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Analyzing Cobalt Strike Beacon Configuration

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mukul975
analyzing-cobalt-strike-beacon-configuration

Extract and analyze Cobalt Strike beacon configuration from PE files and memory dumps to identify C2 infrastructure, malleable profiles, and operator tradecraft.

Overview

Publishermukul975
RepositoryAnthropic-Cybersecurity-Skills
Skill nameanalyzing-cobalt-strike-beacon-configuration
Stars
32.9K
Forks
4K
Bundled files
6
LicenseApache-2.0
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.

  • 6 bundled files

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

  • Open source

    Published by mukul975 on GitHub. Read the source before you install it.

Installation

Install the Analyzing Cobalt Strike Beacon Configuration 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/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/Anthropic-Cybersecurity-Skills
mkdir -p .claude/skills
cp -r /tmp/Anthropic-Cybersecurity-Skills/skills/analyzing-cobalt-strike-beacon-configuration .claude/skills/analyzing-cobalt-strike-beacon-configuration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyzing Cobalt Strike Beacon Configuration 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 Analyzing Cobalt Strike Beacon Configuration 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 Analyzing Cobalt Strike Beacon Configuration 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.

Analyzing Cobalt Strike Beacon Configuration

Overview

Cobalt Strike is a commercial adversary simulation tool widely abused by threat actors for post-exploitation operations. Beacon payloads contain embedded configuration data that reveals C2 server addresses, communication protocols, sleep intervals, jitter values, malleable C2 profile settings, watermark identifiers, and encryption keys. Extracting this configuration from PE files, shellcode, or memory dumps is critical for incident responders to map attacker infrastructure and attribute campaigns. The beacon configuration is XOR-encoded using a single byte (0x69 for version 3, 0x2e for version 4) and stored in a Type-Length-Value (TLV) format within the .data section.

When to Use

  • When investigating security incidents that require analyzing cobalt strike beacon configuration
  • When building detection rules or threat hunting queries for this domain
  • When SOC analysts need structured procedures for this analysis type
  • When validating security monitoring coverage for related attack techniques

Prerequisites

  • Python 3.9+ with dissect.cobaltstrike, pefile, yara-python
  • SentinelOne CobaltStrikeParser (parse_beacon_config.py)
  • Hex editor (010 Editor, HxD) for manual inspection
  • Understanding of PE file format and XOR encoding
  • Memory dump acquisition tools (Volatility3, WinDbg)
  • Network analysis tools (Wireshark) for C2 traffic correlation

Key Concepts

Beacon Configuration Structure

Cobalt Strike beacons store their configuration as a blob of TLV (Type-Length-Value) entries within the .data section of the PE. Stageless beacons XOR the entire beacon code with a 4-byte key. The configuration blob itself uses a single-byte XOR key. Each TLV entry contains a 2-byte type identifier (e.g., 0x0001 for BeaconType, 0x0008 for C2Server), a 2-byte length, and variable-length data.

Malleable C2 Profiles

The beacon configuration encodes the malleable C2 profile that dictates HTTP request/response transformations, including URI paths, headers, metadata encoding (Base64, NetBIOS), and data transforms. Analyzing these settings reveals how the beacon disguises its traffic to blend with legitimate web traffic.

Watermark and License Identification

Each Cobalt Strike license embeds a unique watermark (4-byte integer) into generated beacons. Extracting the watermark can link multiple beacons to the same operator or cracked license. Known watermark databases maintained by threat intelligence providers map watermarks to specific threat actors or leaked license keys.

Workflow

Step 1: Extract Configuration with CobaltStrikeParser

python
#!/usr/bin/env python3
"""Extract Cobalt Strike beacon config from PE or memory dump."""
import sys
import json

# Using SentinelOne's CobaltStrikeParser
# pip install dissect.cobaltstrike
from dissect.cobaltstrike.beacon import BeaconConfig

def extract_beacon_config(filepath):
    """Parse beacon configuration from file."""
    configs = list(BeaconConfig.from_path(filepath))

    if not configs:
        print(f"[-] No beacon configuration found in {filepath}")
        return None

    for i, config in enumerate(configs):
        print(f"\n[+] Beacon Configuration #{i+1}")
        print(f"{'='*60}")

        settings = config.as_dict()

