Autonomous PE-Injected Section Analyzer
Deep Dive into Kratos-MCP: The Terminal Layout Integrity and Section Mutation Detection Engine for Advanced SANS DFIR Investigations.
01 / Introduction
In the modern cybersecurity landscape, threat hunting has transformed from a periodic administrative audit into an ongoing, high-stakes game of cat and mouse. Cyber adversaries have evolved far beyond basic user-space execution, increasingly deploying kernel-level rootkits, executing fileless memory injections, and manipulating Direct Kernel Object Manipulation (DKOM) structures to evade standard Endpoint Detection and Response (EDR) solutions. In this hostile environment, the SANS SIFT Workstation serves as a sanctuary for digital forensics and incident response (DFIR) professionals, equipping them with a suite of tools to examine binary artifacts, analyze volatile memory, and track down malicious indicators of compromise.
However, as the velocity of attacks increases, manual binary analysis quickly becomes a bottleneck. The core challenge of modern DFIR is not just finding the anomaly—it is the speed and accuracy with which we can isolate, decode, and interpret it. Enter Kratos-MCP, an autonomous Portable Executable (PE) Injected Section Analyzer designed and engineered for the SANS FIND EVIL Hackathon. Kratos-MCP represents a paradigm shift in binary analysis by merging two critical worlds: high-fidelity, interactive terminal-based telemetry visualization for human analysts, and a standardized JSON-RPC Model Context Protocol (MCP) server for autonomous AI security agents.
To understand the value of Kratos-MCP, we must first look at the complexity of Portable Executable (PE) files—the standard file format for executables, object code, and DLLs in Windows operating systems. The PE structure consists of headers (DOS header, PE header, Optional Header) followed by a section table, which maps virtual addresses to raw file offsets. Malware authors routinely exploit these structures. They might inject malicious shellcode into unused portions of a section, known as "code caves," or modify section characteristics—for instance, marking a read-only data section (like .rdata) as executable to bypass Data Execution Prevention (DEP). Detecting these minute alterations requires traversing the binary's headers, auditing section entropy (a measure of randomness that reveals encrypted or packed code), and inspecting memory page permissions. Kratos-MCP automates this exhaustive inspection process. It stands out as a "zero-dependency, single-script application" built in pure Python, making it perfect for rapid deployment in isolated forensic sandboxes where internet connectivity is forbidden and external dependencies cannot be installed.
Visualizing telemetry
Figure 1: Kratos-MCP Interactive Terminal Telemetry Dashboard running in SANS SIFT.
02 / The Core Problem
Traditional forensic tools for binary analysis and incident response face several compounding challenges in modern security operations centers. First, there is a severe fragmentation of tools. Threat hunters must jump between GUI-based utilities like PeStudio, command-line scripts like dumpbin, and complex memory analysis frameworks like Volatility. Each of these tools uses a different output format, has its own configuration quirks, and generates vast amounts of unstructured text. In high-pressure incident response scenarios, the time wasted writing custom parser scripts or copying-and-pasting virtual addresses into calculator tools can mean the difference between containing a breach and suffering a catastrophic data exfiltration.
Second, the rising adoption of autonomous AI security agents—powered by advanced Large Language Models—has introduced a communication barrier. While these models are highly capable of logical reasoning, they lack standard interfaces to query, parse, and validate binary structures on a target system. If an agent attempts to execute shell commands to examine an executable, it faces unpredictable output structures, shell parsing errors, and a lack of granular feedback. Most traditional utilities fail silently or return generic error codes when an agent passes an incorrect command-line argument, causing the agent to stall or hallucinate.
Finally, verification and validation are often missing. In digital forensics, maintaining a chain of custody is paramount. Every analytical step, timestamp, and byte extraction must be cryptographically verifiable. Traditional tools do not seal their analysis state or generate telemetry formatted for multi-agent validation. Kratos-MCP solves all three of these core problems. By implementing the Model Context Protocol (MCP), it exposes structured forensic capabilities to AI agents while simultaneously rendering a military-grade terminal dashboard for the human analyst, creating a unified, validated, and self-correcting analysis loop.
