry.py | Multi-architecture mapping (x86, x86_64, ARM32, ARM64, MIPS, RISC-V, PPC β r2 arch/bits/registers) |
result_cache.py | SHA256-based tool result caching decorator (@cache_tool_result) |
analysis_cache.py | Multi-level decompilation cache (L1: Redis, L2: SQLite) |
result.py | ToolSuccess / ToolError Pydantic models |
exceptions.py | 17 exception classes with RCMCP-E* error codes |
decorators.py | @log_execution, @track_metrics |
error_handling.py | @handle_tool_errors decorator |
error_formatting.py | Structured error response formatting |
execution.py | Safe subprocess execution with timeout and output limits |
command_spec.py | Command specification for subprocess calls |
loader.py | Dynamic tool module loader |
plugin.py | Plugin base class |
extension.py | Extension base class |
container.py | Container/sandbox execution support |
audit.py | Audit logging |
binary_cache.py | Binary file caching |
json_utils.py | JSON serialization via orjson (3-5x faster than stdlib json) |
logging_config.py | Loguru-based structured logging |
report_generator.py | Report rendering engine (Markdown, PDF via xhtml2pdf) |
resource_manager.py | MCP resource lifecycle management |
sast/python_ast_scanner.py | Python AST-based vulnerability scanner |
sast/regex_scanner.py | C/C++ regex-based vulnerability scanner |
sast/rule_manager.py | SAST rule loading and management |
Every tool returns a structured ToolResult β either a ToolSuccess with typed data or a ToolError with an RCMCP-E* error code. Tools are organized into 8 plugins.
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