        # Critical fields for incident response
        critical_fields = [
            "SETTING_C2_REQUEST",
            "SETTING_C2_RECOVER",
            "SETTING_PUBKEY",
            "SETTING_DOMAINS",
            "SETTING_BEACONTYPE",
            "SETTING_PORT",
            "SETTING_SLEEPTIME",
            "SETTING_JITTER",
            "SETTING_MAXGET",
            "SETTING_SPAWNTO_X86",
            "SETTING_SPAWNTO_X64",
            "SETTING_PIPENAME",
            "SETTING_WATERMARK",
            "SETTING_C2_VERB_GET",
            "SETTING_C2_VERB_POST",
            "SETTING_USERAGENT",
            "SETTING_PROTOCOL",
        ]

        for field in critical_fields:
            value = settings.get(field, "N/A")
            print(f"  {field}: {value}")

        return settings

    return None


def extract_c2_indicators(config):
    """Extract actionable C2 indicators from beacon config."""
    indicators = {
        "c2_domains": [],
        "c2_ips": [],
        "c2_urls": [],
        "user_agent": "",
        "named_pipes": [],
        "spawn_processes": [],
        "watermark": "",
    }

    if not config:
        return indicators

    # Extract C2 domains
    domains = config.get("SETTING_DOMAINS", "")
    if domains:
        for domain in str(domains).split(","):
            domain = domain.strip().rstrip("/")
            if domain:
                indicators["c2_domains"].append(domain)

    # Extract user agent
    indicators["user_agent"] = str(config.get("SETTING_USERAGENT", ""))

    # Extract named pipes
    pipe = config.get("SETTING_PIPENAME", "")
    if pipe:
        indicators["named_pipes"].append(str(pipe))

    # Extract spawn-to processes
    for arch in ["SETTING_SPAWNTO_X86", "SETTING_SPAWNTO_X64"]:
        proc = config.get(arch, "")
        if proc:
            indicators["spawn_processes"].append(str(proc))

    # Extract watermark
    indicators["watermark"] = str(config.get("SETTING_WATERMARK", ""))

    return indicators


if __name__ == "__main__":
    if len(sys.argv) < 2:
        print(f"Usage: {sys.argv[0]} <beacon_file_or_dump>")
        sys.exit(1)

    config = extract_beacon_config(sys.argv[1])
    if config:
        indicators = extract_c2_indicators(config)
        print(f"\n[+] Extracted C2 Indicators:")
        print(json.dumps(indicators, indent=2))

Step 2: Manual XOR Decryption of Beacon Config

python
import struct

def find_and_decrypt_config(data):
    """Manually locate and decrypt beacon configuration."""
    # Cobalt Strike 4.x uses 0x2e as XOR key
    xor_keys = [0x2e, 0x69]  # v4, v3

    for xor_key in xor_keys:
        # Search for the config magic bytes after XOR
        # Config starts with 0x0001 (BeaconType) XOR'd with key
        magic = bytes([0x00 ^ xor_key, 0x01 ^ xor_key,
                       0x00 ^ xor_key, 0x02 ^ xor_key])

        offset = data.find(magic)
        if offset == -1:
            continue

        print(f"[+] Found config at offset 0x{offset:x} (XOR key: 0x{xor_key:02x})")

        # Decrypt the config blob (typically 4096 bytes)
        config_size = 4096
        encrypted = data[offset:offset + config_size]
        decrypted = bytes([b ^ xor_key for b in encrypted])

        # Parse TLV entries
        entries = parse_tlv(decrypted)
        return entries

    return None


def parse_tlv(data):
    """Parse Type-Length-Value configuration entries."""
    entries = {}
    offset = 0

    # TLV field type mapping
    field_names = {
        0x0001: "BeaconType",
        0x0002: "Port",
        0x0003: "SleepTime",
        0x0004: "MaxGetSize",
        0x0005: "Jitter",
        0x0006: "MaxDNS",
        0x0007: "Deprecated_PublicKey",
        0x0008: "C2Server",
        0x0009: "UserAgent",
        0x000a: "PostURI",
        0x000b: "Malleable_C2_Instructions",
        0x000c: "Deprecated_HttpGet_Metadata",
        0x000d: "SpawnTo_x86",
        0x000e: "SpawnTo_x64",
        0x000f: "CryptoScheme",
        0x001a: "Watermark",
        0x001d: "C2_HostHeader",
        0x0024: "PipeName",
        0x0025: "Year",
        0x0026: "Month",
        0x0027: "Day",
        0x0036: "ProxyHostname",
    }

    while offset + 6 <= len(data):
        entry_type = struct.unpack(">H", data[offset:offset+2])[0]
        entry_len_type = struct.unpack(">H", data[offset+2:offset+4])[0]
        entry_len = struct.unpack(">H", data[offset+4:offset+6])[0]

        if entry_type == 0:
            break

        value_start = offset + 6
        value_end = value_start + entry_len
        value_data = data[value_start:value_end]

        field_name = field_names.get(entry_type, f"Unknown_0x{entry_type:04x}")

        if entry_len_type == 1:  # Short
            value = struct.unpack(">H", value_data[:2])[0]
        elif entry_len_type == 2:  # Int
            value = struct.unpack(">I", value_data[:4])[0]
        elif entry_len_type == 3:  # String/Blob
            value = value_data.rstrip(b'\x00').decode('utf-8', errors='replace')
        else:
            value = value_data.hex()

        entries[field_name] = value
        print(f"  {field_name}: {value}")

        offset = value_end

    return entries

Step 3: YARA Rule for Beacon Detection

python
import yara

cobalt_strike_rule = """
rule CobaltStrike_Beacon_Config {
    meta:
        description = "Detects Cobalt Strike beacon configuration"
        author = "Malware Analysis Team"
        date = "2025-01-01"

    strings:
        // XOR'd config marker for CS 4.x (key 0x2e)
        $config_v4 = { 2e 2f 2e 2c }