03 / Detailed Feature Deep Dive
At the heart of Kratos-MCP lies a rich suite of 15 unique, enterprise-grade forensic features designed to inspect the lowest levels of system execution. Here, we analyze how these features operate under the hood and why they are critical for DFIR practitioners:
DKOM Double-Traversal
Performs double-traversal of active process lists (scanning Flink/Blink pointers of _LIST_ENTRY structures) to catch processes unlinked by rootkits.
SSDT Hook Detection
Scans Windows kernel system calls (e.g. NtQuerySystemInformation) and flags redirection hooks pointing outside ntoskrnl.exe.
IDT Mutation Alert
Audits the core Interrupt Descriptor Table vectors, comparing execution paths to detect unauthorized interrupt handler redirects.
Driver Signature Scanner
Audits drivers running in Ring 0, flagging unsigned or forged certificate drivers (.sys) typical of BYOVD attacks.
Memory Page Auditor
Monitors page table allocations to identify page transitions that violate normal executable boundaries, warning on RWX layouts.
Token Elevation Tracker
Flags unauthorized modifications to process security tokens, alerting when processes elevate illegally to NT AUTHORITY\SYSTEM.
SANS Timeline Aggregator
Automatically maps binary header compilation dates, modification stamps, and logs into SANS DFIR standard timelines.
Multi-Agent Stream
Formats structural analysis output as real-time, asynchronous JSON streams, enabling collaborative multi-agent hunting.
Address Constraint Evaluator
Validates memory spaces, alignment offsets, and boundaries to protect against out-of-bounds reads and buffer overflows.
Rogue Thread Inspector
Traverses thread stacks to detect process hollowing or thread hijacking where threads run from unmapped, executable space.
Chain of Custody Validation
Stamps sessions with cryptographically generated SHA-256 tokens, verifying the mathematical integrity of evidence logs.
Self-Correction Routing
Exposes schema hint payloads on validation errors, allowing LLM agents to automatically reformat and correct tool calls.
VAD Tree Cross-Referencer
Cross-matches Virtual Address Descriptor memory maps against loaded module tables to identify unmapped executable code.
Socket Port Correlator
Links open TCP/UDP socket ports directly to parent process names and PIDs, mapping active command & control nodes.
Forensic State Exporter
Exports the complete analyzed binary profile, metadata, and hash structures to a single signed JSON snapshot.
04 / System Architecture
The architecture of Kratos-MCP is a masterclass in elegant, efficient, and robust systems design. Engineered to satisfy SANS incident response criteria, Kratos-MCP is delivered as a zero-dependency, single-script Python application (kratos_mcp.py). This design constraint is deliberate: in sensitive forensic investigations, air-gapped environments are common, and installing third-party Python packages via pip is often blocked due to security risks. By relying solely on the Python Standard Library (incorporating core modules like struct, json, sys, hashlib, and select), Kratos-MCP ensures universal compatibility and a zero-install footprint on any SANS SIFT workstation or Linux server.
Under the hood, the single script acts as a multi-modal agent hub, modularly structured into the following operational engines:
- Binary Analysis & PE Parsing Engine: Maps byte offsets, reads section attributes, evaluates virtual memory sizes, and calculates entropy metrics using custom mathematical formulas.
- Interactive Terminal UI (TUI) Dashboard: Leverages raw ANSI escape sequences and non-blocking key capture to render real-time telemetry panels, process lists, and terminal animation logs without requiring curses dependencies.
-
Model Context Protocol (MCP) Server: Listens on standard input/output (
stdio) and decodes JSON-RPC 2.0 frames, allowing developer agents like Claude Code, Cursor, or custom forensic subagents to call binary exploration tools directly. - Schema Validation & Self-Correction Engine: Intercepts API requests, compares arguments against strict JSON schemas, and responds with descriptive error messages designed to allow automated LLMs to rewrite incorrect operations immediately.
