# Analyzing Cobalt Strike Malleable Profiles

> >

- **Type:** Skill
- **Install:** `agentstack add skill-autohandai-community-skills-analyzing-cobalt-strike-malleable-profiles`
- **Verified:** Pending review
- **Seller:** [autohandai](https://agentstack.voostack.com/s/autohandai)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [autohandai](https://github.com/autohandai)
- **Source:** https://github.com/autohandai/community-skills/tree/main/analyzing-cobalt-strike-malleable-profiles
- **Website:** https://skilled.autohand.ai

## Install

```sh
agentstack add skill-autohandai-community-skills-analyzing-cobalt-strike-malleable-profiles
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Analyzing Cobalt Strike Malleable Profiles

## Instructions

Parse malleable C2 profiles to extract IOCs and detection opportunities using the
pyMalleableC2 library. Combine with JARM fingerprinting to identify C2 servers.

```python
from malleablec2 import Profile

# Parse a malleable profile from file
profile = Profile.from_file("amazon.profile")

# Extract global options (sleep, jitter, user-agent)
print(profile.ast.pretty())

# Access HTTP-GET block URIs and headers for network signatures
# Access HTTP-POST block for data exfiltration patterns
# Generate JARM fingerprints for known C2 infrastructure
```

Key analysis steps:
1. Parse the malleable profile to extract HTTP-GET/POST URI patterns
2. Extract User-Agent strings and custom headers for IDS signatures
3. Identify sleep time and jitter for beaconing detection thresholds
4. Scan suspect IPs with JARM to match known C2 fingerprint hashes
5. Cross-reference extracted IOCs with network traffic logs

## Examples

```python
# Parse profile and extract detection indicators
from malleablec2 import Profile
p = Profile.from_file("cobaltstrike.profile")
print(p)  # Reconstructed source

# JARM scan a suspect C2 server
import subprocess
result = subprocess.run(
    ["python3", "jarm.py", "suspect-server.com"],
    capture_output=True, text=True
)
print(result.stdout)
# Compare fingerprint against known CS JARM hashes
```

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [autohandai](https://github.com/autohandai)
- **Source:** [autohandai/community-skills](https://github.com/autohandai/community-skills)
- **License:** Apache-2.0
- **Homepage:** https://skilled.autohand.ai

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** yes
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: flagged — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-autohandai-community-skills-analyzing-cobalt-strike-malleable-profiles
- Seller: https://agentstack.voostack.com/s/autohandai
- Browse the marketplace: https://agentstack.voostack.com/browse

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