analyzing-cobaltstrike-malleable-c2-profiles
Parse and analyze Cobalt Strike Malleable C2 profiles using dissect.cobaltstrike and pyMalleableC2 to extract C2 indicators, detect evasion techniques, and generate network detection signatures.
Best use case
analyzing-cobaltstrike-malleable-c2-profiles is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Parse and analyze Cobalt Strike Malleable C2 profiles using dissect.cobaltstrike and pyMalleableC2 to extract C2 indicators, detect evasion techniques, and generate network detection signatures.
Teams using analyzing-cobaltstrike-malleable-c2-profiles should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/analyzing-cobaltstrike-malleable-c2-profiles/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How analyzing-cobaltstrike-malleable-c2-profiles Compares
| Feature / Agent | analyzing-cobaltstrike-malleable-c2-profiles | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Parse and analyze Cobalt Strike Malleable C2 profiles using dissect.cobaltstrike and pyMalleableC2 to extract C2 indicators, detect evasion techniques, and generate network detection signatures.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Analyzing CobaltStrike Malleable C2 Profiles
## 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.