        // XOR'd config marker for CS 3.x (key 0x69)
        $config_v3 = { 69 68 69 6b }

        // Common beacon strings
        $str_pipe = "\\\\.\\pipe\\" ascii wide
        $str_beacon = "beacon" ascii nocase
        $str_sleeptime = "sleeptime" ascii nocase

        // Reflective loader pattern
        $reflective = { 4D 5A 41 52 55 48 89 E5 }

    condition:
        ($config_v4 or $config_v3) or
        (2 of ($str_*) and $reflective)
}
"""

def scan_for_beacons(filepath):
    """Scan file with YARA rules for Cobalt Strike beacons."""
    rules = yara.compile(source=cobalt_strike_rule)
    matches = rules.match(filepath)

    for match in matches:
        print(f"[+] YARA Match: {match.rule}")
        for string_match in match.strings:
            offset = string_match.instances[0].offset
            print(f"    String: {string_match.identifier} at offset 0x{offset:x}")

    return matches

Step 4: Network Traffic Correlation

python
from dissect.cobaltstrike.c2 import HttpC2Config

def analyze_c2_profile(beacon_config):
    """Analyze malleable C2 profile from beacon configuration."""
    print("\n[+] Malleable C2 Profile Analysis")
    print("=" * 60)

    # HTTP GET configuration
    get_verb = beacon_config.get("SETTING_C2_VERB_GET", "GET")
    get_uri = beacon_config.get("SETTING_C2_REQUEST", "")
    print(f"\n  HTTP GET Request:")
    print(f"    Verb: {get_verb}")
    print(f"    URI: {get_uri}")

    # HTTP POST configuration
    post_verb = beacon_config.get("SETTING_C2_VERB_POST", "POST")
    post_uri = beacon_config.get("SETTING_C2_POSTREQ", "")
    print(f"\n  HTTP POST Request:")
    print(f"    Verb: {post_verb}")
    print(f"    URI: {post_uri}")

    # User Agent
    ua = beacon_config.get("SETTING_USERAGENT", "")
    print(f"\n  User-Agent: {ua}")

    # Host header
    host = beacon_config.get("SETTING_C2_HOSTHEADER", "")
    print(f"  Host Header: {host}")

    # Sleep and jitter for traffic pattern
    sleep_ms = beacon_config.get("SETTING_SLEEPTIME", 60000)
    jitter = beacon_config.get("SETTING_JITTER", 0)
    print(f"\n  Sleep Time: {sleep_ms}ms")
    print(f"  Jitter: {jitter}%")

    # Generate Suricata/Snort signatures
    print(f"\n[+] Suggested Network Signatures:")
    if ua:
        print(f'  alert http any any -> any any (msg:"CS Beacon UA"; '
              f'content:"{ua}"; http_user_agent; sid:1000001; rev:1;)')
    if get_uri:
        print(f'  alert http any any -> any any (msg:"CS Beacon URI"; '
              f'content:"{get_uri}"; http_uri; sid:1000002; rev:1;)')

Validation Criteria

  • Beacon configuration successfully extracted from PE file or memory dump
  • C2 server domains/IPs correctly identified with port and protocol
  • Malleable C2 profile parameters decoded showing HTTP transforms
  • Watermark value extracted for attribution correlation
  • Sleep time and jitter values match observed network beacon intervals
  • YARA rules detect beacon in both packed and unpacked samples
  • Network signatures generated from extracted C2 profile

References

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 Analyzing Cobalt Strike Beacon Configuration AI skill do?

Extract and analyze Cobalt Strike beacon configuration from PE files and memory dumps to identify C2 infrastructure, malleable profiles, and operator tradecraft.

Why use Analyzing Cobalt Strike Beacon Configuration on TypingMind?

Because you install it once and use it with any model. Analyzing Cobalt Strike Beacon Configuration 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 Analyzing Cobalt Strike Beacon Configuration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-cobalt-strike-beacon-configuration. 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 Analyzing Cobalt Strike Beacon Configuration?

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 Analyzing Cobalt Strike Beacon Configuration?

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

Is the Analyzing Cobalt Strike Beacon Configuration AI skill free?

Yes. It is published on GitHub by mukul975 under the Apache-2.0 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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