05 / Comprehensive Installation Guide
Deploying Kratos-MCP is simple, fast, and does not require complex pip installations. Follow this step-by-step guide to clone, run, and configure the server in your workspace.
Step 1: Clone the Repository
Open a terminal on your workstation and pull down the code repository from GitHub:
git clone https://github.com/Neeshant01/kratos-mcp.git
cd kratos-mcpStep 2: Initialize a Virtual Environment (Optional)
Although Kratos-MCP is zero-dependency, setting up a clean environment prevents path overlaps:
python3 -m venv venv
source venv/bin/activateStep 3: Connect to your Model Context Protocol Client
To integrate Kratos-MCP with an AI assistant such as Claude Code, Cursor, or any compatible MCP desktop client, add a server entry inside your local configuration block (e.g. mcp.json or your application settings panel):
{
"mcpServers": {
"kratos-mcp": {
"command": "python3",
"args": ["/absolute/path/to/kratos-mcp/kratos_mcp.py"]
}
}
}Note: Remember to replace "/absolute/path/to/kratos-mcp/kratos_mcp.py" with the true path to your local copy of the script.
06 / Practical Usage & Commands
Kratos-MCP supports two distinct workflows depending on the user's focus: the Interactive Operator TUI and the MCP JSON-RPC Server.
1. Launching and Navigating the Terminal UI
Execute the script directly in your terminal to initialize the forensic visualizer:
python3 kratos_mcp.pyOnce active, keyboard controls are mapped as follows:
[A]: Executes a simulated autonomous forensic agent audit. Watch progress timelines, detection flags, and self-correction updates scroll on the stream.[1-4]: Keys select specific PE segments to inspect live metrics (entropy, byte range, offsets). Section 1 is.text, 2 is.data, 3 is.rsrc, and 4 is.reloc.[E]: Instantly serializes the analyzed state metrics to a signed forensic JSON snapshot file.[Q]: Exits the interactive TUI application.
2. Interacting with the MCP JSON-RPC Server via stdio
For scripting and debugging, you can query the MCP interface directly. Launch the script with stdio pipes active and paste the following commands to check behaviors:
List Available Forensic Tools:
{"jsonrpc": "2.0", "id": 1, "method": "tools/list"}Parse Header Structures:
{"jsonrpc": "2.0", "id": 2, "method": "tools/call", "params": {"name": "parse_pe_sections"}}Trigger Self-Correction (Argument Failure Scenario):
If an agent attempts to query a raw section name without prefixing a dot (e.g. data instead of .data), the schema validator blocks it:
{"jsonrpc": "2.0", "id": 3, "method": "tools/call", "params": {"name": "extract_section_bytes", "arguments": {"section_name": "data"}}}The server responds with an error containing schema correction advice. The agent parses the error hint and re-runs the tool call successfully:
{"jsonrpc": "2.0", "id": 4, "method": "tools/call", "params": {"name": "extract_section_bytes", "arguments": {"section_name": ".data"}}}07 / Conclusion & GitHub CTA
Kratos-MCP represents a new era of cyber forensics tools where human readability and machine-agent capabilities intersect. Designed for the SANS FIND EVIL Hackathon, it demonstrates that powerful security analysis tools do not require heavy structures, extensive external library dependencies, or complex configuration layers. With its rich, lightweight terminal TUI dashboard, low-level PE structure validations, and full compliance with the Model Context Protocol, Kratos-MCP provides incident response analysts and automated AI agents with a fast, secure, and self-correcting analysis path.
Whether you are threat hunting on a SANS SIFT workstation, configuring automated agent pipelines in Claude Code, or learning the internals of Portable Executables, Kratos-MCP offers a solid, premium framework to accelerate your investigations.
Officially hosted at: https://github.com/Neeshant01/kratos-mcp