Analyzing CobaltStrike Malleable C2 Profiles

SkillSecurity

Lets your agent parse Cobalt Strike C2 profiles to extract traffic patterns and generate detection signatures.

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About this capability

Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP/DNS transforms, URIs, headers, sleep/jitter, and injection behavior, then generate network detection signatures. Use when reverse-engi

What this skill tells your AI

The instructions your AI receives, as published by mukul975/anthropic-cybersecurity-skills in skills/analyzing-cobaltstrike-malleable-c2-profiles/SKILL.md and read by ahel’s review.

Overview

Cobalt Strike Malleable C2 profiles are domain-specific language scripts that customize how Beacon communicates with the team server, defining HTTP request/response transformations, sleep intervals, jitter values, user agents, URI paths, and process injection behavior. Threat actors use malleable profiles to disguise C2 traffic as legitimate services (Amazon, Google, Slack). Analyzing these profiles reveals network indicators for detection: URI patterns, HTTP headers, POST/GET transforms, DNS settings, and process injection techniques. The dissect.cobaltstrike library can parse both profile files and extract configurations from beacon payloads, while pyMalleableC2 provides AST-based parsing using Lark grammar for programmatic profile manipulation and validation.

When to Use

  • When investigating security incidents that require analyzing cobaltstrike malleable c2 profiles
  • 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 and/or pyMalleableC2
  • Sample Malleable C2 profiles (available from public repositories)
  • Understanding of HTTP protocol and Cobalt Strike beacon communication model
  • Network monitoring tools (Suricata/Snort) for signature deployment
  • PCAP analysis tools for traffic validation

Steps

  1. Install libraries: pip install dissect.cobaltstrike or pip install pyMalleableC2
  2. Parse profile with C2Profile.from_path("profile.profile")
  3. Extract HTTP GET/POST block configurations (URIs, headers, parameters)
  4. Identify user agent strings and spoof targets
  5. Extract sleep time, jitter percentage, and DNS beacon settings
  6. Analyze process injection settings (spawn-to, allocation technique)
  7. Generate Suricata/Snort signatures from extracted network indicators
  8. Compare profile against known threat actor profile collections
  9. Extract staging URIs and payload delivery mechanisms
  10. Produce detection report with IOCs and recommended network signatures

Expected Output

A JSON report containing extracted C2 URIs, HTTP headers, user agents, sleep/jitter settings, process injection config, spawned process paths, DNS settings, and generated Suricata-compatible detection rules.

Signals

GitHub stars
33k
Forks
4k
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
analyzing-cobaltstrike-malleable-c2-profiles-mukul975
Source
github.com/mukul975/anthropic-cybersecurity-skills