Install
$ agentstack add skill-autohandai-community-skills-analyzing-cobalt-strike-malleable-profiles Open-source listing, not yet scanned by AgentStack. Follow the source repository for install instructions.
Security review
⚠ Flagged1 finding(s); flagged for manual review. · v0.1.0 How review works →
- • Prompt-injection patterns
- • Secret / credential exfiltration
- • Dangerous shell & filesystem operations
- • Untrusted network calls
- • Known-malicious package signatures
- high Dangerous shell/eval execution.
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ● Shell / process execution Used
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →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.
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:
- Parse the malleable profile to extract HTTP-GET/POST URI patterns
- Extract User-Agent strings and custom headers for IDS signatures
- Identify sleep time and jitter for beaconing detection thresholds
- Scan suspect IPs with JARM to match known C2 fingerprint hashes
- Cross-reference extracted IOCs with network traffic logs
Examples
# 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
- Source: autohandai/community-skills
- License: Apache-2.0
- Homepage: https://skilled.autohand.ai
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.